Summary of Improving Visual Object Tracking Through Visual Prompting, by Shih-fang Chen and Jun-cheng Chen and I-hong Jhuo and Yen-yu Lin
Improving Visual Object Tracking through Visual Prompting
by Shih-Fang Chen, Jun-Cheng Chen, I-Hong Jhuo, Yen-Yu Lin
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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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 paper introduces PiVOT (Prompting mechanism for generic Visual Object Tracking), a novel approach to dynamic target representation adaptation against distractors in visual object tracking. It leverages CLIP (pre-trained foundation model) to generate and refine visual prompts, enabling the transfer of foundation model knowledge for tracking. The tracker is trained on instance-specific data and excels at recognizing unique object instances. PiVOT first compiles a visual prompt highlighting potential target locations, then refines it based on similarities between candidate objects and reference templates. This refined prompt reduces irrelevant information, guiding the tracker to generate improved instance-aware feature maps and suppress distracting objects. The proposed method does not involve CLIP during training, preserving its generalization capability. Extensive experiments demonstrate PiVOT’s effectiveness in multiple benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PiVOT is a new way for computers to track moving objects in videos. It helps machines learn what makes one object different from others around it. This is important because most tracking methods get confused when there are many similar objects. The paper introduces a special tool that generates and improves visual “prompts” to help the tracker focus on the right objects. This method uses a powerful pre-trained model called CLIP, which helps the tracker recognize unique objects. By using this prompt, the tracker can ignore distracting objects and improve its tracking accuracy. |
Keywords
» Artificial intelligence » Generalization » Object tracking » Prompt » Prompting » Tracking
