We lifted 6 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| michuanhaohao/AICITY2021_Track2_DMT | canonical | 2 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| copy_state_dict | Ran | michuanhaohao/AICITY2021_Track2_DMT/train_stage2_v1.py code served (permissive licence) · get_code("fcfe83b90ec34b09") |
| normalize | Ran | michuanhaohao/AICITY2021_Track2_DMT/loss/triplet_loss.py code served (permissive licence) · get_code("e3b2a83f52101f2d") |
| cosine_dist | Not yet run | michuanhaohao/AICITY2021_Track2_DMT/loss/triplet_loss.py code served (permissive licence) · get_code("71b4e1d375d8fd78") |
| euclidean_dist | Not yet run | michuanhaohao/AICITY2021_Track2_DMT/loss/triplet_loss.py code served (permissive licence) · get_code("5315f75bf5367f0a") |
| extract_features | Not yet run | michuanhaohao/AICITY2021_Track2_DMT/train_stage2_v1.py code served (permissive licence) · get_code("f419ea700861ea83") |
| make_loss | Not yet run | michuanhaohao/AICITY2021_Track2_DMT/loss/make_loss.py code served (permissive licence) · get_code("4b889f410e8c0400") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four points, i.e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge. (1) Both cropping training data and using synthetic data can help the model learn more discriminative features. (2) Since there is a new scenario in the test set that dose not appear in the training set, UDA methods perform well in the challenge. (3) Post-processing techniques including re-ranking, image-to-track retrieval, inter-camera fusion, etc, significantly improve final performance. (4) We ensemble CNN-based models and transformer-based models which provide different representation diversity. With aforementioned techniques, our method finally achieves 0.7445 mAP score, yielding the first place in the competition. Codes are available at https://github.com/michuanhaohao/AICITY2021_Track2_DMT.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2105.09701")
get_code_for_paper("2105.09701")
have("2105.09701")
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