9 citations · 24 across the 11 of their papers we have counts for
11 papers
Stereo Image Coding for Machines with Joint Visual Feature Compression
Dengchao Jin, Jianjun Lei, Bo Peng +3
2D image coding for machines (ICM) has achieved great success in coding efficiency, while less effort has been devoted to stereo image fields. To promote the efficiency of stereo i…
Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection
Zhipeng Yu, Qianqian Xu, Yangbangyan Jiang +2
The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving the robustn…
Prompting Video-Language Foundation Models with Domain-specific Fine-grained Heuristics for Video Question Answering
Ting Yu, Kunhao Fu, Shuhui Wang +2
Video Question Answering (VideoQA) represents a crucial intersection between video understanding and language processing, requiring both discriminative unimodal comprehension and s…
Improved Diversity-Promoting Collaborative Metric Learning for Recommendation
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…
Downstream-Pretext Domain Knowledge Traceback for Active Learning
Beichen Zhang, Liang Li, Zheng-Jun Zha +2
Active learning (AL) is designed to construct a high-quality labeled dataset by iteratively selecting the most informative samples. Such sampling heavily relies on data representat…
Query-guided Prototype Evolution Network for Few-Shot Segmentation
Runmin Cong, Hang Xiong, Jinpeng Chen +3
Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support features for prototype generation, neglecting the specific requirements of the query. To address this, w…
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection
Junwei He, Qianqian Xu, Yangbangyan Jiang +2
Graph anomaly detection is crucial for identifying nodes that deviate from regular behavior within graphs, benefiting various domains such as fraud detection and social network. Al…
DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework
Siran Dai, Qianqian Xu, Zhiyong Yang +2
The Area Under the ROC Curve (AUC) is a widely employed metric in long-tailed classification scenarios. Nevertheless, most existing methods primarily assume that training and testi…
Towards Demystifying the Generalization Behaviors When Neural Collapse Emerges
Peifeng Gao, Qianqian Xu, Yibo Yang +5
Neural Collapse (NC) is a well-known phenomenon of deep neural networks in the terminal phase of training (TPT). It is characterized by the collapse of features and classifier into…
A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization
Zitai Wang, Qianqian Xu, Zhiyong Yang +4
Due to the inherent imbalance in real-world datasets, naïve Empirical Risk Minimization (ERM) tends to bias the learning process towards the majority classes, hindering generalizat…
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top- Multi-Label Learning
Yuchen Sun, Qianqian Xu, Zitai Wang +1
With the great success of deep neural networks, adversarial learning has received widespread attention in various studies, ranging from multi-class learning to multi-label learning…