no code implementations • ECCV 2020 • Jianfei Yang, Han Zou, Yuxun Zhou, Zhaoyang Zeng, Lihua Xie ()
Adversarial domain adaptation has made tremendous success by learning domain-invariant feature representations.
no code implementations • 6 Apr 2024 • Siyu Chen, Kangcheng Liu, Chen Wang, Shenghai Yuan, Jianfei Yang, Lihua Xie
Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs.
no code implementations • 24 Mar 2024 • Junqiao Fan, Jianfei Yang, Yuecong Xu, Lihua Xie
However, the mmWave radar has a limited resolution with severe noise, leading to inaccurate and inconsistent human pose estimation.
no code implementations • 17 Mar 2024 • Tianchen Deng, Yaohui Chen, Leyan Zhang, Jianfei Yang, Shenghai Yuan, Danwei Wang, Weidong Chen
Recent work has shown that 3D Gaussian-based SLAM enables high-quality reconstruction, accurate pose estimation, and real-time rendering of scenes.
no code implementations • 11 Mar 2024 • Haozhi Cao, Yuecong Xu, Jianfei Yang, Pengyu Yin, Xingyu Ji, Shenghai Yuan, Lihua Xie
Multi-modal test-time adaptation (MM-TTA) is proposed to adapt models to an unlabeled target domain by leveraging the complementary multi-modal inputs in an online manner.
1 code implementation • 4 Mar 2024 • Cunyi Yin, Xiren Miao, Jing Chen, Hao Jiang, Jianfei Yang, Yunjiao Zhou, Min Wu, Zhenghua Chen
WiFi-based human pose estimation is a suitable method for monitoring power operations due to its low cost, device-free, and robustness to various illumination conditions. In this paper, a novel Channel State Information (CSI)-based pose estimation framework, namely PowerSkel, is developed to address these challenges.
no code implementations • 29 Feb 2024 • Jianfei Yang, Shijie Tang, Yuecong Xu, Yunjiao Zhou, Lihua Xie
Benefiting from our unsupervised learning procedure, the network requires only a small amount of annotated data for finetuning and can adapt to the new environment with better performance.
no code implementations • 29 Feb 2024 • Zhiyuan Yang, Yunjiao Zhou, Lihua Xie, Jianfei Yang
We find that the tiny model after network augmentation is much easier for a teacher to distill.
1 code implementation • 6 Feb 2024 • Shenghai Yuan, Yizhuo Yang, Thien Hoang Nguyen, Thien-Minh Nguyen, Jianfei Yang, Fen Liu, Jianping Li, Han Wang, Lihua Xie
In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which possess the potential to transport harmful payloads or independently cause damage, we introduce MMAUD: a comprehensive Multi-Modal Anti-UAV Dataset.
no code implementations • 17 Nov 2023 • Yucheng Wang, Yuecong Xu, Jianfei Yang, Min Wu, XiaoLi Li, Lihua Xie, Zhenghua Chen
In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA, aiming to reduce domain discrepancy at both the local and global sensor levels.
no code implementations • 14 Nov 2023 • Yunjiao Zhou, Jianfei Yang, Han Zou, Lihua Xie
Through the IoT-language contrastive learning, we derive a unified semantic feature space that aligns multi-modal features with language embeddings, so that the IoT data corresponds to specific words that describe the IoT data.
no code implementations • 29 Sep 2023 • Yunjiao Zhou, Jianfei Yang, He Huang, Lihua Xie
The results demonstrate the effectiveness and robustness of AdaPose in eliminating domain shift, thereby facilitating the widespread application of WiFi-based pose estimation in smart cities.
1 code implementation • 21 Sep 2023 • Haozhi Cao, Yuecong Xu, Jianfei Yang, Pengyu Yin, Shenghai Yuan, Lihua Xie
In this work, we propose Multi-modal Prior Aided (MoPA) domain adaptation to improve the performance of rare objects.
1 code implementation • 11 Sep 2023 • Yucheng Wang, Yuecong Xu, Jianfei Yang, Min Wu, XiaoLi Li, Lihua Xie, Zhenghua Chen
For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances, enabling us to fully model the ST dependencies by considering the correlations between DEDT.
1 code implementation • 11 Sep 2023 • Yucheng Wang, Yuecong Xu, Jianfei Yang, Min Wu, XiaoLi Li, Lihua Xie, Zhenghua Chen
As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data.
no code implementations • 30 May 2023 • Jianfei Yang, Hanjie Qian, Yuecong Xu, Kai Wang, Lihua Xie
Unsupervised domain adaptation (UDA) involves adapting a model trained on a label-rich source domain to an unlabeled target domain.
