15 citations · 135 across the 41 of their papers we have counts for
21 papers · 1 filter
TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation
Xin Lin, Chong Shi, Yibing Zhan +3
Dynamic scene graph generation (SGG) focuses on detecting objects in a video and determining their pairwise relationships. Existing dynamic SGG methods usually suffer from several…
Free-Form Composition Networks for Egocentric Action Recognition
Haoran Wang, Qinghua Cheng, Baosheng Yu +4
Egocentric action recognition is gaining significant attention in the field of human action recognition. In this paper, we address data scarcity issue in egocentric action recognit…
Token Contrast for Weakly-Supervised Semantic Segmentation
Lixiang Ru, Heliang Zheng, Yibing Zhan +1
Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the local structure…
DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes
Shenghao Hao, Peiyuan Liu, Yibing Zhan +5
Cross-view multi-object tracking aims to link objects between frames and camera views with substantial overlaps. Although cross-view multi-object tracking has received increased at…
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
Yuanyuan Liu, Wenbin Wang, Yibing Zhan +3
Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets…
Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition
Mengya Han, Yibing Zhan, Yong Luo +4
Few-shot visual recognition refers to recognize novel visual concepts from a few labeled instances. Many few-shot visual recognition methods adopt the metric-based meta-learning pa…
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection
Gaoang Wang, Yibing Zhan, Xinchao Wang +2
Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables ef…
Learning Graph Neural Networks for Image Style Transfer
Yongcheng Jing, Yining Mao, Yiding Yang +4
State-of-the-art parametric and non-parametric style transfer approaches are prone to either distorted local style patterns due to global statistics alignment, or unpleasing artifa…
Pluralistic Image Completion with Probabilistic Mixture-of-Experts
Xiaobo Xia, Wenhao Yang, Jie Ren +4
Pluralistic image completion focuses on generating both visually realistic and diverse results for image completion. Prior methods enjoy the empirical successes of this task. Howev…
HL-Net: Heterophily Learning Network for Scene Graph Generation
Xin Lin, Changxing Ding, Yibing Zhan +2
Scene graph generation (SGG) aims to detect objects and predict their pairwise relationships within an image. Current SGG methods typically utilize graph neural networks (GNNs) to…
RU-Net: Regularized Unrolling Network for Scene Graph Generation
Xin Lin, Changxing Ding, Jing Zhang +2
Scene graph generation (SGG) aims to detect objects and predict the relationships between each pair of objects. Existing SGG methods usually suffer from several issues, including 1…
BatchFormerV2: Exploring Sample Relationships for Dense Representation Learning
Zhi Hou, Baosheng Yu, Chaoyue Wang +2
Attention mechanisms have been very popular in deep neural networks, where the Transformer architecture has achieved great success in not only natural language processing but also…
An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection
Jia Liu, Wenjie Xuan, Yuhang Gan +2
Existing deep learning-based change detection methods try to elaborately design complicated neural networks with powerful feature representations, but ignore the universal domain s…
Contrastive Boundary Learning for Point Cloud Segmentation
Liyao Tang, Yibing Zhan, Zhe Chen +2
Point cloud segmentation is fundamental in understanding 3D environments. However, current 3D point cloud segmentation methods usually perform poorly on scene boundaries, which deg…
Hyper-relationship Learning Network for Scene Graph Generation
Yibing Zhan, Zhi Chen, Jun Yu +3
Generating informative scene graphs from images requires integrating and reasoning from various graph components, i.e., objects and relationships. However, current scene graph gene…
Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers
Lixiang Ru, Yibing Zhan, Baosheng Yu +1
Weakly-supervised semantic segmentation (WSSS) with image-level labels is an important and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS have…
Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling
Lixiang Ru, Bo Du, Yibing Zhan +1
Weakly-Supervised Semantic Segmentation (WSSS) methods with image-level labels generally train a classification network to generate the Class Activation Maps (CAMs) as the initial…
Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation
Chao Chen, Yibing Zhan, Baosheng Yu +3
Scene Graph Generation (SGG) aims to build a structured representation of a scene using objects and pairwise relationships, which benefits downstream tasks. However, current SGG me…
Expression Snippet Transformer for Robust Video-based Facial Expression Recognition
Yuanyuan Liu, Wenbin Wang, Chuanxu Feng +3
The recent success of Transformer has provided a new direction to various visual understanding tasks, including video-based facial expression recognition (FER). By modeling visual…
Graph Pattern Loss based Diversified Attention Network for Cross-Modal Retrieval
Xueying Chen, Rong Zhang, Yibing Zhan
Cross-modal retrieval aims to enable flexible retrieval experience by combining multimedia data such as image, video, text, and audio. One core of unsupervised approaches is to dig…
On Exploring Undetermined Relationships for Visual Relationship Detection
Yibing Zhan, Jun Yu, Ting Yu +1
In visual relationship detection, human-notated relationships can be regarded as determinate relationships. However, there are still large amount of unlabeled data, such as object…