2 citations · 4 across the 3 of their papers we have counts for
9 papers
Disentangled Noisy Correspondence Learning
Zhuohang Dang, Minnan Luo, Jihong Wang +6
Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impract…
Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
Haochen Han, Qinghua Zheng, Guang Dai +2
Collecting well-matched multimedia datasets is crucial for training cross-modal retrieval models. However, in real-world scenarios, massive multimodal data are harvested from the I…
Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation
Zhuohang Dang, Minnan Luo, Chengyou Jia +3
Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. Recently, to alleviate expensive data collection, co-occurring pairs from the Inte…
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition
Chengyou Jia, Minnan Luo, Xiaojun Chang +6
Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing method…
Disentangled Representation Learning with Transmitted Information Bottleneck
Zhuohang Dang, Minnan Luo, Chengyou Jia +4
Encoding only the task-related information from the raw data, \ie, disentangled representation learning, can greatly contribute to the robustness and generalizability of models. Al…
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement
Chengyou Jia, Minnan Luo, Zhuohang Dang +3
Dominant Person Search methods aim to localize and recognize query persons in a unified network, which jointly optimizes two sub-tasks, \ie, pedestrian detection and Re-IDentificat…