1 code implementation • NeurIPS 2023 • Jianfei Yang, He Huang, Yunjiao Zhou, Xinyan Chen, Yuecong Xu, Shenghai Yuan, Han Zou, Chris Xiaoxuan Lu, Lihua Xie
Extensive experiments have been conducted to compare the sensing capacity of each or several modalities in terms of multiple tasks.
no code implementations • 18 Mar 2023 • Xiyu Wang, Yuecong Xu, Jianfei Yang, Bihan Wen, Alex C. Kot
The second module compares the outputs of augmented data from the current model to the outputs of weakly augmented data from the source model, forming a novel consistency regularization on the model to alleviate the accumulation of prediction errors.
no code implementations • ICCV 2023 • Yuecong Xu, Jianfei Yang, Yunjiao Zhou, Zhenghua Chen, Min Wu, XiaoLi Li
We thus consider a more realistic \textit{Few-Shot Video-based Domain Adaptation} (FSVDA) scenario where we adapt video models with only a few target video samples.
no code implementations • ICCV 2023 • Haozhi Cao, Yuecong Xu, Jianfei Yang, Pengyu Yin, Shenghai Yuan, Lihua Xie
In this paper, we explore Multi-Modal Continual Test-Time Adaptation (MM-CTTA) as a new extension of CTTA for 3D semantic segmentation.
no code implementations • 17 Nov 2022 • Yuecong Xu, Haozhi Cao, Zhenghua Chen, XiaoLi Li, Lihua Xie, Jianfei Yang
To tackle performance degradation and address concerns in high video annotation cost uniformly, the video unsupervised domain adaptation (VUDA) is introduced to adapt video models from the labeled source domain to the unlabeled target domain by alleviating video domain shift, improving the generalizability and portability of video models.
no code implementations • 21 Sep 2022 • Dazhuo Wang, Jianfei Yang, Wei Cui, Lihua Xie, Sumei Sun
The AirFi is a novel domain generalization framework that learns the critical part of CSI regardless of different environments and generalizes the model to unseen scenarios, which does not require collecting any data for adaptation to the new environment.
1 code implementation • 30 Aug 2022 • Lang Deng, Jianfei Yang, Shenghai Yuan, Han Zou, Chris Xiaoxuan Lu, Lihua Xie
As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications.
no code implementations • 22 Aug 2022 • Jianfei Yang, Yunjiao Zhou, He Huang, Han Zou, Lihua Xie
Avatar refers to a representative of a physical user in the virtual world that can engage in different activities and interact with other objects in metaverse.
no code implementations • 10 Aug 2022 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Min Wu, XiaoLi Li, Lihua Xie, Zhenghua Chen
To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models.
2 code implementations • 16 Jul 2022 • Jianfei Yang, Xinyan Chen, Dazhuo Wang, Han Zou, Chris Xiaoxuan Lu, Sumei Sun, Lihua Xie
WiFi sensing has been evolving rapidly in recent years.
1 code implementation • 28 May 2022 • Jianfei Yang, Xiangyu Peng, Kai Wang, Zheng Zhu, Jiashi Feng, Lihua Xie, Yang You
Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain.
1 code implementation • 30 Apr 2022 • Kai Wang, Xiangyu Peng, Shuo Yang, Jianfei Yang, Zheng Zhu, Xinchao Wang, Yang You
This paradigm, however, is prone to significant degeneration under heavy label noise, as the number of clean samples is too small for conventional methods to behave well.
no code implementations • 13 Apr 2022 • Xiyu Wang, Yuecong Xu, Kezhi Mao, Jianfei Yang
It utilizes a novel class weight calibration method to alleviate the negative transfer caused by incorrect class weights.
1 code implementation • 12 Apr 2022 • Jianfei Yang, Xinyan Chen, Han Zou, Dazhuo Wang, Lihua Xie
The AutoFi transfers knowledge from randomly collected CSI samples into human gait recognition and achieves state-of-the-art performance.
no code implementations • 8 Apr 2022 • Jianfei Yang, Xinyan Chen, Han Zou, Dazhuo Wang, Qianwen Xu, Lihua Xie
WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access.
no code implementations • 4 Apr 2022 • Jianfei Yang, Han Zou, Lihua Xie
The results validate that our method works well on wireless human activity recognition and person identification systems.
no code implementations • 11 Mar 2022 • Jin Hao, Jiaxiang Liu, Jin Li, Wei Pan, Ruizhe Chen, Huimin Xiong, Kaiwei Sun, Hangzheng Lin, Wanlu Liu, Wanghui Ding, Jianfei Yang, Haoji Hu, Yueling Zhang, Yang Feng, Zeyu Zhao, Huikai Wu, Youyi Zheng, Bing Fang, Zuozhu Liu, Zhihe Zhao
Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental model), and a fusion model to generate 3D fused crown-root-bone structures with high fidelity and accurate occlusal and dentition information.
