Papers › Dense Interaction Learning for Video-based Person Re-identification

Dense Interaction Learning for Video-based Person Re-identification

16 Mar 2021ICCV 2021 10arXiv:2103.09013archive 2025-07-28

Tianyu He, Xin Jin, Xu Shen, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua

Video-based person re-identification (re-ID) aims at matching the same person across video clips. Efficiently exploiting multi-scale fine-grained features while building the structural interaction among them is pivotal for its success. In this paper, we propose a hybrid framework, Dense Interaction Learning (DenseIL), that takes the principal advantages of both CNN-based and Attention-based architectures to tackle video-based person re-ID difficulties. DenseIL contains a CNN encoder and a Dense Interaction (DI) decoder. The CNN encoder is responsible for efficiently extracting discriminative spatial features while the DI decoder is designed to densely model spatial-temporal inherent interaction across frames. Different from previous works, we additionally let the DI decoder densely attends to intermediate fine-grained CNN features and that naturally yields multi-grained spatial-temporal representation for each video clip. Moreover, we introduce Spatio-TEmporal Positional Embedding (STEP-Emb) into the DI decoder to investigate the positional relation among the spatial-temporal inputs. Our experiments consistently and significantly outperform all the state-of-the-art methods on multiple standard video-based person re-ID datasets.

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Tasks

Decoder Person Re-Identification Video-Based Person Re-Identification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Person Re-Identification	DukeMTMC-reID	DenseIL	mAP	97.1	#1 of 94	Archive leaderboard	report
Person Re-Identification	MARS	DenseIL	Rank-1	90.8	#2 of 21	Archive leaderboard	report
Person Re-Identification	MARS	DenseIL	Rank-20	98.8	#2 of 21	Archive leaderboard	report
Person Re-Identification	MARS	DenseIL	Rank-5	97.1	#2 of 21	Archive leaderboard	report
Person Re-Identification	MARS	DenseIL	mAP	87	#2 of 21	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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