Papers › Learning Feature Fusion for Unsupervised Domain Adaptive Person Re-identification
Learning Feature Fusion for Unsupervised Domain Adaptive Person Re-identification
Jin Ding, Xue Zhou
Unsupervised domain adaptive (UDA) person re-identification (ReID) has gained increasing attention for its effectiveness on the target domain without manual annotations. Most fine-tuning based UDA person ReID methods focus on encoding global features for pseudo labels generation, neglecting the local feature that can provide for the fine-grained information. To handle this issue, we propose a Learning Feature Fusion (LF2) framework for adaptively learning to fuse global and local features to obtain a more comprehensive fusion feature representation. Specifically, we first pre-train our model within a source domain, then fine-tune the model on unlabeled target domain based on the teacher-student training strategy. The average weighting teacher network is designed to encode global features, while the student network updating at each iteration is responsible for fine-grained local features. By fusing these multi-view features, multi-level clustering is adopted to generate diverse pseudo labels. In particular, a learnable Fusion Module (FM) for giving prominence to fine-grained local information within the global feature is also proposed to avoid obscure learning of multiple pseudo labels. Experiments show that our proposed LF2 framework outperforms the state-of-the-art with 73.5% mAP and 83.7% Rank1 on Market1501 to DukeMTMC-ReID, and achieves 83.2% mAP and 92.8% Rank1 on DukeMTMC-ReID to Market1501.
Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Domain Adaptation | Duke to Market | LF2 | mAP | 83.2 | #3 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Duke to Market | LF2 | rank-1 | 92.8 | #3 of 26 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to Duke | LF2 | mAP | 73.5 | #2 of 25 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Market to Duke | LF2 | rank-1 | 83.7 | #2 of 25 | 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.
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