Hugging Face Trending Papers
DiffReID: Discriminative Diffusion Model for Object Re-Identification
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arXiv Computer Vision
4d ago
DiffReID: Discriminative Diffusion Model for Object Re-Identification
arXiv:2609.36894v1 Announce Type: new Abstract: As a fundamental image processing task, object Re-Identification (ReID) aims to retrieve objects across non-overlapping cameras. Recently, with the dev...
Hugging Face Trending Papers
Jun 1
Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification
The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting training objectives, leading to suboptimal shared representations. While I2I ReID focuses on identity-level invariance across images of the same person, T2I ReID is driven by instance-specific textual descriptions tied to unique visual traits.
arXiv Computer Vision
Sep 22
Incentive Noise and Structural Prior Infusion for Multi-modal Object Re-Identification
arXiv:2609.24539v1 Announce Type: new Abstract: Multi-modal object Re-Identification (ReID) benefits from complementary information across heterogeneous imaging modalities. To further enrich semantic...
arXiv AI
Jun 2
Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification
arXiv:2606. 02242v1 Announce Type: cross Abstract: The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting training objectives, leading to suboptimal shared representations.
arXiv AI
Sep 15
A Generative AI Integrated Multimodal Framework for Low-Latency Multi-Camera Person Re-Identification
arXiv:2609.14419v1 Announce Type: cross Abstract: Person re-identification (ReID) is essential for multi-camera surveillance and tracking, yet remains difficult due to viewpoint and illumination chan...
Hugging Face Trending Papers
Jul 23
GroupVideo: Multi-Identity Customized Text-to-Video Generation
Current identity customized video generation methodologies are predominantly limited to single-identity scenarios, as the lack of explicit identity separation mechanisms often leads to identity confusion in multi-identity settings. Existing multi-identity approaches, which directly extend single-identity frameworks by concatenating face images as input conditions, frequently result in unnatural facial expressions and motions, manifesting as the "copy-paste" phenomenon.