本项目基于 ACT (AAAI 2020) 的开源代码 (论文, 代码)
根据初始匹配结果的时间戳 (用GMM) 学习到一个时空模型，然后用于度量学习中的三元组选取、距离矩阵的重排序。
| M2D | D2M | |
|---|---|---|
| ACT | 54.5 | 60.6 |
| ST-ReID visual | 60.9 | 64.1 |
| MMT | 68.7 | 74.5 |
| ST-ReID fused | 76.0 | 69.9 |
- python 3.7
- Market1501, DukeMTMC-reID and other datasets.
Download all necessary datasets and move them to 'data' by following instructions in 'data/readme.md'
- Other necessary packages listed in requirements.txt
- ACT pre-trained models
Download models from Baidu NetDisk (Password: 9aba) or Google Drive. Models are named by the following formula:
ada{src}2{tgt}.pthwhere "src" and "tgt" are the initial letter of source and target dataset's name.
CUDA_VISIBLE_DEVICES=0,1,2 python selftrainingBayes.py --src_dataset market1501 --tgt_dataset dukemtmc --resume ACT_pretrain/logMar/adaM2D.pth --data_dir ./data --logs_dir ./log/M2D > out.log
avaliable choices to fill "src_dataset_name" and "tgt_dataset_name" are: market1501 (for Market1501), dukemtmc (for DukeMTMC-reID), cuhk03 (for CUHK03).
selftraining*.py 文件各代表一种训练算法，现已有 ACT、PAST-ReID的复现，不同分类 loss 或 triplet loss 的对比，改用hough变换作为时空约束等。
实现算法时，除了 selftraining*.py，主要改动还有 reid/trainers.py, reid/loss/*.py, reid/utils/data/sampler.py
test_*.ipynb 是对时空模型的具体测试，包括对时空转移概率分布的可视化、三元组选取准确率的计算、重排序方法的对比等。