2024 Proceeding- Program Chairs:
- Zhedong Zheng,
- Yaxiong Wang,
- Xuelin Qian,
- Zhun Zhong,
- Zheng Wang,
- Liang Zheng
This workshop aims to bring together researchers, practitioners, and enthusiasts interested in object re-id to delve into the latest advancements, challenges, and opportunities in this dynamic field. The workshop covers a spectrum of topics related to object re-id, including but not limited to deep metric learning, multi-view data generation, video-based object re-id, cross-domain object re-id and real-world applications. The workshop provides a platform for researchers to showcase their work, exchange ideas, and foster potential collaborations. Additionally, it serves as a valuable opportunity for practitioners to stay abreast of the latest developments in object re-id technology. Overall, this workshop creates a unique space to explore the rapidly evolving field of object re-identification and its profound impact on advancing the capabilities of multimedia analysis and retrieval.
Proceeding Downloads
Exploring Part Features for Unsupervised Visible-Infrared Person Re-Identification
Unsupervised visible-infrared person re-identification (USVI-ReID) is a challenging task that aims to retrieve images of the same person from different modalities without annotations. Existing works mainly focus on constructing cross-modality ...
Refining Video-Based Person Re-Identification: An Integrated Framework with Facial and Body Cues
In Person Re-Identification (Re-ID), the use of facial cues has often been overlooked due to the focus on low-quality image datasets in past research. However, these cues are essential biometric markers, particularly valuable in video person re-...
Comprehensive Survey on Person Identification: Queries, Methods, and Datasets
Pedestrian recognition, which entails identifying and retrieving a specific pedestrian from an image gallery based on a query, is pivotal for applications such as urban surveillance and autonomous vehicle navigation. This survey provides a detailed ...
Revisit MTN: High-resolution Features Deserve More Attention
Multi-Scale Triplane Network (MTN), combining with a progressive learning strategy, is a recently proposed method for text-to-3D generation and has achieved favorable results. This paper aims to analyze the performance of MTN and explore refinements to ...
Analytical Study of DreamGaussian and MTN in the Field of 3D Generation
The development of AI in the field of 3D generation is a significant bright spot in the field of science and technology in recent years. Using AI generated 3D model can greatly improve the efficiency of the 3D model generation, will also promote 3D ...