Computer Science > Computer Vision and Pattern Recognition
arXiv:2412.19648 (cs)
[Submitted on 27 Dec 2024]
Title:Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues
View a PDF of the paper titled Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues, by X. Feng and 7 other authors
View PDFHTML (experimental)Abstract:Vision-Language Tracking (VLT) aims to localize a target in video sequences using a visual template and language description. While textual cues enhance tracking potential, current datasets typically contain much more image data than text, limiting the ability of VLT methods to align the two modalities effectively. To address this imbalance, we propose a novel plug-and-play method named CTVLT that leverages the strong text-image alignment capabilities of foundation grounding models. CTVLT converts textual cues into interpretable visual heatmaps, which are easier for trackers to process. Specifically, we design a textual cue mapping module that transforms textual cues into target distribution heatmaps, visually representing the location described by the text. Additionally, the heatmap guidance module fuses these heatmaps with the search image to guide tracking more effectively. Extensive experiments on mainstream benchmarks demonstrate the effectiveness of our approach, achieving state-of-the-art performance and validating the utility of our method for enhanced VLT.
| Comments: | Accepted by ICASSP '25 ! Code:this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM) |
| Cite as: | arXiv:2412.19648 [cs.CV] |
| (orarXiv:2412.19648v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2412.19648 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues, by X. Feng and 7 other authors
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