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20222024
most citedUnified Loss of Pair Similarity Optimization for Vision-Language Retrieval

2 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024★ 1 cited

Attribute-Aware Implicit Modality Alignment for Text Attribute Person Search

Xin Wang, Fangfang Liu, Zheng Li +1

Text attribute person search aims to find specific pedestrians through given textual attributes, which is very meaningful in the scene of searching for designated pedestrians throu…

cs.CV2023★ 1 cited

Boundary-Aware Proposal Generation Method for Temporal Action Localization

Hao Zhang, Chunyan Feng, Jiahui Yang +2

The goal of Temporal Action Localization (TAL) is to find the categories and temporal boundaries of actions in an untrimmed video. Most TAL methods rely heavily on action recogniti…

cs.CV2023★ 2 cited

Integrating Listwise Ranking into Pairwise-based Image-Text Retrieval

Zheng Li, Caili Guo, Xin Wang +2

Image-Text Retrieval (ITR) is essentially a ranking problem. Given a query caption, the goal is to rank candidate images by relevance, from large to small. The current ITR datasets…

cs.CV2023★ 2 cited

Selectively Hard Negative Mining for Alleviating Gradient Vanishing in Image-Text Matching

Zheng Li, Caili Guo, Xin Wang +2

Recently, a series of Image-Text Matching (ITM) methods achieve impressive performance. However, we observe that most existing ITM models suffer from gradients vanishing at the beg…

cs.CV2022★ 1 cited

Image-Text Retrieval with Binary and Continuous Label Supervision

Zheng Li, Caili Guo, Zerun Feng +3

Most image-text retrieval work adopts binary labels indicating whether a pair of image and text matches or not. Such a binary indicator covers only a limited subset of image-text s…

cs.CV2022★ 2 cited

Unified Loss of Pair Similarity Optimization for Vision-Language Retrieval

Zheng Li, Caili Guo, Xin Wang +3

There are two popular loss functions used for vision-language retrieval, i.e., triplet loss and contrastive learning loss, both of them essentially minimize the difference between…