Summary of Tell Me What to Track: Infusing Robust Language Guidance For Enhanced Referring Multi-object Tracking, by Wenjun Huang et al.
Tell Me What to Track: Infusing Robust Language Guidance for Enhanced Referring Multi-Object Tracking
by Wenjun Huang, Yang Ni, Hanning Chen, Yirui He, Ian Bryant, Yezi Liu, Mohsen Imani
First submitted to arxiv on: 17 Dec 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 Referring Multi-Object Tracking (RMOT) framework aims to localize and track an arbitrary number of targets based on language expressions in videos, overcoming limitations in existing methods by addressing imbalanced data distribution between newborn and existing targets. A collaborative matching strategy is employed to detect newborn targets while maintaining tracking performance. The framework integrates cross-modal and multi-scale fusion in the encoder and develops a referring-infused adaptation in the decoder, providing explicit guidance through query tokens. Compared to prior works, the proposed model demonstrates superior performance (+3.42%), highlighting its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to track objects mentioned in videos based on what people say. Right now, it’s hard to find objects that are new and start moving around. The researchers created a better system that can do this by looking at both the audio (what people say) and video (the movement of objects). They also made sure their system is good at finding old objects too. This helps make object tracking in videos more accurate and reliable. |
Keywords
» Artificial intelligence » Decoder » Encoder » Object tracking » Tracking
