no code implementations • NAACL 2022 • Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen
Large-scale pre-trained language models have attracted extensive attentions in the research community and shown promising results on various tasks of natural language processing.
no code implementations • 18 Apr 2024 • Zunran Wang, Zhonghua Li, Wei Shen, Qi Ye, Liqiang Nie
To effectively enrich the feature context representations of term weight, the Feature Context Module (FCM) is introduced, which leverages the power of BERT's representation to determine dynamic weights for each element in the embedding.
no code implementations • 18 Apr 2024 • Chongjie Si, Xuehui Wang, Xiaokang Yang, Wei Shen
However, a scenario usually arises where a pixel is concurrently predicted as an old class by the pre-trained segmentation model and a new class by the seed areas.
no code implementations • 22 Mar 2024 • Kailing Wang, Chen Yang, Yuehao Wang, Sikuang Li, Yan Wang, Qi Dou, Xiaokang Yang, Wei Shen
Precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time online visualization are critical for intrabody medical imaging devices such as endoscopes and capsule robots.
no code implementations • 12 Mar 2024 • Wei Shen, Xiaoying Zhang, Yuanshun Yao, Rui Zheng, Hongyi Guo, Yang Liu
Reinforcement learning from human feedback (RLHF) is the mainstream paradigm used to align large language models (LLMs) with human preferences.
no code implementations • 8 Mar 2024 • Xiaoying Zhang, Jean-Francois Ton, Wei Shen, Hongning Wang, Yang Liu
We introduce Adversarial Policy Optimization (AdvPO), a novel solution to the pervasive issue of reward over-optimization in Reinforcement Learning from Human Feedback (RLHF) for Large Language Models (LLMs).
1 code implementation • 15 Feb 2024 • Chen Yang, Sikuang Li, Jiemin Fang, Ruofan Liang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian
Then we construct a Gaussian repair model based on diffusion models to supplement the omitted object information, where Gaussians are further refined.
1 code implementation • 8 Feb 2024 • Zhiheng Xi, Wenxiang Chen, Boyang Hong, Senjie Jin, Rui Zheng, wei he, Yiwen Ding, Shichun Liu, Xin Guo, Junzhe Wang, Honglin Guo, Wei Shen, Xiaoran Fan, Yuhao Zhou, Shihan Dou, Xiao Wang, Xinbo Zhang, Peng Sun, Tao Gui, Qi Zhang, Xuanjing Huang
In this paper, we propose R$^3$: Learning Reasoning through Reverse Curriculum Reinforcement Learning (RL), a novel method that employs only outcome supervision to achieve the benefits of process supervision for large language models.
1 code implementation • 2 Feb 2024 • Shihan Dou, Yan Liu, Haoxiang Jia, Limao Xiong, Enyu Zhou, Wei Shen, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Xuanjing Huang, Tao Gui
The advancement of large language models (LLMs) has significantly propelled the field of code generation.
no code implementations • 30 Jan 2024 • Danning Lao, Qi Liu, Jiazi Bu, Junchi Yan, Wei Shen
As computer vision continues to advance and finds widespread applications across various domains, the need for interpretability in deep learning models becomes paramount.
1 code implementation • 21 Jan 2024 • Songyang Gao, Qiming Ge, Wei Shen, Shihan Dou, Junjie Ye, Xiao Wang, Rui Zheng, Yicheng Zou, Zhi Chen, Hang Yan, Qi Zhang, Dahua Lin
This reliance limits the applicability of RLHF and hinders the development of professional assistants tailored to diverse human preferences.
1 code implementation • 11 Jan 2024 • Binghai Wang, Rui Zheng, Lu Chen, Yan Liu, Shihan Dou, Caishuang Huang, Wei Shen, Senjie Jin, Enyu Zhou, Chenyu Shi, Songyang Gao, Nuo Xu, Yuhao Zhou, Xiaoran Fan, Zhiheng Xi, Jun Zhao, Xiao Wang, Tao Ji, Hang Yan, Lixing Shen, Zhan Chen, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data.
no code implementations • 6 Jan 2024 • Hongyi Guo, Yuanshun Yao, Wei Shen, Jiaheng Wei, Xiaoying Zhang, Zhaoran Wang, Yang Liu
The key idea is to first retrieve high-quality samples related to the target domain and use them as In-context Learning examples to generate more samples.
2 code implementations • 23 Dec 2023 • Chen Yang, Kailing Wang, Yuehao Wang, Qi Dou, Xiaokang Yang, Wei Shen
Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal for advanced surgical systems.
no code implementations • 18 Dec 2023 • Chongjie Si, Xuehui Wang, Yan Wang, Xiaokang Yang, Wei Shen
In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth.
