no code implementations • 24 Apr 2024 • Xin Jiang, Hao Tang, Rui Yan, Jinhui Tang, Zechao Li
This paper presents a meticulous analysis leading to the proposal of practical guidelines to identify subcategory-specific discrepancies and generate discriminative features to design effective FGIR models.
no code implementations • 14 Apr 2024 • Jianyuan Ni, Hao Tang, Syed Tousiful Haque, Yan Yan, Anne H. H. Ngu
We begin by presenting the recent sensor modalities as well as deep learning approaches in HAR.
2 code implementations • 9 Apr 2024 • Ming Tao, Bing-Kun Bao, Hao Tang, YaoWei Wang, Changsheng Xu
3) The story visualization and continuation models are trained and inferred independently, which is not user-friendly.
2 code implementations • 4 Apr 2024 • Wencan Cheng, Hao Tang, Luc van Gool, Jong Hwan Ko
Extracting keypoint locations from input hand frames, known as 3D hand pose estimation, is a critical task in various human-computer interaction applications.
no code implementations • 2 Apr 2024 • Haoyu Chen, Hao Tang, Ehsan Adeli, Guoying Zhao
This work is driven by the intuition that the robustness of the model can be enhanced by introducing adversarial samples into the training, leading to a more invulnerable model to the noisy inputs, which even can be further extended to directly handling the real-world data like raw point clouds/scans without intermediate processing.
no code implementations • 1 Apr 2024 • Gengyu Zhang, Hao Tang, Yan Yan
To address these deficiencies, we propose a versatile diffusion-based approach for both 2D and 3D route planning under partial observability.
no code implementations • 1 Apr 2024 • Junyi Wu, Weitai Kang, Hao Tang, Yuan Hong, Yan Yan
In contrast, our proposed SaCo offers a reliable faithfulness measurement, establishing a robust metric for interpretations.
no code implementations • 26 Mar 2024 • YingJie Xu, Bangzhen Liu, Hao Tang, Bailin Deng, Shengfeng He
We propose a voxel-based optimization framework, ReVoRF, for few-shot radiance fields that strategically address the unreliability in pseudo novel view synthesis.
no code implementations • 26 Mar 2024 • Hao Tang, Lianglun Cheng, Guoheng Huang, Zhengguang Tan, Junhao Lu, Kaihong Wu
Image segmentation holds a vital position in the realms of diagnosis and treatment within the medical domain.
no code implementations • 24 Mar 2024 • Guillaume Thiry, Hao Tang, Radu Timofte, Luc van Gool
Video inpainting tasks have seen significant improvements in recent years with the rise of deep neural networks and, in particular, vision transformers.
no code implementations • 21 Mar 2024 • Junyi Wu, Bin Duan, Weitai Kang, Hao Tang, Yan Yan
To incorporate the influence of token transformation into interpretation, we propose TokenTM, a novel post-hoc explanation method that utilizes our introduced measurement of token transformation effects.
no code implementations • 21 Mar 2024 • Bin Xie, Hao Tang, Bin Duan, Dawen Cai, Yan Yan
Each pair of auxiliary mask and box prompts, which can solve the requirements of extra prompts, is associated with class label predictions by the sum of the auxiliary classifier token and the learnable global classifier tokens in the mask decoder of SAM to solve the predictions of semantic labels.
no code implementations • 16 Mar 2024 • Rui Wang, Hailong Guo, Jiaming Liu, Huaxia Li, Haibo Zhao, Xu Tang, Yao Hu, Hao Tang, Peipei Li
In this paper, we introduce StableGarment, a unified framework to tackle garment-centric(GC) generation tasks, including GC text-to-image, controllable GC text-to-image, stylized GC text-to-image, and robust virtual try-on.
no code implementations • 16 Mar 2024 • Jun Liu, Chao Wu, Changdi Yang, Hao Tang, Haoye Dong, Zhenglun Kong, Geng Yuan, Wei Niu, Dong Huang, Yanzhi Wang
Large language models (LLMs) have become crucial for many generative downstream tasks, leading to an inevitable trend and significant challenge to deploy them efficiently on resource-constrained devices.
no code implementations • 14 Mar 2024 • Huan-ang Gao, Mingju Gao, Jiaju Li, Wenyi Li, Rong Zhi, Hao Tang, Hao Zhao
Semantic image synthesis (SIS) shows good promises for sensor simulation.
2 code implementations • 14 Mar 2024 • Haiyang Wang, Hao Tang, Li Jiang, Shaoshuai Shi, Muhammad Ferjad Naeem, Hongsheng Li, Bernt Schiele, LiWei Wang
Due to its simple design, this paradigm holds promise for narrowing the architectural gap between vision and language.
1 code implementation • 12 Mar 2024 • Zeyu Zhang, Akide Liu, Ian Reid, Richard Hartley, Bohan Zhuang, Hao Tang
Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging.
no code implementations • 8 Mar 2024 • Zichong Meng, Changdi Yang, Jun Liu, Hao Tang, Pu Zhao, Yanzhi Wang
In response to this challenge, our study introduces a novel image editing framework with enhanced generalization robustness by boosting in-context learning capability and unifying language instruction.
1 code implementation • 1 Mar 2024 • Tom Hosking, Hao Tang, Mirella Lapata
We show that HIRO learns an encoding space that is more semantically structured than prior work, and generates summaries that are more representative of the opinions in the input reviews.
no code implementations • 19 Feb 2024 • Hao Tang, Darren Key, Kevin Ellis
We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment.
no code implementations • 14 Feb 2024 • Jiancheng Yang, Rui Shi, Liang Jin, Xiaoyang Huang, Kaiming Kuang, Donglai Wei, Shixuan Gu, Jianying Liu, PengFei Liu, Zhizhong Chai, Yongjie Xiao, Hao Chen, Liming Xu, Bang Du, Xiangyi Yan, Hao Tang, Adam Alessio, Gregory Holste, Jiapeng Zhang, Xiaoming Wang, Jianye He, Lixuan Che, Hanspeter Pfister, Ming Li, Bingbing Ni
The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis.
no code implementations • 17 Jan 2024 • Weiyao Wang, Pierre Gleize, Hao Tang, Xingyu Chen, Kevin J Liang, Matt Feiszli
Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images.
no code implementations • 15 Jan 2024 • Hao Tang, Ling Shao, Nicu Sebe, Luc van Gool
Finally, we propose a novel self-guided pre-training method for graph representation learning.
Generative Adversarial Network Graph Representation Learning +1
1 code implementation • 26 Dec 2023 • Yuxuan Zhang, Yiren Song, Jiaming Liu, Rui Wang, Jinpeng Yu, Hao Tang, Huaxia Li, Xu Tang, Yao Hu, Han Pan, Zhongliang Jing
Recent advancements in subject-driven image generation have led to zero-shot generation, yet precise selection and focus on crucial subject representations remain challenging.
