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Video Person Re-identification with Competitive Snippet-Similarity Aggregation and Co-attentive Snippet Embedding

机译:具有竞争性摘要相似性聚合和共同关注性摘要嵌入的视频人重新识别

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In this paper, we address video-based person re-identification with competitive snippet-similarity aggregation and co-attentive snippet embedding. Our approach divides long person sequences into multiple short video snippets and aggregates the top-ranked snippet similarities for sequence-similarity estimation. With this strategy, the intra-person visual variation of each sample could be minimized for similarity estimation, while the diverse appearance and temporal information are maintained. The snippet similarities are estimated by a deep neural network with a novel temporal co-attention for snippet embedding. The attention weights are obtained based on a query feature, which is learned from the whole probe snippet by an LSTM network, making the resulting embeddings less affected by noisy frames. The gallery snippet shares the same query feature with the probe snippet. Thus the embedding of gallery snippet can present more relevant features to compare with the probe snippet, yielding more accurate snippet similarity. Extensive ablation studies verify the effectiveness of competitive snippet-similarity aggregation as well as the temporal co-attentive embedding. Our method significantly outperforms the current state-of-the-art approaches on multiple datasets.
机译:在本文中,我们通过竞争性片段相似性聚合和共同关注性片段嵌入来解决基于视频的人员重新识别问题。我们的方法将长人物序列分成多个短视频片段,并汇总排名靠前的片段相似度,以进行序列相似度估算。通过这种策略,可以将每个样本的人内视觉变化最小化,以进行相似性估计,同时保持各种外观和时间信息。片段相似性是由深度神经网络估计的,其中具有针对片段嵌入的新颖的时间共关注。注意力权重是基于查询功能获得的,该功能是通过LSTM网络从整个探针片段中学习到的,从而使生成的嵌入内容受噪声帧的影响较小。图库片段与探针片段共享相同的查询功能。因此,图库片段的嵌入可以呈现更多相关特征以与探针片段进行比较,从而产生更准确的片段相似性。广泛的消融研究证明了竞争性片段相似性聚合以及时间上的共同关注嵌入的有效性。我们的方法大大优于当前在多个数据集上使用的最新方法。

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