5 papers
Multimodal LLM-Empowered Re-Ranking for Generalizable Person Re-Identification
Jiachen Li, Xiaojin Gong
Domain Generalizable (DG) person re-identification (Re-ID) has attracted growing research interest due to its potential for deployment in unseen real-world scenarios. Most existing…
Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
Jiachen Li, Xiaojin Gong
This work aims to adapt large-scale pre-trained vision-language models, such as contrastive language-image pretraining (CLIP), to enhance the performance of object reidentification…
Modality-Aware Bias Mitigation and Invariance Learning for Unsupervised Visible-Infrared Person Re-Identification
Menglin Wang, Xiaojin Gong, Jiachen Li +1
Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match individuals across visible and infrared cameras without relying on any annotation. Given the signif…
Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-Identification
Jiachen Li, Xiaojin Gong
Domain-generalizable re-identification (DG Re-ID) aims to train a model on one or more source domains and evaluate its performance on unseen target domains, a task that has attract…
Prior-Constrained Association Learning for Fine-Grained Generalized Category Discovery
Menglin Wang, Zhun Zhong, Xiaojin Gong
This paper addresses generalized category discovery (GCD), the task of clustering unlabeled data from potentially known or unknown categories with the help of labeled instances fro…