1 code implementation • 15 Dec 2023 • Xiaofeng Zhang, Zishan Xu, Hao Tang, Chaochen Gu, Wei Chen, Shanying Zhu, Xinping Guan
Low-light image enhancement is a crucial visual task, and many unsupervised methods tend to overlook the degradation of visible information in low-light scenes, which adversely affects the fusion of complementary information and hinders the generation of satisfactory results.
no code implementations • 13 Dec 2023 • Liangchen Song, Liangliang Cao, Jiatao Gu, Yifan Jiang, Junsong Yuan, Hao Tang
In this work, we propose that by incorporating correspondence regularization into diffusion models, the process of 3D editing can be significantly accelerated.
no code implementations • 10 Nov 2023 • Ziye Fang, Xin Jiang, Hao Tang, Zechao Li
In the field of intelligent multimedia analysis, ultra-fine-grained visual categorization (Ultra-FGVC) plays a vital role in distinguishing intricate subcategories within broader categories.
1 code implementation • 7 Nov 2023 • Neng Dong, Shuanglin Yan, Hao Tang, Jinhui Tang, Liyan Zhang
Moreover, as multiple images with the same identity are not accessible in the testing stage, we devise an Information Propagation (IP) mechanism to distill knowledge from the comprehensive representation to that of a single occluded image.
no code implementations • 2 Nov 2023 • Qingsen Yan, Tao Hu, Yuan Sun, Hao Tang, Yu Zhu, Wei Dong, Luc van Gool, Yanning Zhang
To address this challenge, we formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition, consisting of the feature condition generator and the noise predictor.
no code implementations • 26 Oct 2023 • Gene-Ping Yang, Hao Tang
We study two key properties that enable matching, namely, whether cluster centroids of self-supervised representations reduce the variability of phone instances and respect the relationship among phones.
no code implementations • 19 Oct 2023 • Changhao Li, Boning Li, Omar Amer, Ruslan Shaydulin, Shouvanik Chakrabarti, Guoqing Wang, Haowei Xu, Hao Tang, Isidor Schoch, Niraj Kumar, Charles Lim, Ju Li, Paola Cappellaro, Marco Pistoia
Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes.
no code implementations • 15 Oct 2023 • Jiahao Xia, Gavin Gong, Jiawei Liu, Zhigang Zhu, Hao Tang
In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sourced geospatial data including LiDAR data and satellite imagery data.
2 code implementations • NeurIPS 2023 • Beining Yang, Kai Wang, Qingyun Sun, Cheng Ji, Xingcheng Fu, Hao Tang, Yang You, JianXin Li
We validate the proposed SGDD across 9 datasets and achieve state-of-the-art results on all of them: for example, on the YelpChi dataset, our approach maintains 98. 6% test accuracy of training on the original graph dataset with 1, 000 times saving on the scale of the graph.
no code implementations • 4 Oct 2023 • Yifan Jiang, Hao Tang, Jen-Hao Rick Chang, Liangchen Song, Zhangyang Wang, Liangliang Cao
Although the fidelity and generalizability are greatly improved, training such a powerful diffusion model requires a vast volume of training data and model parameters, resulting in a notoriously long time and high computational costs.
no code implementations • 28 Sep 2023 • Sanghwan Kim, Hao Tang, Fisher Yu
Notably, our method incurs negligible computational overhead compared to previous distillation techniques, facilitating straightforward and rapid integration with existing samplers.
no code implementations • 20 Sep 2023 • Yifeng Xiong, Haoyu Ma, Shanlin Sun, Kun Han, Hao Tang, Xiaohui Xie
Starting from the camera pose matrices, LFD transforms them into light field encoding, with the same shape as the reference image, to describe the direction of each ray.
no code implementations • 16 Sep 2023 • Xin Jiang, Hao Tang, Junyao Gao, Xiaoyu Du, Shengfeng He, Zechao Li
In this paper, we aim to fully exploit the capabilities of cross-modal description to tackle FGVC tasks and propose a novel multimodal prompting solution, denoted as MP-FGVC, based on the contrastive language-image pertaining (CLIP) model.
Ranked #6 on Fine-Grained Image Classification on NABirds
1 code implementation • 14 Sep 2023 • Zhaochong An, Guolei Sun, Zongwei Wu, Hao Tang, Luc van Gool
Modern approaches have proved the huge potential of addressing semantic segmentation as a mask classification task which is widely used in instance-level segmentation.
1 code implementation • 4 Sep 2023 • Lei Ding, Kun Zhu, Daifeng Peng, Hao Tang, Kuiwu Yang, Lorenzo Bruzzone
In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve the change detection of high-resolution Remote Sensing Images (RSIs).
2 code implementations • ICCV 2023 • Haiyang Wang, Hao Tang, Shaoshuai Shi, Aoxue Li, Zhenguo Li, Bernt Schiele, LiWei Wang
Jointly processing information from multiple sensors is crucial to achieving accurate and robust perception for reliable autonomous driving systems.
Ranked #8 on 3D Object Detection on nuScenes
no code implementations • 6 Aug 2023 • Hao Tang, Jun Liu, Shuanglin Yan, Rui Yan, Zechao Li, Jinhui Tang
Due to the scarcity of manually annotated data required for fine-grained video understanding, few-shot fine-grained (FS-FG) action recognition has gained significant attention, with the aim of classifying novel fine-grained action categories with only a few labeled instances.
no code implementations • 31 Jul 2023 • Elia Peruzzo, Willi Menapace, Vidit Goel, Federica Arrigoni, Hao Tang, Xingqian Xu, Arman Chopikyan, Nikita Orlov, Yuxiao Hu, Humphrey Shi, Nicu Sebe, Elisa Ricci
This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP.
no code implementations • 23 Jul 2023 • Shanlin Sun, Thanh-Tung Le, Chenyu You, Hao Tang, Kun Han, Haoyu Ma, Deying Kong, Xiangyi Yan, Xiaohui Xie
We present Hybrid-CSR, a geometric deep-learning model that combines explicit and implicit shape representations for cortical surface reconstruction.
1 code implementation • 22 Jul 2023 • Hao Tang, Guolei Sun, Nicu Sebe, Luc van Gool
To tackle 2), we design an effective module to selectively highlight class-dependent feature maps according to the original semantic layout to preserve the semantic information.
1 code implementation • 14 Jul 2023 • Neng Dong, Liyan Zhang, Shuanglin Yan, Hao Tang, Jinhui Tang
Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational resources and only consider the issue of missing information caused by occlusion.
1 code implementation • 21 Jun 2023 • Chengchao Shen, Dawei Liu, Hao Tang, Zhe Qu, Jianxin Wang
In this paper, we propose a novel image mix method, PatchMix, for contrastive learning in Vision Transformer (ViT), to model inter-instance similarities among images.
2 code implementations • 17 Jun 2023 • Qihan Zhao, Xiaofeng Zhang, Hao Tang, Chaochen Gu, Shanying Zhu
Image restoration is a low-level visual task, and most CNN methods are designed as black boxes, lacking transparency and intrinsic aesthetics.
no code implementations • 3 Jun 2023 • Ramon Sanabria, Ondrej Klejch, Hao Tang, Sharon Goldwater
Acoustic word embeddings are typically created by training a pooling function using pairs of word-like units.
no code implementations • 1 Jun 2023 • Linhui Dai, Hong Liu, Pinhao Song, Hao Tang, Runwei Ding, Shengquan Li
The key to addressing these challenges is to focus the model on obtaining more discriminative information.
