Summary of How Texts Help? a Fine-grained Evaluation to Reveal the Role Of Language in Vision-language Tracking, by Xuchen Li et al.
How Texts Help? A Fine-grained Evaluation to Reveal the Role of Language in Vision-Language Tracking
by Xuchen Li, Shiyu Hu, Xiaokun Feng, Dailing Zhang, Meiqi Wu, Jing Zhang, Kaiqi Huang
First submitted to arxiv on: 23 Nov 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 Vision-language tracking (VLT) framework, VLTVerse, aims to improve tracking performance in challenging conditions by incorporating textual information. Current VLT trackers often underperform compared to single-modality methods on multiple benchmarks. To address this, the authors propose a fine-grained evaluation framework that comprehensively considers multiple challenge factors and diverse semantic information. The contributions include: introducing 10 sequence-level challenge labels and 6 types of multi-granularity semantic information; conducting systematic fine-grained evaluations of three mainstream SOTA VLT trackers across complex scenarios; and examining the impact of various semantic types on specific challenge factors in relation to different algorithms. This framework aims to reveal the role of language in VLT and provide essential guidance for enhancing VLT across data, evaluation, and algorithmic dimensions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VLT is a way to track objects that includes words from videos or text descriptions. Current trackers don’t work well in situations with fast motion or changing shapes. The problem is that the extra information from text can sometimes be confusing. To fix this, researchers created a new evaluation framework called VLTVerse. It looks at how different algorithms perform under various conditions and types of semantic information. This helps us understand how language affects tracking performance. The results show what factors affect which algorithms and which challenges they struggle with. This will help improve VLT in the future. |
Keywords
» Artificial intelligence » Tracking