1 code implementation • 9 Mar 2022 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Keyu Wu, Wu Min, Zhenghua Chen
Video-based Unsupervised Domain Adaptation (VUDA) methods improve the robustness of video models, enabling them to be applied to action recognition tasks across different environments.
no code implementations • 19 Feb 2022 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Jianxiong Yin, Zhenghua Chen, XiaoLi Li, Zhengguo Li, Qianwen Xu
While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to challenging environments in real-world scenarios are still under-explored.
no code implementations • 28 Jan 2022 • Changwei Xu, Jianfei Yang, Haoran Tang, Han Zou, Cheng Lu, Tianshuo Zhang
Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with the help of adversarially trained models.
no code implementations • 17 Jan 2022 • Tianyi Xie, Liucheng Liao, Cheng Bi, Benlai Tang, Xiang Yin, Jianfei Yang, Mingjie Wang, Jiali Yao, Yang Zhang, Zejun Ma
The task of few-shot visual dubbing focuses on synchronizing the lip movements with arbitrary speech input for any talking head video.
no code implementations • CVPR 2022 • Minghui Hu, Yujie Wang, Tat-Jen Cham, Jianfei Yang, P. N. Suganthan
We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context, which compensates for the deficiency of the classical autoregressive model along pixel space.
no code implementations • 26 Sep 2021 • Haozhi Cao, Yuecong Xu, Jianfei Yang, Kezhi Mao, Lihua Xie, Jianxiong Yin, Simon See
This paper introduces a novel self-supervised method that leverages incoherence detection for video representation learning.
no code implementations • 21 Sep 2021 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Keyu Wu, Min Wu, Rui Zhao, Zhenghua Chen
Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios.
no code implementations • ICCV 2021 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Qi Li, Kezhi Mao, Zhenghua Chen
For videos, such negative transfer could be triggered by both spatial and temporal features, which leads to a more challenging Partial Video Domain Adaptation (PVDA) problem.
no code implementations • 11 Jul 2021 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Kezhi Mao, Jianxiong Yin, Simon See
Yet correlation features of the same action would differ across domains due to domain shift.
1 code implementation • ECCV 2020 • Xiaojiang Peng, Kai Wang, Zhaoyang Zeng, Qing Li, Jianfei Yang, Yu Qiao
Specifically, this plug-and-play AFM first leverages a \textit{group-to-attend} module to construct groups and assign attention weights for group-wise samples, and then uses a \textit{mixup} module with the attention weights to interpolate massive noisy-suppressed samples.
no code implementations • 26 Aug 2020 • Haozhi Cao, Yuecong Xu, Jianfei Yang, Kezhi Mao, Jianxiong Yin, Simon See
Temporal feature extraction is an essential technique in video-based action recognition.
no code implementations • 9 Jun 2020 • Yuecong Xu, Haozhi Cao, Jianfei Yang, Kezhi Mao, Jianxiong Yin, Simon See
Empirical results prove the effectiveness and efficiency of our PNL module, which achieves state-of-the-art performance of 83. 09% on the Mini-Kinetics dataset, with decreased computation cost compared to the non-local block.
1 code implementation • 6 Jun 2020 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Kezhi Mao, Jianxiong Yin, Simon See
We bridge the gap of the lack of data for this task by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset.
no code implementations • 6 May 2020 • Yuecong Xu, Jianfei Yang, Kezhi Mao, Jianxiong Yin, Simon See
Temporal feature extraction is an important issue in video-based action recognition.
no code implementations • ICLR 2020 • Jianfei Yang, Han Zou, Yuxun Zhou, Lihua Xie
The proposed MDAT stabilizes the gradient reversing in ARN by replacing the domain classifier with a reconstruction network, and in this manner ARN conducts both feature-level and pixel-level domain alignment without involving extra network structures.
2 code implementations • CVPR 2020 • Kai Wang, Xiaojiang Peng, Jianfei Yang, Shijian Lu, Yu Qiao
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 8 Jul 2019 • Kai Wang, Jianfei Yang, Da Guo, Kaipeng Zhang, Xiaojiang Peng, Yu Qiao
Based on our winner solution last year, we mainly explore head features and body features with a bootstrap strategy and two novel loss functions in this paper.
1 code implementation • 10 May 2019 • Kai Wang, Xiaojiang Peng, Jianfei Yang, Debin Meng, Yu Qiao
Extensive experiments show that our RAN and region biased loss largely improve the performance of FER with occlusion and variant pose.
Ranked #2 on Facial Expression Recognition (FER) on SFEW
Facial Expression Recognition Facial Expression Recognition (FER)
6 code implementations • CVPR 2019 • Chen Wang, Jianfei Yang, Lihua Xie, Junsong Yuan
Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in many computer vision tasks.