1 code implementation • 24 May 2023 • Tong Xu, Micol Spitale, Hao Tang, Lu Liu, Hatice Gunes, Siyang Song
This means that we approach this problem by considering the generation of a distribution of the listener's appropriate facial reactions instead of multiple different appropriate facial reactions, i. e., 'many' appropriate facial reaction labels are summarised as 'one' distribution label during training.
no code implementations • 21 May 2023 • Oli Liu, Hao Tang, Sharon Goldwater
Self-supervised speech representations are known to encode both speaker and phonetic information, but how they are distributed in the high-dimensional space remains largely unexplored.
1 code implementation • 19 May 2023 • Tom Hosking, Hao Tang, Mirella Lapata
We propose a method for unsupervised opinion summarization that encodes sentences from customer reviews into a hierarchical discrete latent space, then identifies common opinions based on the frequency of their encodings.
no code implementations • CVPR 2023 • Hao Tang, Songhua Liu, Tianwei Lin, Shaoli Huang, Fu Li, Dongliang He, Xinchao Wang
On the other hand, different from the vanilla version, we adopt a learnable scaling operation on content features before content-style feature interaction, which better preserves the original similarity between a pair of content features while ensuring the stylization quality.
no code implementations • CVPR 2023 • Qingsen Yan, Song Zhang, Weiye Chen, Hao Tang, Yu Zhu, Jinqiu Sun, Luc van Gool, Yanning Zhang
In this work, we propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR.
no code implementations • 6 Apr 2023 • Xiangyi Yan, Junayed Naushad, Chenyu You, Hao Tang, Shanlin Sun, Kun Han, Haoyu Ma, James Duncan, Xiaohui Xie
In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supervised pre-training methods for medical image segmentation.
no code implementations • CVPR 2023 • Guofeng Mei, Hao Tang, Xiaoshui Huang, Weijie Wang, Juan Liu, Jian Zhang, Luc van Gool, Qiang Wu
Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data.
no code implementations • CVPR 2023 • Hao Tang, Zhenyu Zhang, Humphrey Shi, Bo Li, Ling Shao, Nicu Sebe, Radu Timofte, Luc van Gool
We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task.
no code implementations • 9 Mar 2023 • Hao Tang, Aref Miri Rekavandi, Dharjinder Rooprai, Girish Dwivedi, Frank Sanfilippo, Farid Boussaid, Mohammed Bennamoun
This study investigates the effectiveness of Explainable Artificial Intelligence (XAI) techniques in predicting suicide risks and identifying the dominant causes for such behaviours.
1 code implementation • CVPR 2023 • Xuan Shen, Yaohua Wang, Ming Lin, Yilun Huang, Hao Tang, Xiuyu Sun, Yanzhi Wang
To this end, a novel framework termed Mathematical Architecture Design for Deep CNN (DeepMAD) is proposed to design high-performance CNN models in a principled way.
Ranked #1 on Neural Architecture Search on ImageNet
1 code implementation • 3 Feb 2023 • Chao Yu, Jiaxuan Gao, Weilin Liu, Botian Xu, Hao Tang, Jiaqi Yang, Yu Wang, Yi Wu
A crucial limitation of this framework is that every policy in the pool is optimized w. r. t.
2 code implementations • CVPR 2023 • Ming Tao, Bing-Kun Bao, Hao Tang, Changsheng Xu
The complex scene understanding ability of CLIP enables the discriminator to accurately assess the image quality.
Ranked #3 on Text-to-Image Generation on CUB
1 code implementation • 24 Jan 2023 • Baptiste Chopin, Hao Tang, Mohamed Daoudi
The generation of natural human motion interactions is a hot topic in computer vision and computer animation.
no code implementations • ICCV 2023 • Jianbing Wu, Hong Liu, Yuxin Su, Wei Shi, Hao Tang
Owing to the large distribution gap between the heterogeneous data in Visible-Infrared Person Re-identification (VI Re-ID), we point out that existing paradigms often suffer from the inter-modal semantic misalignment issue and thus fail to align and compare local details properly.
no code implementations • CVPR 2023 • Changdi Yang, Pu Zhao, Yanyu Li, Wei Niu, Jiexiong Guan, Hao Tang, Minghai Qin, Bin Ren, Xue Lin, Yanzhi Wang
With the ever-increasing popularity of edge devices, it is necessary to implement real-time segmentation on the edge for autonomous driving and many other applications.
1 code implementation • 12 Dec 2022 • Hui Wei, Zhixiang Wang, Xuemei Jia, Yinqiang Zheng, Hao Tang, Shin'ichi Satoh, Zheng Wang
Adversarial attacks on thermal infrared imaging expose the risk of related applications.
no code implementations • 7 Dec 2022 • Hao Ding, Changchang Sun, Hao Tang, Dawen Cai, Yan Yan
Recently, due to the increasing requirements of medical imaging applications and the professional requirements of annotating medical images, few-shot learning has gained increasing attention in the medical image semantic segmentation field.
1 code implementation • 19 Nov 2022 • Zhenglun Kong, Haoyu Ma, Geng Yuan, Mengshu Sun, Yanyue Xie, Peiyan Dong, Xin Meng, Xuan Shen, Hao Tang, Minghai Qin, Tianlong Chen, Xiaolong Ma, Xiaohui Xie, Zhangyang Wang, Yanzhi Wang
Vision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit their generalization.
1 code implementation • 17 Nov 2022 • Tzu-Quan Lin, Hung-Yi Lee, Hao Tang
Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks.
1 code implementation • 17 Nov 2022 • Tzu-Quan Lin, Tsung-Huan Yang, Chun-Yao Chang, Kuang-Ming Chen, Tzu-hsun Feng, Hung-Yi Lee, Hao Tang
Despite the success of Transformers in self- supervised learning with applications to various downstream tasks, the computational cost of training and inference remains a major challenge for applying these models to a wide spectrum of devices.
no code implementations • 12 Nov 2022 • Hao Tang, Lei Ding, Songsong Wu, Bin Ren, Nicu Sebe, Paolo Rota
The proposed TSDPC is a generic and powerful framework and it has two advantages compared with previous works, one is that it can calculate the number of key frames automatically.
1 code implementation • 12 Nov 2022 • Hao Tang, Ling Shao, Philip H. S. Torr, Nicu Sebe
To further capture the change in pose of each part more precisely, we propose a novel part-aware bipartite graph reasoning (PBGR) block to decompose the task of reasoning the global structure transformation with a bipartite graph into learning different local transformations for different semantic body/face parts.