1 code implementation • 15 Dec 2023 • Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Jun Zhao, Wei Shen, Yuhao Zhou, Zhiheng Xi, Xiao Wang, Xiaoran Fan, ShiLiang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang
Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks.
no code implementations • 8 Dec 2023 • Tongkun Guan, Wei Shen, Xue Yang, Xuehui Wang, Xiaokang Yang
Existing scene text detection methods typically rely on extensive real data for training.
no code implementations • 1 Dec 2023 • Jiazhong Cen, Jiemin Fang, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian
Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation.
no code implementations • 28 Nov 2023 • Zelin Peng, Zhengqin Xu, Zhilin Zeng, Lingxi Xie, Qi Tian, Wei Shen
Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data.
no code implementations • 14 Nov 2023 • Yuhan Li, Jian Wu, Zhiwei Yu, Börje F. Karlsson, Wei Shen, Manabu Okumura, Chin-Yew Lin
To close this gap in data availability and enable cross-modality IE, while alleviating labeling costs, we propose a semi-supervised pipeline for annotating entities in text, as well as entities and relations in tables, in an iterative procedure.
no code implementations • 2 Nov 2023 • Wei Shen, Minhui Huang, Jiawei Zhang, Cong Shen
In recent years, federated minimax optimization has attracted growing interest due to its extensive applications in various machine learning tasks.
no code implementations • 18 Oct 2023 • Rui Zheng, Wei Shen, Yuan Hua, Wenbin Lai, Shihan Dou, Yuhao Zhou, Zhiheng Xi, Xiao Wang, Haoran Huang, Tao Gui, Qi Zhang, Xuanjing Huang
In this work, we propose a novel approach that can learn a consistent policy via RL across various data groups or domains.
no code implementations • 8 Oct 2023 • Wei Shen, Rui Zheng, WenYu Zhan, Jun Zhao, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values.
2 code implementations • 12 Sep 2023 • Anthony Cioppa, Silvio Giancola, Vladimir Somers, Floriane Magera, Xin Zhou, Hassan Mkhallati, Adrien Deliège, Jan Held, Carlos Hinojosa, Amir M. Mansourian, Pierre Miralles, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdullah Kamal, Adrien Maglo, Albert Clapés, Amr Abdelaziz, Artur Xarles, Astrid Orcesi, Atom Scott, Bin Liu, Byoungkwon Lim, Chen Chen, Fabian Deuser, Feng Yan, Fufu Yu, Gal Shitrit, Guanshuo Wang, Gyusik Choi, Hankyul Kim, Hao Guo, Hasby Fahrudin, Hidenari Koguchi, Håkan Ardö, Ibrahim Salah, Ido Yerushalmy, Iftikar Muhammad, Ikuma Uchida, Ishay Be'ery, Jaonary Rabarisoa, Jeongae Lee, Jiajun Fu, Jianqin Yin, Jinghang Xu, Jongho Nang, Julien Denize, Junjie Li, Junpei Zhang, Juntae Kim, Kamil Synowiec, Kenji Kobayashi, Kexin Zhang, Konrad Habel, Kota Nakajima, Licheng Jiao, Lin Ma, Lizhi Wang, Luping Wang, Menglong Li, Mengying Zhou, Mohamed Nasr, Mohamed Abdelwahed, Mykola Liashuha, Nikolay Falaleev, Norbert Oswald, Qiong Jia, Quoc-Cuong Pham, Ran Song, Romain Hérault, Rui Peng, Ruilong Chen, Ruixuan Liu, Ruslan Baikulov, Ryuto Fukushima, Sergio Escalera, Seungcheon Lee, Shimin Chen, Shouhong Ding, Taiga Someya, Thomas B. Moeslund, Tianjiao Li, Wei Shen, Wei zhang, Wei Li, Wei Dai, Weixin Luo, Wending Zhao, Wenjie Zhang, Xinquan Yang, Yanbiao Ma, Yeeun Joo, Yingsen Zeng, Yiyang Gan, Yongqiang Zhu, Yujie Zhong, Zheng Ruan, Zhiheng Li, Zhijian Huang, Ziyu Meng
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
no code implementations • 28 Aug 2023 • Zelin Peng, Zhengqin Xu, Zhilin Zeng, Xiaokang Yang, Wei Shen
Most existing fine-tuning methods attempt to bridge the gaps among different scenarios by introducing a set of new parameters to modify SAM's original parameter space.
no code implementations • 26 Aug 2023 • Danyang Tu, Wei Shen, Wei Sun, Xiongkuo Min, Guangtao Zhai
In contrast, we reframe the gaze following detection task as detecting human head locations and their gaze followings simultaneously, aiming at jointly detect human gaze location and gaze object in a unified and single-stage pipeline.
no code implementations • ICCV 2023 • Danyang Tu, Wei Sun, Guangtao Zhai, Wei Shen
We propose an agglomerative Transformer (AGER) that enables Transformer-based human-object interaction (HOI) detectors to flexibly exploit extra instance-level cues in a single-stage and end-to-end manner for the first time.
