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- W2766657036 abstract "Multi-shot person Re-IDentification (Re-ID) has recently received more research attention as its problem setting is more realistic compared to single-shot Re-ID in terms of application. While many large-scale single-shot Re-ID human image datasets have been released, most existing multishot Re-ID video sequence datasets containonly a few (i.e., several hundreds) human instances, which hinders further improvement of multi-shot Re-ID performance. To this end, we propose a deep cross-modality alignment network, which jointly explores both human sequence pairs and image pairs to facilitate training better multi-shot human Re-ID models, i.e., via transferring knowledge from image data to sequence data. To mitigate modality-to-modality mismatch issue, the proposed network is equipped with an image-to-sequence adaption module called cross-modality alignment sub-network, which successfully maps each human image into a pseudo human sequence to facilitate knowledge transferring and joint training. Extensive experimental results on several multi-shot person Re-ID benchmarks demonstrate great performance gain brought up by the proposed network." @default.
- W2766657036 created "2017-11-10" @default.
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- W2766657036 date "2017-10-19" @default.
- W2766657036 modified "2023-09-28" @default.
- W2766657036 title "Deep Cross-Modality Alignment for Multi-Shot Person Re-IDentification" @default.
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- W2766657036 doi "https://doi.org/10.1145/3123266.3123324" @default.
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