1 code implementation • 2 Nov 2022 • Xuan Shen, Zhenglun Kong, Minghai Qin, Peiyan Dong, Geng Yuan, Xin Meng, Hao Tang, Xiaolong Ma, Yanzhi Wang
That is, there exists a subset of input image patches such that a ViT can be trained from scratch by using only this subset of patches and achieve similar accuracy to the ViTs trained by using all image patches.
no code implementations • 29 Oct 2022 • Sung-Lin Yeh, Hao Tang
While discrete latent variable models have had great success in self-supervised learning, most models assume that frames are independent.
no code implementations • 28 Oct 2022 • Ramon Sanabria, Hao Tang, Sharon Goldwater
Given the strong results of self-supervised models on various tasks, there have been surprisingly few studies exploring self-supervised representations for acoustic word embeddings (AWE), fixed-dimensional vectors representing variable-length spoken word segments.
1 code implementation • 27 Oct 2022 • Chin-Yun Yu, Sung-Lin Yeh, György Fazekas, Hao Tang
Moreover, by coupling the proposed sampling method with an unconditional DM, i. e., a DM with no auxiliary inputs to its noise predictor, we can generalize it to a wide range of SR setups.
no code implementations • 19 Oct 2022 • Peng Xing, Hao Tang, Jinhui Tang, Zechao Li
However, existing KDAD methods suffer from two main limitations: 1) the student network can effortlessly replicate the teacher network's representations, and 2) the features of the teacher network serve solely as a ``reference standard" and are not fully leveraged.
no code implementations • 13 Oct 2022 • Yen Meng, Hsuan-Jui Chen, Jiatong Shi, Shinji Watanabe, Paola Garcia, Hung-Yi Lee, Hao Tang
Subsampling while training self-supervised models not only improves the overall performance on downstream tasks under certain frame rates, but also brings significant speed-up in inference.
1 code implementation • 10 Oct 2022 • Yitong Xia, Hao Tang, Radu Timofte, Luc van Gool
NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization tasks, i. e., reconstructing real-world scenes and registering camera parameters simultaneously.
1 code implementation • 4 Oct 2022 • Zican Zha, Hao Tang, Yunlian Sun, Jinhui Tang
To address this challenging task, we propose a two-stage background suppression and foreground alignment framework, which is composed of a background activation suppression (BAS) module, a foreground object alignment (FOA) module, and a local-to-local (L2L) similarity metric.
1 code implementation • 30 Sep 2022 • Hui Wei, Hao Tang, Xuemei Jia, Zhixiang Wang, Hanxun Yu, Zhubo Li, Shin'ichi Satoh, Luc van Gool, Zheng Wang
Building upon this foundation, we uncover the pervasive role of artifacts carrying adversarial perturbations in the physical world.
1 code implementation • 16 Sep 2022 • Haoyu Ma, Zhe Wang, Yifei Chen, Deying Kong, Liangjian Chen, Xingwei Liu, Xiangyi Yan, Hao Tang, Xiaohui Xie
In this paper, we propose the token-Pruned Pose Transformer (PPT) for 2D human pose estimation, which can locate a rough human mask and performs self-attention only within selected tokens.
Ranked #17 on 3D Human Pose Estimation on Human3.6M (using extra training data)
1 code implementation • 5 Sep 2022 • Hao Tang, Nicu Sebe
We propose a simple yet powerful Landmark guided Generative Adversarial Network (LandmarkGAN) for the facial expression-to-expression translation using a single image, which is an important and challenging task in computer vision since the expression-to-expression translation is a non-linear and non-aligned problem.
Facial Expression Translation Generative Adversarial Network +1
no code implementations • 30 Aug 2022 • Shuanglin Yan, Hao Tang, Liyan Zhang, Jinhui Tang
Moreover, existing methods seldom consider the information inequality problem between modalities caused by image-specific information.
1 code implementation • 26 Aug 2022 • Jichao Zhang, Aliaksandr Siarohin, Yahui Liu, Hao Tang, Nicu Sebe, Wei Wang
Generative Neural Radiance Fields (GNeRF) based 3D-aware GANs have demonstrated remarkable capabilities in generating high-quality images while maintaining strong 3D consistency.
1 code implementation • 25 Aug 2022 • Jianbing Wu, Hong Liu, Wei Shi, Hao Tang, Jingwen Guo
To mitigate the resolution degradation issue and mine identity-sensitive cues from human faces, we propose to restore the missing facial details using prior facial knowledge, which is then propagated to a smaller network.
no code implementations • 19 Aug 2022 • Pan Xie, Qipeng Zhang, Taiyi Peng, Hao Tang, Yao Du, Zexian Li
Our approach focuses on the transformation of sign gloss sequences into their corresponding sign pose sequences (G2P).
no code implementations • 10 Aug 2022 • Zhengang Li, Mengshu Sun, Alec Lu, Haoyu Ma, Geng Yuan, Yanyue Xie, Hao Tang, Yanyu Li, Miriam Leeser, Zhangyang Wang, Xue Lin, Zhenman Fang
Compared with state-of-the-art ViT quantization work (algorithmic approach only without hardware acceleration), our quantization achieves 0. 47% to 1. 36% higher Top-1 accuracy under the same bit-width.
1 code implementation • 25 Jul 2022 • Yushu Wu, Yifan Gong, Pu Zhao, Yanyu Li, Zheng Zhan, Wei Niu, Hao Tang, Minghai Qin, Bin Ren, Yanzhi Wang
Instead of measuring the speed on mobile devices at each iteration during the search process, a speed model incorporated with compiler optimizations is leveraged to predict the inference latency of the SR block with various width configurations for faster convergence.
1 code implementation • 21 Jul 2022 • Guolei Sun, Yun Liu, Hao Tang, Ajad Chhatkuli, Le Zhang, Luc van Gool
The essence of video semantic segmentation (VSS) is how to leverage temporal information for prediction.
1 code implementation • 21 Jul 2022 • JieZhang Cao, Jingyun Liang, Kai Zhang, Wenguan Wang, Qin Wang, Yulun Zhang, Hao Tang, Luc van Gool
These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences.
no code implementations • 17 Jul 2022 • Bin Xie, Hao Tang, Bin Duan, Dawen Cai, Yan Yan
Brain vessel image segmentation can be used as a promising biomarker for better prevention and treatment of different diseases.
no code implementations • 16 Jul 2022 • Daqian Shi, Xiaolei Diao, Hao Tang, Xiaomin Li, Hao Xing, Hao Xu
SENet aims to preserve the structural consistency of the character and normalize complex noise.
1 code implementation • 16 Jul 2022 • Daqian Shi, Xiaolei Diao, Lida Shi, Hao Tang, Yang Chi, Chuntao Li, Hao Xu
Degraded images commonly exist in the general sources of character images, leading to unsatisfactory character recognition results.
no code implementations • 12 Jul 2022 • Xiaolei Diao, Daqian Shi, Hao Tang, Qiang Shen, Yanzeng Li, Lei Wu, Hao Xu
The long-tail effect is a common issue that limits the performance of deep learning models on real-world datasets.
1 code implementation • 9 Jul 2022 • Bin Ren, Hao Tang, Yiming Wang, Xia Li, Wei Wang, Nicu Sebe
For semantic-guided cross-view image translation, it is crucial to learn where to sample pixels from the source view image and where to reallocate them guided by the target view semantic map, especially when there is little overlap or drastic view difference between the source and target images.