1 code implementation • 11 Jul 2023 • Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang
Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model.
no code implementations • 18 Jun 2023 • Hangjian Li, Dong Xu, Konstantin Shmakov, Kuang-Chih Lee, Wei Shen
Online retailers often use third-party demand-side-platforms (DSPs) to conduct offsite advertising and reach shoppers across the Internet on behalf of their advertisers.
2 code implementations • 31 May 2023 • Chen Yang, Kailing Wang, Yuehao Wang, Xiaokang Yang, Wei Shen
Reconstructing deformable tissues from endoscopic stereo videos in robotic surgery is crucial for various clinical applications.
2 code implementations • CVPR 2023 • Yunhao Bai, Duowen Chen, Qingli Li, Wei Shen, Yan Wang
In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution.
Image Segmentation Semi-supervised Medical Image Segmentation +1
1 code implementation • NeurIPS 2023 • Jiazhong Cen, Jiemin Fang, Zanwei Zhou, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian
The Segment Anything Model (SAM) emerges as a powerful vision foundation model to generate high-quality 2D segmentation results.
no code implementations • CVPR 2023 • Chen Yang, Peihao Li, Zanwei Zhou, Shanxin Yuan, Bingbing Liu, Xiaokang Yang, Weichao Qiu, Wei Shen
We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room.
1 code implementation • 30 Mar 2023 • Huiyu Duan, Wei Shen, Xiongkuo Min, Danyang Tu, Long Teng, Jia Wang, Guangtao Zhai
Recently, masked autoencoders (MAE) for feature pre-training have further unleashed the potential of Transformers, leading to state-of-the-art performances on various high-level vision tasks.
Ranked #4 on Image Defocus Deblurring on DPD (Dual-view)
no code implementations • ICCV 2023 • Zelin Peng, Guanchun Wang, Lingxi Xie, Dongsheng Jiang, Wei Shen, Qi Tian
Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS).
Multi-Label Classification Weakly supervised Semantic Segmentation +1
no code implementations • 3 Mar 2023 • Yuanying Cai, Chuheng Zhang, Wei Shen, Xuyun Zhang, Wenjie Ruan, Longbo Huang
Inspired by the recent success of sequence modeling in RL and the use of masked language model for pre-training, we propose a masked model for pre-training in RL, RePreM (Representation Pre-training with Masked Model), which trains the encoder combined with transformer blocks to predict the masked states or actions in a trajectory.
no code implementations • 9 Jan 2023 • Yang Peng, Changzheng Liu, Wei Shen
Customer-centric marketing campaigns generate a large portion of e-commerce website traffic for Walmart.
1 code implementation • CVPR 2023 • Duowen Chen, Yunhao Bai, Wei Shen, Qingli Li, Lequan Yu, Yan Wang
Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch).
no code implementations • 6 Dec 2022 • Zanwei Zhou, RuiZhe Zhong, Chen Yang, Yan Wang, Xiaokang Yang, Wei Shen
In this study, we point out that the current tokenization strategy in MTSF Transformer architectures ignores the token uniformity inductive bias of Transformers.
no code implementations • 5 Dec 2022 • Wei Shen, Xiaonan He, Chuheng Zhang, Xuyun Zhang, Jian Xie
Moreover, they are trained and evaluated on the benchmark datasets with adequate labels, which are expensive to obtain in a commercial dialogue system.
1 code implementation • 5 Dec 2022 • Yuanying Cai, Chuheng Zhang, Li Zhao, Wei Shen, Xuyun Zhang, Lei Song, Jiang Bian, Tao Qin, TieYan Liu
There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different states due to the variation of the action coverage induced by different behavior policies.
no code implementations • Proceedings of the 2021 International Conference on Management of Data 2021 • Yinan Liu, Wei Shen, Yuanfei Wang, Jianyong Wang, Zhenglu Yang, Xiaojie Yuan
However, noun phrases (NPs) and relation phrases (RPs) in OKBs are not canonicalized and often appear in different paraphrased textual variants, which leads to redundant and ambiguous facts.
no code implementations • 28 Nov 2022 • Yinan Liu, Hu Chen, Wei Shen, Jiaoyan Chen
Previous studies often rely on a relative number of resources such as labeled utterances and external data, yet the attribute knowledge embedded in unlabeled utterances is underutilized and their performance of predicting some difficult personal attributes is still unsatisfactory.
