1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking

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Deep Learning Winner_ECCV20_TAO
Overview

Code for the winner of TAO 2020

Note:

This repo is NOT a fully reinplementation of our submitted result([email protected]).

Instead, two models for appearance modeling are included, together with the open-source BAGS model and the full set of code for inference. With this code, you can achieve around mAP@23 with TAO test set (based on our estimation).

Start from here:

Please find instructions for the detection model in tao_detection_release/README.md and the tracking model in tao_tracking_release/README.md

License

Apache 2.0

Citation

If you find this code useful, please cite our arxiv tech report:

@article{Du_2020_TAO,
author = {Fei Du and Bo Xu and Jiasheng Tang and Yuqi Zhang and Fan Wang and Hao Li},
title = {1st Place Solution to {ECCV-TAO-2020:} Detect and Represent Any Object for Tracking},
journal = {arXiv preprint arXiv: 2101.08040},
year = {2021},
url = {https://arxiv.org/abs/2101.08040}
}

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Comments
  • Validation set results?

    Validation set results?

    Hey!

    Awesome job winning the TAO challenge @ ECCV20.

    We would love to compare to your method (but using a different evaluation methodology) for our ICCV paper (deadline in 1.5 days).

    In order to do that we would need the result of your method of the TAO validation set.

    Do you just have this on your computer and you could easily share? It would be AWESOME if that's the case.

    We need val because test gt is hidden so we can't run our different evaluation code on it. The test results for your method can be downloaded from MOTCha website but not val.

    Thanks heaps, Jono

    opened by JonathonLuiten 3
Owner
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