1 code implementation • 7 Jul 2022 • Zhan Chen, Hong Liu, Tianyu Guo, Zhengyan Chen, Pinhao Song, Hao Tang
First, SkeleMix utilizes the topological information of skeleton data to mix two skeleton sequences by randomly combing the cropped skeleton fragments (the trimmed view) with the remaining skeleton sequences (the truncated view).
1 code implementation • 4 Jul 2022 • Baptiste Chopin, Hao Tang, Naima Otberdout, Mohamed Daoudi, Nicu Sebe
To address this limitation, we propose a novel interaction Transformer (InterFormer) consisting of a Transformer network with both temporal and spatial attention.
1 code implementation • 1 Jul 2022 • Jichao Zhang, Jingjing Chen, Hao Tang, Enver Sangineto, Peng Wu, Yan Yan, Nicu Sebe, Wei Wang
Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels.
1 code implementation • 29 Jun 2022 • Sherwin Bahmani, Jeong Joon Park, Despoina Paschalidou, Hao Tang, Gordon Wetzstein, Leonidas Guibas, Luc van Gool, Radu Timofte
Generative models have emerged as an essential building block for many image synthesis and editing tasks.
no code implementations • 13 Jun 2022 • Hao Tang, Kevin Ellis
Toward combining inductive reasoning with perception abilities, we develop techniques for neurosymbolic program synthesis where perceptual input is first parsed by neural nets into a low-dimensional interpretable representation, which is then processed by a synthesized program.
1 code implementation • 13 Jun 2022 • Wenhao Li, Hong Liu, Tianyu Guo, Runwei Ding, Hao Tang
To the best of our knowledge, this is the first MLP-Like architecture for 3D human pose estimation in a single frame and a video sequence.
Ranked #53 on 3D Human Pose Estimation on Human3.6M
no code implementations • 7 Jun 2022 • Shanlin Sun, Kun Han, Hao Tang, Deying Kong, Junayed Naushad, Xiangyi Yan, Xiaohui Xie
Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images.
1 code implementation • 2 Jun 2022 • Ming Tao, Bing-Kun Bao, Hao Tang, Fei Wu, Longhui Wei, Qi Tian
To solve these limitations, we propose: (i) a Dynamic Editing Block (DEBlock) which composes different editing modules dynamically for various editing requirements.
no code implementations • 2 Jun 2022 • Yanyu Li, Xuan Shen, Geng Yuan, Jiexiong Guan, Wei Niu, Hao Tang, Bin Ren, Yanzhi Wang
In this work we demonstrate real-time portrait stylization, specifically, translating self-portrait into cartoon or anime style on mobile devices.
1 code implementation • 25 May 2022 • Linhui Dai, Hong Liu, Hao Tang, Zhiwei Wu, Pinhao Song
Comprehensive experiments on several challenging datasets show that our method achieves superior performance on the AOOD task.
no code implementations • 25 Apr 2022 • Gene-Ping Yang, Hao Tang
Attention mechanism in sequence-to-sequence models is designed to model the alignments between acoustic features and output tokens in speech recognition.
no code implementations • 2 Apr 2022 • Zeyong Wei, Honghua Chen, Hao Tang, Qian Xie, Mingqiang Wei, Jun Wang
The shape of circle is one of fundamental geometric primitives of man-made engineering objects.
1 code implementation • 29 Mar 2022 • Sung-Lin Yeh, Hao Tang
While several self-supervised approaches for learning discrete speech representation have been proposed, it is unclear how these seemingly similar approaches relate to each other.
2 code implementations • 24 Mar 2022 • Kai Zhang, Yawei Li, Jingyun Liang, JieZhang Cao, Yulun Zhang, Hao Tang, Deng-Ping Fan, Radu Timofte, Luc van Gool
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved.
Ranked #1 on Image Denoising on urban100 sigma15
1 code implementation • 22 Mar 2022 • Songsong Wu, Hao Tang, Xiao-Yuan Jing, Haifeng Zhao, Jianjun Qian, Nicu Sebe, Yan Yan
In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a top-view aerial image, which is a challenging problem due to the large gap between the two image domains with different view-points.
1 code implementation • CVPR 2022 • Shanlin Sun, Kun Han, Deying Kong, Hao Tang, Xiangyi Yan, Xiaohui Xie
Recently DIFs-based methods have been proposed to handle shape reconstruction and dense point correspondences simultaneously, capturing semantic relationships across shapes of the same class by learning a DIFs-modeled shape template.
1 code implementation • ACL 2022 • Tom Hosking, Hao Tang, Mirella Lapata
We propose a generative model of paraphrase generation, that encourages syntactic diversity by conditioning on an explicit syntactic sketch.
Ranked #1 on Paraphrase Generation on Paralex
no code implementations • 4 Mar 2022 • Yu-Xin Jin, Jun-Jie Hu, Qi Li, Zhi-Cheng Luo, Fang-Yan Zhang, Hao Tang, Kun Qian, Xian-Min Jin
New COVID-19 epidemic strains like Delta and Omicron with increased transmissibility and pathogenicity emerge and spread across the whole world rapidly while causing high mortality during the pandemic period.
1 code implementation • 28 Feb 2022 • Hao Tang, Ling Shao, Philip H. S. Torr, Nicu Sebe
To learn more discriminative class-specific feature representations for the local generation, we also propose a novel classification module.
no code implementations • 25 Feb 2022 • Kun Han, Shanlin Sun, Xiangyi Yan, Chenyu You, Hao Tang, Junayed Naushad, Haoyu Ma, Deying Kong, Xiaohui Xie
Here we propose a new optimization-based method named DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which utilizes deep neural network to model the space of admissible transformations.
no code implementations • 8 Feb 2022 • Yue Song, Hao Tang, Nicu Sebe, Wei Wang
Specifically, the detail modeling focuses on capturing the object edges by supervision of explicitly decomposed detail label that consists of the pixels that are nested on the edge and near the edge.
1 code implementation • 1 Feb 2022 • Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Hao Tang, Xavier Alameda-Pineda, Elisa Ricci
To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channel-level dependencies.
no code implementations • CVPR 2022 • Zhenyu Zhang, Yanhao Ge, Ying Tai, Xiaoming Huang, Chengjie Wang, Hao Tang, Dongjin Huang, Zhifeng Xie
In-the-wild 3D face modelling is a challenging problem as the predicted facial geometry and texture suffer from a lack of reliable clues or priors, when the input images are degraded.
no code implementations • CVPR 2022 • Zhenyu Zhang, Yanhao Ge, Ying Tai, Weijian Cao, Renwang Chen, Kunlin Liu, Hao Tang, Xiaoming Huang, Chengjie Wang, Zhifeng Xie, Dongjin Huang
This paper presents a novel Physically-guided Disentangled Implicit Rendering (PhyDIR) framework for high-fidelity 3D face modeling.