1 code implementation • ICCV 2023 • Tongkun Guan, Wei Shen, Xue Yang, Qi Feng, Zekun Jiang, Xiaokang Yang
Therefore, exploring the robust text feature representations on unlabeled real images by self-supervised learning is a good solution.
1 code implementation • 9 Oct 2022 • Yunhao Li, Zhenbo Yu, Yucheng Zhu, Bingbing Ni, Guangtao Zhai, Wei Shen
Stage I introduces a test time adaptation strategy, which improves the physical plausibility of synthesized human skeleton motions by optimizing skeleton joint locations.
no code implementations • 22 Sep 2022 • Cheng Jie, Da Xu, Zigeng Wang, Wei Shen
Organic search comprises a large portion of the total traffic for e-commerce companies.
no code implementations • 12 Sep 2022 • Xue Li, Wei Shen, Denis Charles
In this paper, we propose TEDL, a two-stage learning approach to quantify uncertainty for deep learning models in classification tasks, inspired by our findings in experimenting with Evidential Deep Learning (EDL) method, a recently proposed uncertainty quantification approach based on the Dempster-Shafer theory.
no code implementations • 30 Aug 2022 • Li Lyna Zhang, Youkow Homma, Yujing Wang, Min Wu, Mao Yang, Ruofei Zhang, Ting Cao, Wei Shen
Remarkably, under our latency requirement of 1900us on CPU, SwiftPruner achieves a 0. 86% higher AUC than the state-of-the-art uniform sparse baseline for BERT-Mini on a large scale real-world dataset.
1 code implementation • 29 Aug 2022 • Yinan Liu, Hu Chen, Wei Shen
Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation.
1 code implementation • 8 Aug 2022 • Chenwei Ran, Wei Shen, Jianbo Gao, Yuhan Li, Jianyong Wang, Yantao Jia
Entity linking (EL) is the process of linking entity mentions appearing in text with their corresponding entities in a knowledge base.
1 code implementation • 6 Jul 2022 • Yuan YAO, Fengze Liu, Zongwei Zhou, Yan Wang, Wei Shen, Alan Yuille, Yongyi Lu
Previous methods proposed Variational Autoencoder (VAE) based models to learn the distribution of shape for a particular organ and used it to automatically evaluate the quality of a segmentation prediction by fitting it into the learned shape distribution.
no code implementations • 4 Jul 2022 • Wei Shen, Zelin Peng, Xuehui Wang, Huayu Wang, Jiazhong Cen, Dongsheng Jiang, Lingxi Xie, Xiaokang Yang, Qi Tian
Next, we summarize the existing label-efficient image segmentation methods from a unified perspective that discusses an important question: how to bridge the gap between weak supervision and dense prediction -- the current methods are mostly based on heuristic priors, such as cross-pixel similarity, cross-label constraint, cross-view consistency, and cross-image relation.
2 code implementations • 22 Jun 2022 • Wei Shen, Yang Yang, Yinan Liu
In this paper, we propose CMVC, a novel unsupervised framework that leverages these two views of knowledge jointly for canonicalizing OKBs without the need of manually annotated labels.
no code implementations • 4 Jun 2022 • Danyang Tu, Wei Sun, Xiongkuo Min, Guangtao Zhai, Wei Shen
We present a novel vision Transformer, named TUTOR, which is able to learn tubelet tokens, served as highly-abstracted spatiotemporal representations, for video-based human-object interaction (V-HOI) detection.
2 code implementations • 24 May 2022 • Yuhan Li, Wei Shen, Jianbo Gao, Yadong Wang
Community Question Answering (CQA) platforms contain plenty of CQA texts (i. e., questions and answers corresponding to the question) where named entities appear ubiquitously.
1 code implementation • 21 Apr 2022 • Yuzhi Zhao, Lai-Man Po, Xuehui Wang, Qiong Yan, Wei Shen, Yujia Zhang, Wei Liu, Chun-Kit Wong, Chiu-Sing Pang, Weifeng Ou, Wing-Yin Yu, Buhua Liu
On this basis, we formulate predictions as a mapping from parents' genetic factors to children's genetic factors, and disentangle them from external and variety factors.
