Summary of Clip-based Camera-agnostic Feature Learning For Intra-camera Person Re-identification, by Xuan Tan et al.
CLIP-based Camera-Agnostic Feature Learning for Intra-camera Person Re-Identification
by Xuan Tan, Xun Gong, Yang Xiang
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed CLIP-based Camera-Agnostic Feature Learning (CCAFL) framework excels in intra-camera supervised person re-identification (ICS ReID) tasks, leveraging the inherent textual description capabilities of Contrastive Language-Image Pre-Training (CLIP) models. Two custom modules, Intra-Camera Discriminative Learning (ICDL) and Inter-Camera Adversarial Learning (ICAL), are designed to guide the model towards learning camera-agnostic pedestrian features. The framework first establishes learnable textual prompts for intra-camera images, providing semantic supervision signals for subsequent intra- and inter-camera learning. ICDL increases inter-class variation by considering hard positive and negative samples within each camera, leading to finer-grained pedestrian feature learning. ICAL reduces inter-camera pedestrian feature discrepancies by penalizing the model’s ability to predict camera origins, enhancing its capability to recognize pedestrians from different viewpoints. The approach demonstrates state-of-the-art performance on popular ReID datasets, including a 58.9% mAP accuracy on MSMT17, surpassing previous methods by 7.6%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The CLIP-based Camera-Agnostic Feature Learning (CCAFL) framework is a new way to improve person re-identification tasks. It uses the Contrastive Language-Image Pre-Training (CLIP) model in a special way to help identify people in different cameras. The framework has two parts: one that makes the model learn more about each camera and another that helps the model understand that people can look different from different angles. This approach is really good at identifying people, even when they are shown from different viewpoints. |
Keywords
» Artificial intelligence » Supervised