1 code implementation • 27 Dec 2021 • Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Mengshu Sun, Wei Niu, Xuan Shen, Geng Yuan, Bin Ren, Minghai Qin, Hao Tang, Yanzhi Wang
Moreover, our framework can guarantee the identified model to meet resource specifications of mobile devices and FPGA, and even achieve the real-time execution of DeiT-T on mobile platforms.
Ranked #4 on Efficient ViTs on ImageNet-1K (with DeiT-S)
1 code implementation • 14 Dec 2021 • Yidi Li, Hong Liu, Hao Tang
Multi-modal fusion is proven to be an effective method to improve the accuracy and robustness of speaker tracking, especially in complex scenarios.
1 code implementation • 14 Dec 2021 • Haoyu Chen, Hao Tang, Zitong Yu, Nicu Sebe, Guoying Zhao
Specifically, we propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies across the given meshes.
1 code implementation • 10 Dec 2021 • Nicola Dall'Asen, Yiming Wang, Hao Tang, Luca Zanella, Elisa Ricci
With the goal to maintain the geometric attributes of the source face, i. e., the facial pose and expression, and to promote more natural face generation, we propose to exploit a Bipartite Graph to explicitly model the relations between the facial landmarks of the source identity and the ones of the condition identity through a deep model.
1 code implementation • 2 Dec 2021 • Jichao Zhang, Enver Sangineto, Hao Tang, Aliaksandr Siarohin, Zhun Zhong, Nicu Sebe, Wei Wang
However, they usually struggle to generate high-quality images representing non-rigid objects, such as the human body, which is of a great interest for many computer graphics applications.
1 code implementation • CVPR 2022 • Zipeng Xu, Tianwei Lin, Hao Tang, Fu Li, Dongliang He, Nicu Sebe, Radu Timofte, Luc van Gool, Errui Ding
We propose a novel framework, i. e., Predict, Prevent, and Evaluate (PPE), for disentangled text-driven image manipulation that requires little manual annotation while being applicable to a wide variety of manipulations.
1 code implementation • CVPR 2022 • Wenhao Li, Hong Liu, Hao Tang, Pichao Wang, Luc van Gool
Estimating 3D human poses from monocular videos is a challenging task due to depth ambiguity and self-occlusion.
Ranked #22 on 3D Human Pose Estimation on MPI-INF-3DHP
1 code implementation • 19 Nov 2021 • Guanglei Yang, Hao Tang, Humphrey Shi, Mingli Ding, Nicu Sebe, Radu Timofte, Luc van Gool, Elisa Ricci
The global alignment network aims to transfer the input image from the source domain to the target domain.
1 code implementation • 19 Nov 2021 • Guanglei Yang, Zhun Zhong, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci
Specifically, in the image translation stage, Bi-Mix leverages the knowledge of day-night image pairs to improve the quality of nighttime image relighting.
no code implementations • 25 Oct 2021 • Yijie Zhong, Bo Li, Lv Tang, Hao Tang, Shouhong Ding
With a lightweight basic convolution block, we build a two-stages framework: Segmentation Network (SN) is designed to capture sufficient semantics and classify the pixels into unknown, foreground and background regions; Matting Refine Network (MRN) aims at capturing detailed texture information and regressing accurate alpha values.
no code implementations • 20 Oct 2021 • Xiangyi Yan, Hao Tang, Shanlin Sun, Haoyu Ma, Deying Kong, Xiaohui Xie
One has to either downsample the image or use cropped local patches to reduce GPU memory usage, which limits its performance.
1 code implementation • 20 Oct 2021 • Haoyu Chen, Hao Tang, Nicu Sebe, Guoying Zhao
Instead, we introduce AniFormer, a novel Transformer-based architecture, that generates animated 3D sequences by directly taking the raw driving sequences and arbitrary same-type target meshes as inputs.
1 code implementation • 19 Oct 2021 • Bin Ren, Hao Tang, Nicu Sebe
To ease this problem, we propose a novel two-stage framework with a new Cascaded Cross MLP-Mixer (CrossMLP) sub-network in the first stage and one refined pixel-level loss in the second stage.
1 code implementation • 18 Oct 2021 • Haoyu Ma, Liangjian Chen, Deying Kong, Zhe Wang, Xingwei Liu, Hao Tang, Xiangyi Yan, Yusheng Xie, Shih-Yao Lin, Xiaohui Xie
The 3D position encoding guided by the epipolar field provides an efficient way of encoding correspondences between pixels of different views.
Ranked #20 on 3D Human Pose Estimation on Human3.6M (using extra training data)
no code implementations • 29 Sep 2021 • Fan-Yun Sun, Jonathan Kuck, Hao Tang, Stefano Ermon
Several indices used in a factor graph data structure can be permuted without changing the underlying probability distribution.
no code implementations • 29 Sep 2021 • Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Mengshu Sun, Wei Niu, Bin Ren, Minghai Qin, Hao Tang, Yanzhi Wang
Recently, Vision Transformer (ViT) has continuously established new milestones in the computer vision field, while the high computation and memory cost makes its propagation in industrial production difficult.
no code implementations • 29 Sep 2021 • Haoyu Ma, Yifan Huang, Tianlong Chen, Hao Tang, Chenyu You, Zhangyang Wang, Xiaohui Xie
However, it is unclear why the distorted distribution of the logits is catastrophic to the student model.
no code implementations • EMNLP (insights) 2021 • Ramon Sanabria, Hao Tang, Sharon Goldwater
Word segmentation, the problem of finding word boundaries in speech, is of interest for a range of tasks.
1 code implementation • 1 Sep 2021 • Hao Tang, Guoshuai Zhao, Yuxia Wu, Xueming Qian
Therefore, we propose a Multi-Sample based Contrastive Loss (MSCL) function which solves the two problems by balancing the importance of positive and negative samples and data augmentation.
1 code implementation • 29 Aug 2021 • Hao Tang, Nicu Sebe
In this paper, we address the task of layout-to-image translation, which aims to translate an input semantic layout to a realistic image.
1 code implementation • ICCV 2021 • Haoyu Chen, Hao Tang, Henglin Shi, Wei Peng, Nicu Sebe, Guoying Zhao
With the strength of deep generative models, 3D pose transfer regains intensive research interests in recent years.
1 code implementation • ICCV 2021 • Hao Tang, Xingwei Liu, Shanlin Sun, Xiangyi Yan, Xiaohui Xie
Although having achieved great success in medical image segmentation, deep convolutional neural networks usually require a large dataset with manual annotations for training and are difficult to generalize to unseen classes.
no code implementations • 7 Jul 2021 • Gaowen Liu, Hao Tang, Hugo Latapie, Jason Corso, Yan Yan
Particularly, we propose a novel Bi-directional Spatial Temporal Attention Fusion Generative Adversarial Network (STA-GAN) to learn both spatial and temporal information to generate egocentric video sequences from the exocentric view.
1 code implementation • 29 Jun 2021 • Lei Ding, Dong Lin, Shaofu Lin, Jing Zhang, Xiaojie Cui, Yuebin Wang, Hao Tang, Lorenzo Bruzzone
To overcome this limitation, we propose a Wide-Context Network (WiCoNet) for the semantic segmentation of HR RSIs.
