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arXiv Computer Vision By Mingyu Wang, Wei Jiang, Haojie Liu, Zhiyong Li, Weijie Mao

Beyond Discrete Samples: High Information Density Replay for Efficient Lifelong Person Re-Identification

Read the original on arXiv Computer Vision →

The paper introduces HiDeR, a High Information Density Replay framework for Lifelong Person Re-Identification that replaces discrete sample selection with information compression. It uses a complexity‑aware memory allocation based on intra‑class variance and a metric‑guided condensation objective to preserve essential identity topologies, while a cross‑modality adaptation strategy bridges synthetic and real styles to improve training. Experiments show HiDeR outperforms state‑of‑the‑art methods in knowledge retention and generalization, and reduces cumulative replay cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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