116 citations · 140 across the 11 of their papers we have counts for
6 papers · 1 filter
Multi-Modal Dataset Distillation in the Wild
Zhuohang Dang, Minnan Luo, Chengyou Jia +3
Recent multi-modal models have shown remarkable versatility in real-world applications. However, their rapid development encounters two critical data challenges. First, the trainin…
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…
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…
CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search
Chengyou Jia, Minnan Luo, Caixia Yan +2
Recently, weakly supervised person search is proposed to discard human-annotated identities and train the model with only bounding box annotations. A natural way to solve this prob…
Semantics-Guided Contrastive Network for Zero-Shot Object detection
Caixia Yan, Xiaojun Chang, Minnan Luo +3
Zero-shot object detection (ZSD), the task that extends conventional detection models to detecting objects from unseen categories, has emerged as a new challenge in computer vision…