Age-Invariant Face Recognition Image-to-Image Translation +2
1 code implementation • 18 Apr 2022 • Huiyu Duan, Wei Shen, Xiongkuo Min, Danyang Tu, Jing Li, Guangtao Zhai
Therefore, in this paper, we mainly analyze the interaction effect between background (BG) scenes and AR contents, and study the saliency prediction problem in AR.
1 code implementation • 22 Mar 2022 • Feng Wang, Huiyu Wang, Chen Wei, Alan Yuille, Wei Shen
Recent advances in self-supervised contrastive learning yield good image-level representation, which favors classification tasks but usually neglects pixel-level detailed information, leading to unsatisfactory transfer performance to dense prediction tasks such as semantic segmentation.
no code implementations • 20 Mar 2022 • Danyang Tu, Xiongkuo Min, Huiyu Duan, Guodong Guo, Guangtao Zhai, Wei Shen
Iwin Transformer is a hierarchical Transformer which progressively performs token representation learning and token agglomeration within irregular windows.
no code implementations • CVPR 2022 • Danyang Tu, Xiongkuo Min, Huiyu Duan, Guodong Guo, Guangtao Zhai, Wei Shen
In contrast, we redefine the HGT detection task as detecting human head locations and their gaze targets, simultaneously.
1 code implementation • CVPR 2022 • Xuehui Wang, Kai Zhao, Ruixin Zhang, Shouhong Ding, Yan Wang, Wei Shen
In this framework, annotated masks of seen categories and pseudo masks of unseen categories serve as a prior for contrastive learning, where features from the mask regions (foreground) are pulled together, and are contrasted against those from the background, and vice versa.
no code implementations • 11 Mar 2022 • Kai Zhao, Lei Shen, Yingyi Zhang, Chuhan Zhou, Tao Wang, Ruixin Zhang, Shouhong Ding, Wei Jia, Wei Shen
In this paper, by observing that palmar creases are the key information to deep-learning-based palmprint recognition, we propose to synthesize training data by manipulating palmar creases.
1 code implementation • CVPR 2023 • Tongkun Guan, Chaochen Gu, Jingzheng Tu, Xue Yang, Qi Feng, Yudi Zhao, Xiaokang Yang, Wei Shen
Supervised attention can alleviate the above issue, but it is character category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when handling languages with larger character categories.
Ranked #2 on Scene Text Recognition on ICDAR 2003
no code implementations • 4 Jan 2022 • Yuyin Zhou, David Dreizin, Yan Wang, Fengze Liu, Wei Shen, Alan L. Yuille
The spleen is one of the most commonly injured solid organs in blunt abdominal trauma.
no code implementations • 24 Nov 2021 • Jiazhong Cen, Zenkun Jiang, Lingxi Xie, Qi Tian, Xiaokang Yang, Wei Shen
Anomaly segmentation is a crucial task for safety-critical applications, such as autonomous driving in urban scenes, where the goal is to detect out-of-distribution (OOD) objects with categories which are unseen during training.
Ranked #10 on Anomaly Detection on Fishyscapes L&F
2 code implementations • 23 Nov 2021 • Xintian Mao, Yiming Liu, Fengze Liu, Qingli Li, Wei Shen, Yan Wang
Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image.
Ranked #2 on Deblurring on RealBlur-R
1 code implementation • 15 Nov 2021 • Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, Tao Kong
We present a self-supervised framework iBOT that can perform masked prediction with an online tokenizer.
Ranked #1 on Unsupervised Image Classification on ImageNet
no code implementations • ICLR 2022 • Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, Tao Kong
The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces.
no code implementations • 26 Sep 2021 • Wei Shen, Yuhan Li, Yinan Liu, Jiawei Han, Jianyong Wang, Xiaojie Yuan
Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base.
2 code implementations • 25 Aug 2021 • Wei Shen, Chuheng Zhang, Yun Tian, Liang Zeng, Xiaonan He, Wanchun Dou, Xiaolong Xu
However, without node content (i. e., side information) for training, the user (or item) specific representation can not be learned in the inductive setting, that is, a model trained on one group of users (or items) cannot adapt to new users (or items).
Ranked #3 on Recommendation Systems on MovieLens 1M
no code implementations • 24 Jun 2021 • Cheng Jie, Da Xu, Zigeng Wang, Lu Wang, Wei Shen
With the increasing scale of search engine marketing, designing an efficient bidding system is becoming paramount for the success of e-commerce companies.
1 code implementation • CVPR 2021 • Yi Fang, Jiapeng Tang, Wang Shen, Wei Shen, Xiao Gu, Li Song, Guangtao Zhai
In the third stage, we use the generated dual attention as guidance to perform two sub-tasks: (1) identifying whether the gaze target is inside or out of the image; (2) locating the target if inside.