1 code implementation • 21 Jun 2021 • Hao Tang, Nicu Sebe
Both generators are mutually connected and trained in an end-to-end fashion and explicitly form three cycled subnets, i. e., one image generation cycle and two guidance generation cycles.
Generative Adversarial Network Image-to-Image Translation +1
1 code implementation • 31 May 2021 • Jichao Zhang, Aliaksandr Siarohin, Hao Tang, Enver Sangineto, Wei Wang, Humphrey Sh, Nicu Sebe
Moreover, we propose a novel Self-Training Part Replacement (STPR) strategy to refine the model for the texture-transfer task, which improves the quality of the generated clothes and the preservation ability of non-target regions.
1 code implementation • 28 May 2021 • Guanglei Yang, Hao Tang, Zhun Zhong, Mingli Ding, Ling Shao, Nicu Sebe, Elisa Ricci
In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation.
1 code implementation • 12 Apr 2021 • Bin Ren, Hao Tang, Fanyang Meng, Runwei Ding, Philip H. S. Torr, Nicu Sebe
In the second stage, we put forth a CIT reasoning block for establishing global mutual interactive dependencies among person representation, the warped clothing item, and the corresponding warped cloth mask.
1 code implementation • ICCV 2021 • Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci
While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation.
Ranked #8 on Depth Estimation on NYU-Depth V2
1 code implementation • 22 Feb 2021 • Lei Ding, Hao Tang, Yahui Liu, Yilei Shi, Xiao Xiang Zhu, Lorenzo Bruzzone
To address this issue, we propose an adversarial shape learning network (ASLNet) to model the building shape patterns that improve the accuracy of building segmentation.
no code implementations • 1 Jan 2021 • Hao Tang, Nicu Sebe
We propose the Semantically-Adaptive UpSampling (SA-UpSample), a general and highly effective upsampling method for the layout-to-image translation task.
no code implementations • 16 Dec 2020 • Hao Tang, Xingwei Liu, Kun Han, Shanlin Sun, Narisu Bai, Xuming Chen, Huang Qian, Yong liu, Xiaohui Xie
State-of-the-art CNN segmentation models apply either 2D or 3D convolutions on input images, with pros and cons associated with each method: 2D convolution is fast, less memory-intensive but inadequate for extracting 3D contextual information from volumetric images, while the opposite is true for 3D convolution.
no code implementations • NeurIPS 2020 • Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-liang Lu, Hao Su
Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems.
no code implementations • NeurIPS 2020 • Tongzhou Mu, Jiayuan Gu, Zhiwei Jia, Hao Tang, Hao Su
We study how to learn a policy with compositional generalizability.
1 code implementation • 26 Oct 2020 • Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-liang Lu, Hao Su
Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems.
1 code implementation • 29 Aug 2020 • Hao Tang, Song Bai, Nicu Sebe
We also propose two novel modules, i. e., position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention Module (CAM), to capture semantic structure attention in spatial and channel dimensions, respectively.
no code implementations • 14 Aug 2020 • Bin Duan, Hao Tang, Wei Wang, Ziliang Zong, Guowei Yang, Yan Yan
Recent works have shown that attention mechanism is beneficial to the fusion process.
3 code implementations • CVPR 2022 • Ming Tao, Hao Tang, Fei Wu, Xiao-Yuan Jing, Bing-Kun Bao, Changsheng Xu
To these ends, we propose a simpler but more effective Deep Fusion Generative Adversarial Networks (DF-GAN).
Ranked #4 on Text-to-Image Generation on CUB (Inception score metric)
1 code implementation • 10 Aug 2020 • Hao Tang, Song Bai, Philip H. S. Torr, Nicu Sebe
We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task.
Ranked #1 on Pose Transfer on Market-1501 (PCKh metric)
1 code implementation • 9 Aug 2020 • Jichao Zhang, Jingjing Chen, Hao Tang, Wei Wang, Yan Yan, Enver Sangineto, Nicu Sebe
In this paper we address the problem of unsupervised gaze correction in the wild, presenting a solution that works without the need for precise annotations of the gaze angle and the head pose.
no code implementations • 6 Aug 2020 • Hao Tang, Anurag Pal, Lu-Feng Qiao, Tian-Yu Wang, Jun Gao, Xian-Min Jin
Collateralized debt obligation (CDO) has been one of the most commonly used structured financial products and is intensively studied in quantitative finance.
no code implementations • 20 Jul 2020 • Hao Ding, Songsong Wu, Hao Tang, Fei Wu, Guangwei Gao, Xiao-Yuan Jing
This is even more laborious when generating images with very different views.
2 code implementations • ECCV 2020 • Hao Tang, Song Bai, Li Zhang, Philip H. S. Torr, Nicu Sebe
We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i. e., translating the pose of a given person to a desired one.
Ranked #1 on Pose Transfer on Market-1501 (IS metric)
no code implementations • ACL 2020 • Hao Tang, Donghong Ji, Chenliang Li, Qiji Zhou
The idea is to allow the dependency graph to guide the representation learning of the transformer encoder and vice versa.
1 code implementation • NeurIPS 2020 • Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon
Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems.
no code implementations • ACL 2020 • Qiji Zhou, Yue Zhang, Donghong Ji, Hao Tang
Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences.
1 code implementation • 21 May 2020 • Hao Tang, Hong Liu, Wei Xiao, Nicu Sebe
Then the activated dictionary atoms are assembled and passed to the compound dictionary learning and coding layers.
no code implementations • 20 May 2020 • Xinya Chen, Yanrui Bin, Changxin Gao, Nong Sang, Hao Tang
The module builds a fully connected directed graph between the regions of different density where each node (region) is represented by weighted global pooled feature, and GCN is learned to map this region graph to a set of relation-aware regions representations.
2 code implementations • 17 May 2020 • Yu-An Chung, Hao Tang, James Glass
Autoregressive Predictive Coding (APC), as a self-supervised objective, has enjoyed success in learning representations from large amounts of unlabeled data, and the learned representations are rich for many downstream tasks.
2 code implementations • 31 Mar 2020 • Hao Tang, Xiaojuan Qi, Guolei Sun, Dan Xu, Nicu Sebe, Radu Timofte, Luc van Gool
We propose a novel ECGAN for the challenging semantic image synthesis task.
no code implementations • 8 Feb 2020 • Gaowen Liu, Hao Tang, Hugo Latapie, Yan Yan
In this paper, we investigate exocentric (third-person) view to egocentric (first-person) view image generation.
1 code implementation • 3 Feb 2020 • Hao Tang, Philip H. S. Torr, Nicu Sebe
In the first stage, the input image and the conditional semantic guidance are fed into a cycled semantic-guided generation network to produce initial coarse results.
Generative Adversarial Network Image-to-Image Translation +1
no code implementations • 13 Jan 2020 • Shanlin Sun, Yang Liu, Narisu Bai, Hao Tang, Xuming Chen, Qian Huang, Yong liu, Xiaohui Xie
Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning.
