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Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification
Video-based person re-identification (reID) aims at matching the same pe...
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Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification
Person Re-Identification (person re-id) is a crucial task as its applica...
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Sharp Attention Network via Adaptive Sampling for Person Re-identification
In this paper, we present novel sharp attention networks by adaptively s...
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HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Pedestrian analysis plays a vital role in intelligent video surveillance...
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Re-Identification with Consistent Attentive Siamese Networks
We propose a new deep architecture for person re-identification (re-id)....
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Multi-Level Factorisation Net for Person Re-Identification
Key to effective person re-identification (Re-ID) is modelling discrimin...
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Batch Coherence-Driven Network for Part-aware Person Re-Identification
Existing part-aware person re-identification methods typically employ tw...
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ABD-Net: Attentive but Diverse Person Re-Identification
Attention mechanism has been shown to be effective for person re-identification (Re-ID). However, the learned attentive feature embeddings which are often not naturally diverse nor uncorrelated, will compromise the retrieval performance based on the Euclidean distance. We advocate that enforcing diversity could greatly complement the power of attention. To this end, we propose an Attentive but Diverse Network (ABD-Net), which seamlessly integrates attention modules and diversity regularization throughout the entire network, to learn features that are representative, robust, and more discriminative. Specifically, we introduce a pair of complementary attention modules, focusing on channel aggregation and position awareness, respectively. Furthermore, a new efficient form of orthogonality constraint is derived to enforce orthogonality on both hidden activations and weights. Through careful ablation studies, we verify that the proposed attentive and diverse terms each contributes to the performance gains of ABD-Net. On three popular benchmarks, ABD-Net consistently outperforms existing state-of-the-art methods.
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