104 citations · 123 across the 5 of their papers we have counts for
5 papers
Rethinking Deep Contrastive Learning with Embedding Memory
Haozhi Zhang, Xun Wang, Weilin Huang +1
Pair-wise loss functions have been extensively studied and shown to continuously improve the performance of deep metric learning (DML). However, they are primarily designed with in…
Channel Interaction Networks for Fine-Grained Image Categorization
Yu Gao, Xintong Han, Xun Wang +2
Fine-grained image categorization is challenging due to the subtle inter-class differences.We posit that exploiting the rich relationships between channels can help capture such di…
Cross-Batch Memory for Embedding Learning
Xun Wang, Haozhi Zhang, Weilin Huang +1
Mining informative negative instances are of central importance to deep metric learning (DML), however this task is intrinsically limited by mini-batch training, where only a mini-…
HAL: Improved Text-Image Matching by Mitigating Visual Semantic Hubs
Fangyu Liu, Rongtian Ye, Xun Wang +1
The hubness problem widely exists in high-dimensional embedding space and is a fundamental source of error for cross-modal matching tasks. In this work, we study the emergence of h…
Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning
Xun Wang, Xintong Han, Weilin Huang +2
A family of loss functions built on pair-based computation have been proposed in the literature which provide a myriad of solutions for deep metric learning. In this paper, we prov…