1 code implementation • 10 Jun 2021 • Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani, AnnetteKopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Bram van Ginneken, Michel Bilello, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc J. Gollub, Stephan H. Heckers, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Jennifer S. Goli Pernicka, Kawal Rhode, Catalina Tobon-Gomez, Eugene Vorontsov, Henkjan Huisman, James A. Meakin, Sebastien Ourselin, Manuel Wiesenfarth, Pablo Arbelaez, Byeonguk Bae, Sihong Chen, Laura Daza, Jianjiang Feng, Baochun He, Fabian Isensee, Yuanfeng Ji, Fucang Jia, Namkug Kim, Ildoo Kim, Dorit Merhof, Akshay Pai, Beomhee Park, Mathias Perslev, Ramin Rezaiifar, Oliver Rippel, Ignacio Sarasua, Wei Shen, Jaemin Son, Christian Wachinger, Liansheng Wang, Yan Wang, Yingda Xia, Daguang Xu, Zhanwei Xu, Yefeng Zheng, Amber L. Simpson, Lena Maier-Hein, M. Jorge Cardoso
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
no code implementations • 5 Jun 2021 • Yilin Wang, Shaozuo Yu, Xiaokang Yang, Wei Shen
In this paper, we propose a generic model transfer scheme to make Convlutional Neural Networks (CNNs) interpretable, while maintaining their high classification accuracy.
1 code implementation • NeurIPS 2021 • Qihang Yu, Yingda Xia, Yutong Bai, Yongyi Lu, Alan Yuille, Wei Shen
It is motivated by the Glance and Gaze behavior of human beings when recognizing objects in natural scenes, with the ability to efficiently model both long-range dependencies and local context.
no code implementations • 31 May 2021 • Yan Wang, Peng Tang, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille
We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier.
1 code implementation • IEEE Transactions on Knowledge and Data Engineering 2021 • Wei Shen, Yuwei Yin, Yang Yang, Jiawei Han, Jianyong Wang, Xiaojie Yuan
The task of linking an entity mention in a tweet with its corresponding entity in a heterogeneous information network is of great importance, for the purpose of enriching heterogeneous information networks with the abundant and fresh knowledge embedded in tweets.
1 code implementation • 5 Mar 2021 • Boxiang Yun, Yan Wang, Jieneng Chen, Huiyu Wang, Wei Shen, Qingli Li
Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relied on high-precision pathology image segmentation, such as computational pathology and precision medicine.
1 code implementation • ICCV 2021 • Yunhao Li, Wei Shen, Zhongpai Gao, Yucheng Zhu, Guangtao Zhai, Guodong Guo
Specifically, the local region is obtained as a 2D cone-shaped field along the 2D projection of the sight line starting at the human subject's head position, and the distant region is obtained by searching along the sight line in 3D sphere space.
1 code implementation • 28 Nov 2020 • Yuhui Xu, Lingxi Xie, Cihang Xie, Jieru Mei, Siyuan Qiao, Wei Shen, Hongkai Xiong, Alan Yuille
Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data distributions.
no code implementations • 29 Oct 2020 • Yingwei Li, Zhuotun Zhu, Yuyin Zhou, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille
Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources.
no code implementations • 14 Oct 2020 • Yiren Chen, Yaming Yang, Hong Sun, Yujing Wang, Yu Xu, Wei Shen, Rong Zhou, Yunhai Tong, Jing Bai, Ruofei Zhang
We add the model designed by AutoADR as a sub-model into the production Ad Relevance model.
1 code implementation • ICLR 2021 • Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie
To prevent models from exclusively attending on a single cue in representation learning, we augment training data with images with conflicting shape and texture information (eg, an image of chimpanzee shape but with lemon texture) and, most importantly, provide the corresponding supervisions from shape and texture simultaneously.
Ranked #601 on Image Classification on ImageNet
no code implementations • ICLR 2021 • Chen Wei, Huiyu Wang, Wei Shen, Alan Yuille
Regarding the similarity of the query crop to each crop from other images as "unlabeled", the consistency term takes the corresponding similarity of a positive crop as a pseudo label, and encourages consistency between these two similarities.
no code implementations • 25 Aug 2020 • Wei Shen, Xiaonan He, Chuheng Zhang, Qiang Ni, Wanchun Dou, Yan Wang
Therefore, it is crucial to design a participant selection algorithm that applies to different MCS systems to achieve multiple goals.
no code implementations • 9 Jul 2020 • Daniil Pakhomov, Wei Shen, Nassir Navab
Surgical tool segmentation in endoscopic images is an important problem: it is a crucial step towards full instrument pose estimation and it is used for integration of pre- and intra-operative images into the endoscopic view.
