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<!-- # ALTrack -->ALTrack: Autogenic Language Embedding for Coherent Point Tracking
<div align="center"> <br> <a>Zikai Song</a><sup><span>1</span></sup>, <a>Ying Tang</a><sup><span>1</span></sup>, <a>Run Luo</a><sup><span>1</span></sup>, <a>Lintao Ma</a><sup><span>1</span></sup>, <a>Junqing Yu</a><sup><span>1</span></sup>, <a>Yi-Ping Phoebe Chen<sup><span>2</span></sup>, <a>WeiYang<sup><span>1</span></sup> <br><sup>1</sup> Huazhong University of Science and Technology <br>
<sup>2</sup> La Trobe University<br>
paper
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ALTrack is a coherent point tracking framework which designes an autogenic language embedding for visual feature enhancement, strengthens point correspondence in long-term sequences. Unlike existing visual-language schemes, our approach learns text embeddings from visual features through a dedicated mapping network, enabling seamless adaptation to various tracking tasks without explicit text annotations. Additionally, we introduce a consistency decoder that efficiently integrates text tokens into visual features with minimal computational overhead.
Code
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Citation
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@inproceedings{song2024altrack,
title={Autogenic Language Embedding for Coherent Point Tracking},
author={Song, Zikai and Tang, Ying and Luo, Run and Ma, Lintao and Yu, Junqing and Chen, Yi-Ping Phoebe and Yang, Wei},
booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
year={2024}
}