2 code implementations • CVPR 2020 • Hao Tang, Dan Xu, Yan Yan, Philip H. S. Torr, Nicu Sebe
To tackle this issue, in this work we consider learning the scene generation in a local context, and correspondingly design a local class-specific generative network with semantic maps as a guidance, which separately constructs and learns sub-generators concentrating on the generation of different classes, and is able to provide more scene details.
1 code implementation • 14 Dec 2019 • Hao Tang, Dan Xu, Hong Liu, Nicu Sebe
In this paper, we analyze the limitation of the existing symmetric GAN models in asymmetric translation tasks, and propose an AsymmetricGAN model with both translation and reconstruction generators of unequal sizes and different parameter-sharing strategy to adapt to the asymmetric need in both unsupervised and supervised image-to-image translation tasks.
1 code implementation • 12 Dec 2019 • Hao Tang, Hong Liu, Nicu Sebe
The proposed model consists of a single generator and a discriminator taking a conditional image and the target controllable structure as input.
Ranked #1 on Cross-View Image-to-Image Translation on cvusa
Facial Expression Translation Generative Adversarial Network +3
2 code implementations • 27 Nov 2019 • Hao Tang, Hong Liu, Dan Xu, Philip H. S. Torr, Nicu Sebe
State-of-the-art methods in image-to-image translation are capable of learning a mapping from a source domain to a target domain with unpaired image data.
Ranked #1 on Facial Expression Translation on CelebA
no code implementations • 20 Nov 2019 • Lei Ding, Hao Tang, Lorenzo Bruzzone
High-level features extracted from the late layers of a neural network are rich in semantic information, yet have blurred spatial details; low-level features extracted from the early layers of a network contain more pixel-level information, but are isolated and noisy.
1 code implementation • 2 Aug 2019 • Hao Tang, Dan Xu, Gaowen Liu, Wei Wang, Nicu Sebe, Yan Yan
In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C$^2$GAN) for the task of keypoint-guided image generation.
1 code implementation • 25 Jul 2019 • Hao Tang, Chupeng Zhang, Xiaohui Xie
Pulmonary nodule detection, false positive reduction and segmentation represent three of the most common tasks in the computeraided analysis of chest CT images.
1 code implementation • 3 Jul 2019 • Bin Duan, Wei Wang, Hao Tang, Hugo Latapie, Yan Yan
However, in machine learning, this cross-modal learning is a nontrivial task because different modalities have no homogeneous properties.
1 code implementation • ACL 2019 • Sharmistha Jat, Hao Tang, Partha Talukdar, Tom Mitchell
To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence.
no code implementations • arXiv 2019 • Jichao Zhang, Meng Sun, Jingjing Chen, Hao Tang, Yan Yan, Xueying Qin, Nicu Sebe
Gaze correction aims to redirect the person's gaze into the camera by manipulating the eye region, and it can be considered as a specific image resynthesis problem.
no code implementations • 14 May 2019 • Hao Tang, Wei Wang, Songsong Wu, Xinya Chen, Dan Xu, Nicu Sebe, Yan Yan
In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute.
no code implementations • 11 May 2019 • Songsong Wu, Zhiqiang Lu, Hao Tang, Yan Yan, Songhao Zhu, Xiao-Yuan Jing, Zuoyong Li
Multi-view subspace clustering aims to divide a set of multisource data into several groups according to their underlying subspace structure.
no code implementations • 11 May 2019 • Achintya kr. Sarkar, Zheng-Hua Tan, Hao Tang, Suwon Shon, James Glass
There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 11 May 2019 • Songsong Wu, Yan Yan, Hao Tang, Jianjun Qian, Jian Zhang, Xiao-Yuan Jing
However, the number of labeled source samples are always limited due to expensive annotation cost in practice, making sub-optimal performance been observed.
3 code implementations • CVPR 2019 • Hao Tang, Dan Xu, Nicu Sebe, Yanzhi Wang, Jason J. Corso, Yan Yan
In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map.
Bird View Synthesis Cross-View Image-to-Image Translation +1
no code implementations • 7 Apr 2019 • Suwon Shon, Hao Tang, James Glass
In this paper, we propose VoiceID loss, a novel loss function for training a speech enhancement model to improve the robustness of speaker verification.
5 code implementations • 5 Apr 2019 • Yu-An Chung, Wei-Ning Hsu, Hao Tang, James Glass
This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations.
8 code implementations • 28 Mar 2019 • Hao Tang, Dan Xu, Nicu Sebe, Yan Yan
To handle the limitation, in this paper we propose a novel Attention-Guided Generative Adversarial Network (AGGAN), which can detect the most discriminative semantic object and minimize changes of unwanted part for semantic manipulation problems without using extra data and models.
Ranked #1 on Facial Expression Translation on CelebA
no code implementations • 23 Mar 2019 • Hao Tang, Daniel R. Kim, Xiaohui Xie
Finally, we introduce a method to ensemble models from both stages via consensus to give the final predictions.
1 code implementation • 23 Mar 2019 • Hao Tang, Chupeng Zhang, Xiaohui Xie
To validate the robustness and performance of our proposed framework trained with a small number of training examples, we further tested our model on CT scans from an independent dataset.
no code implementations • 23 Mar 2019 • Hao Tang, Xingwei Liu, Xiaohui Xie
Most of the existing deep learning nodule detection systems are constructed in two steps: a) nodule candidates screening and b) false positive reduction, using two different models trained separately.
1 code implementation • 31 Jan 2019 • Greg Olmschenk, Hao Tang, Zhigang Zhu
Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event.
1 code implementation • 28 Jan 2019 • Hao Tang, Xinya Chen, Wei Wang, Dan Xu, Jason J. Corso, Nicu Sebe, Yan Yan
To this end, we propose a novel Attribute-Guided Sketch Generative Adversarial Network (ASGAN) which is an end-to-end framework and contains two pairs of generators and discriminators, one of which is used to generate faces with attributes while the other one is employed for image-to-sketch translation.
1 code implementation • 15 Jan 2019 • Hao Tang, Hong Liu, Wei Xiao, Nicu Sebe
Gesture recognition is a hot topic in computer vision and pattern recognition, which plays a vitally important role in natural human-computer interface.
Ranked #1 on Hand Gesture Recognition on Cambridge
1 code implementation • 14 Jan 2019 • Hao Tang, Dan Xu, Wei Wang, Yan Yan, Nicu Sebe
State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data.
Generative Adversarial Network Image-to-Image Translation +1
no code implementations • 27 Nov 2018 • Greg Olmschenk, Zhigang Zhu, Hao Tang
We first demonstrate the capabilities of semi-supervised regression GANs on a toy dataset which allows for a detailed understanding of how they operate in various circumstances.
no code implementations • 31 Oct 2018 • Hao Tang, James Glass
In addition, we study three types of inductive bias, leveraging a pronunciation dictionary, word boundary annotations, and constraints on word durations.
1 code implementation • 12 Sep 2018 • Suwon Shon, Hao Tang, James Glass
In this paper, we propose a Convolutional Neural Network (CNN) based speaker recognition model for extracting robust speaker embeddings.