no code implementations • 21 May 2020 • R. Daniel Meyer, Bohdana Ratitch, Marcel Wolbers, Olga Marchenko, Hui Quan, Daniel Li, Chrissie Fletcher, Xin Li, David Wright, Yue Shentu, Stefan Englert, Wei Shen, Jyotirmoy Dey, Thomas Liu, Ming Zhou, Norman Bohidar, Peng-Liang Zhao, Michael Hale
The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials.
no code implementations • 18 May 2020 • Shuhao Fu, Yongyi Lu, Yan Wang, Yuyin Zhou, Wei Shen, Elliot Fishman, Alan Yuille
In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains).
no code implementations • 4 Apr 2020 • Zhuotun Zhu, Yongyi Lu, Wei Shen, Elliot K. Fishman, Alan L. Yuille
This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans.
1 code implementation • ECCV 2020 • Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, Alan Yuille
The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of semantic segmentation, such as autonomous driving and medical image analysis.
Ranked #8 on Anomaly Detection on Road Anomaly (using extra training data)
no code implementations • 18 Mar 2020 • Yingda Xia, Qihang Yu, Wei Shen, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population.
no code implementations • CVPR 2020 • Yan Wang, Xu Wei, Fengze Liu, Jieneng Chen, Yuyin Zhou, Wei Shen, Elliot K. Fishman, Alan L. Yuille
Tubular structure segmentation in medical images, e. g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases.
1 code implementation • CVPR 2021 • Hao Ding, Siyuan Qiao, Alan Yuille, Wei Shen
The key to a successful cascade architecture for precise instance segmentation is to fully leverage the relationship between bounding box detection and mask segmentation across multiple stages.
1 code implementation • 21 Nov 2019 • Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, Alan Yuille
To address this issue, we propose BatchChannel Normalization (BCN), which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models.
no code implementations • 9 Sep 2019 • Mingqing Xiao, Adam Kortylewski, Ruihai Wu, Siyuan Qiao, Wei Shen, Alan Yuille
Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency between training and testing data.
no code implementations • 23 Jul 2019 • Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo wang, Alan Yuille
Both of them connect split nodes to the top layer of convolutional neural networks (CNNs) and deal with inhomogeneous data by jointly learning input-dependent data partitions at the split nodes and age distributions at the leaf nodes.
no code implementations • 30 Jun 2019 • Wei Shen, Fei Li, Rujie Liu
We argue that the discard of the correlated discriminative information is partially caused by the fact that the minimization of the classification loss doesn't ensure to learn the overall discriminative information but only the most discriminative information.
no code implementations • 25 Apr 2019 • Wei Shen, Zhenhuan Yang, Yiming Ying, Xiaoming Yuan
From this fundamental trade-off, we obtain lower bounds for the optimization error of SGD algorithms and the excess expected risk over a class of pairwise losses.
no code implementations • 25 Mar 2019 • Wei Shen, Ziqiang Shi, Jun Sun
Then we use the adversarial region attention to aggregate the feature maps to obtain the adversarial features.
8 code implementations • 25 Mar 2019 • Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, Alan Yuille
Batch Normalization (BN) has become an out-of-box technique to improve deep network training.
Ranked #76 on Instance Segmentation on COCO minival
no code implementations • ICLR 2018 • Wei Shen, Rujie Liu
In this paper, we propose to generate sample-specific filters for convolutional layers in the forward pass.
1 code implementation • 28 Nov 2018 • Zhishuai Zhang, Wei Shen, Siyuan Qiao, Yan Wang, Bo wang, Alan Yuille
In this paper, we propose that the robustness of a face detector against hard faces can be improved by learning small faces on hard images.
Ranked #8 on Face Detection on WIDER Face (Hard)
no code implementations • 27 Nov 2018 • Wei Shen, Rujie Liu
Recent advances in fine-grained recognition utilize attention maps to localize objects of interest.
no code implementations • 27 Nov 2018 • Wei Shen, Rujie Liu
However, we find that choosing squared Euclidean distance may cause distance explosion leading gradients to be extremely sparse in the early stage of back propagation.
4 code implementations • 9 Jul 2018 • Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, Alan Yuille
The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one.
