434 citations · 2.1k across the 88 of their papers we have counts for
28 papers · 1 filter
Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360° Omnidirectional Image
Runmin Cong, Ke Huang, Jianjun Lei +3
Salient object detection (SOD) aims to determine the most visually attractive objects in an image. With the development of virtual reality technology, 360° omnidirectional image ha…
Progressive Multi-resolution Loss for Crowd Counting
Ziheng Yan, Yuankai Qi, Guorong Li +4
Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth. To effectively regulate…
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection
Chen Zhang, Guorong Li, Yuankai Qi +4
Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved signific…
Consistency-Aware Anchor Pyramid Network for Crowd Localization
Xinyan Liu, Guorong Li, Yuankai Qi +4
Crowd localization aims to predict the spatial position of humans in a crowd scenario. We observe that the performance of existing methods is challenged from two aspects: (i) ranki…
Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective
Yunrui Zhao, Qianqian Xu, Yangbangyan Jiang +2
Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, t…
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm
Huiyang Shao, Qianqian Xu, Zhiyong Yang +2
The Partial Area Under the ROC Curve (PAUC), typically including One-way Partial AUC (OPAUC) and Two-way Partial AUC (TPAUC), measures the average performance of a binary classifie…
Does Thermal Really Always Matter for RGB-T Salient Object Detection?
Runmin Cong, Kepu Zhang, Chen Zhang +4
In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by i…
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
Yao Zhu, Yuefeng Chen, Xiaodan Li +6
Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and t…
CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
Runmin Cong, Qinwei Lin, Chen Zhang +4
Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (…
OpenAUC: Towards AUC-Oriented Open-Set Recognition
Zitai Wang, Qianqian Xu, Zhiyong Yang +3
Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some…
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Shilong Bao, Qianqian Xu, Zhiyong Yang +3
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Fo…
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability
Peisong Wen, Qianqian Xu, Zhiyong Yang +2
Stochastic optimization of the Area Under the Precision-Recall Curve (AUPRC) is a crucial problem for machine learning. Although various algorithms have been extensively studied fo…
MaxMatch: Semi-Supervised Learning with Worst-Case Consistency
Yangbangyan Jiang, Xiaodan Li, Yuefeng Chen +5
In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-a…
A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game
Ke Ma, Qianqian Xu, Jinshan Zeng +3
Rank aggregation with pairwise comparisons has shown promising results in elections, sports competitions, recommendations, and information retrieval. However, little attention has…
Optimizing Partial Area Under the Top-k Curve: Theory and Practice
Zitai Wang, Qianqian Xu, Zhiyong Yang +3
Top-k error has become a popular metric for large-scale classification benchmarks due to the inevitable semantic ambiguity among classes. Existing literature on top-k optimization…
Multi-Attention Network for Compressed Video Referring Object Segmentation
Weidong Chen, Dexiang Hong, Yuankai Qi +5
Referring video object segmentation aims to segment the object referred by a given language expression. Existing works typically require compressed video bitstream to be decoded to…
Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
Xuejing Liu, Liang Li, Shuhui Wang +4
Weakly supervised Referring Expression Grounding (REG) aims to ground a particular target in an image described by a language expression while lacking the correspondence between ta…
Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction
Tianwei Cao, Qianqian Xu, Zhiyong Yang +1
Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in the recommender systems. Recen…
Geometry Interaction Knowledge Graph Embeddings
Zongsheng Cao, Qianqian Xu, Zhiyong Yang +2
Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks. Previous work usually embeds KGs into a sing…
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
Wenzheng Hou, Qianqian Xu, Zhiyong Yang +3
It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a ty…
ER: Equivariance Regularizer for Knowledge Graph Completion
Zongsheng Cao, Qianqian Xu, Zhiyong Yang +1
Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model…
Optimizing Two-way Partial AUC with an End-to-end Framework
Zhiyong Yang, Qianqian Xu, Shilong Bao +3
The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive…
Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling
Shilong Bao, Qianqian Xu, Zhiyong Yang +2
The recently proposed Collaborative Metric Learning (CML) paradigm has aroused wide interest in the area of recommendation systems (RS) owing to its simplicity and effectiveness. T…
Automatic Relation-aware Graph Network Proliferation
Shaofei Cai, Liang Li, Xinzhe Han +3
Graph neural architecture search has sparked much attention as Graph Neural Networks (GNNs) have shown powerful reasoning capability in many relational tasks. However, the currentl…