Image-Text-Image Knowledge Transferring for Lifelong Person Re-Identification with Hybrid Clothing States
<|reference_start|>Image-Text-Image Knowledge Transferring for Lifelong Person Re-Identification with Hybrid Clothing States: With the continuous expansion of intelligent surveillance networks, lifelong person re-identification (LReID) has received widespread attention, pursuing the need of self-evolution across different domains. However, existing LReID studies accumulate knowledge with the assumption that people would not change their clothes. In this paper, we propose a more practical task, namely lifelong person re-identification with hybrid clothing states (LReID-Hybrid), which takes a series of cloth-changing and cloth-consistent domains into account during lifelong learning. To tackle the challenges of knowledge granularity mismatch and knowledge presentation mismatch that occurred in LReID-Hybrid, we take advantage of the consistency and generalization of the text space, and propose a novel framework, dubbed $Teata$, to effectively align, transfer and accumulate knowledge in an "image-text-image" closed loop. Concretely, to achieve effective knowledge transfer, we design a Structured Semantic Prompt (SSP) learning to decompose the text prompt into several structured pairs to distill knowledge from the image space with a unified granularity of text description. Then, we introduce a Knowledge Adaptation and Projection strategy (KAP), which tunes text knowledge via a slow-paced learner to adapt to different tasks without catastrophic forgetting. Extensive experiments demonstrate the superiority of our proposed $Teata$ for LReID-Hybrid as well as on conventional LReID benchmarks over advanced methods.<|reference_end|>
@article{wang2024image-text-image,
    title={Image-Text-Image Knowledge Transferring for Lifelong Person
  Re-Identification with Hybrid Clothing States},
    author={Qizao Wang, Xuelin Qian, Bin Li, Yanwei Fu, Xiangyang Xue},
    journal={arXiv preprint arXiv:2405.16600},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16600},
    primaryClass={cs.CV}
}