Ranked #1 on Weakly Supervised Object Detection on ImageNet
no code implementations • 1 Jul 2018 • Kai Zhao, Wei Shen, ShangHua Gao, Dandan Li, Ming-Ming Cheng
In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts.
no code implementations • 16 May 2018 • Yunhan Zhao, Ye Tian, Charless Fowlkes, Wei Shen, Alan Yuille
Experimental results verify that our approach significantly improves the ability of deep networks to resist large variations between training and testing data and achieves classification accuracy improvements on several benchmark datasets, including MNIST, affNIST, SVHN, CIFAR-10 and miniImageNet.
no code implementations • 23 Apr 2018 • Yan Wang, Yuyin Zhou, Wei Shen, Seyoun Park, Elliot K. Fishman, Alan L. Yuille
To address these challenges, we introduce a novel framework for multi-organ segmentation by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity.
no code implementations • 7 Apr 2018 • Yan Wang, Yuyin Zhou, Peng Tang, Wei Shen, Elliot K. Fishman, Alan L. Yuille
Based on the fact that very hard samples might have annotation errors, we propose a new sample selection policy, named Relaxed Upper Confident Bound (RUCB).
no code implementations • 7 Apr 2018 • Yuyin Zhou, Yan Wang, Peng Tang, Song Bai, Wei Shen, Elliot K. Fishman, Alan L. Yuille
In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain.
1 code implementation • ECCV 2018 • Siyuan Qiao, Wei Shen, Zhishuai Zhang, Bo wang, Alan Yuille
We present Deep Co-Training, a deep learning based method inspired by the Co-Training framework.
no code implementations • 5 Jan 2018 • Kai Zhao, Wei Shen, Shang-Hua Gao, Dandan Li, Ming-Ming Cheng
In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem.
Ranked #2 on Object Skeleton Detection on SK-LARGE
2 code implementations • CVPR 2018 • Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo wang, Alan Yuille
Age estimation from facial images is typically cast as a nonlinear regression problem.
Ranked #6 on Age Estimation on FGNET
no code implementations • 1 Dec 2017 • Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan L. Yuille
In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images.
no code implementations • CVPR 2018 • Zhishuai Zhang, Siyuan Qiao, Cihang Xie, Wei Shen, Bo wang, Alan L. Yuille
Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.
no code implementations • ICML 2018 • Siyuan Qiao, Zhishuai Zhang, Wei Shen, Bo wang, Alan Yuille
Our method is by introducing computation orderings to the channels within convolutional layers or blocks, based on which we gradually compute the outputs in a channel-wise manner.
1 code implementation • CVPR 2018 • Siyuan Qiao, Chenxi Liu, Wei Shen, Alan Yuille
In this paper, we are interested in the few-shot learning problem.
no code implementations • ICCV 2017 • Siyuan Qiao, Wei Shen, Weichao Qiu, Chenxi Liu, Alan Yuille
We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range.
no code implementations • ICCV 2017 • Wei Shen, Bin Wang, Yuan Jiang, Yan Wang, Alan Yuille
This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue.
no code implementations • NeurIPS 2017 • Wei Shen, Kai Zhao, Yilu Guo, Alan Yuille
This paper presents label distribution learning forests (LDLFs) - a novel label distribution learning algorithm based on differentiable decision trees, which have several advantages: 1) Decision trees have the potential to model any general form of label distributions by a mixture of leaf node predictions.
Ranked #11 on Age Estimation on MORPH album2 (Caucasian)
3 code implementations • 25 Dec 2016 • Yuyin Zhou, Lingxi Xie, Wei Shen, Yan Wang, Elliot K. Fishman, Alan L. Yuille
Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans.
1 code implementation • CVPR 2017 • Wei Shen, Rujie Liu
The transformation networks are responsible for the attribute manipulation and its dual operation and the discriminative network is used to distinguish the generated images from real images.
1 code implementation • 13 Sep 2016 • Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Xiang Bai, Alan Yuille
By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network.
no code implementations • 20 May 2016 • Wei Shen, Yuan Jiang, Wenjing Gao, Dan Zeng, Xinggang Wang
Contour and skeleton are two complementary representations for shape recognition.
1 code implementation • CVPR 2016 • Zheng Zhang, Chengquan Zhang, Wei Shen, Cong Yao, Wenyu Liu, Xiang Bai
In this paper, we propose a novel approach for text detec- tion in natural images.
Ranked #40 on Scene Text Detection on ICDAR 2015
no code implementations • CVPR 2016 • Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Zhijiang Zhang, Xiang Bai
Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts.
no code implementations • CVPR 2015 • Zheng Zhang, Wei Shen, Cong Yao, Xiang Bai
Recently, a variety of real-world applications have triggered huge demand for techniques that can extract textual information from natural scenes.
no code implementations • CVPR 2015 • Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang
Contour detection serves as the basis of a variety of computer vision tasks such as image segmentation and object recognition.