Code for "Localization with Sampling-Argmax", NeurIPS 2021

JeffLi

Last update: Dec 17, 2022

Related tags

Deep Learning sampling-argmax

Overview

Localization with Sampling-Argmax

[Paper] [arXiv] [Project Page]

Localization with Sampling-Argmax
Jiefeng Li, Tong Chen, Ruiqi Shi, Yujing Lou, Yong-Lu Li, Cewu Lu
NeurIPS 2021

Differentiable Sampling

Code is coming soon!

If you find our code or paper useful, please consider citing

@inproceedings{li2021localization,
    title={Localization with Sampling-Argmax},
    author={Li, Jiefeng and Chen, Tong and Shi, Ruiqi and Lou, Yujing and Li, Yong-Lu and Lu, Cewu},
    booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
    year={2021}
}

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Comments

I wonder why "training with only sample outperforms soft-argmax"

Thanks for your great work! I've just read your paper and the discussions on OpenReview, and have the same question with the reviewer——

Although it's indicated in "We conduct an experiment of training the model with Eq. 5. The model only obtains 30.9 mAP on COCO Keypoint" that training with discrete density map can not work, I still wonder why sampling (works) and why "training with only sample outperforms soft-argmax"

opened by Indigo6 1
Annealing Schedule

Hello, the paper mentions you use an annealing schedule going from high to low temperature. In the review its mentioned how the method is parameter free, I take it you found a single annealing schedule that worked effectively for the temperature during training? I was wondering since the code is not published yet if you could provide the schedule you used.

On a side note it's great to see more research on soft-argmax! Thank you to all of the authors :clap:

opened by csvance 1
issue？

Hi, in the function of norm_heatmap, it writes gumbel_heatmap = heatmap - log_eps / tau, which is different from gumbel_heatmap = (heatmap - log_eps)/ tau in the gumbel_softmax. Is this matter when training with annealing strategy？

opened by DIVE128 0
why custom the backward() for predicted score_map in this form ?

Thanks for your wonderful work. But I am confused about the custom backward in ClipIntegral, why the derivative of this operation is binarized as {-1, 1} by the comparison between weight and output_coord ?

https://github.com/Jeff-sjtu/sampling-argmax/blob/12c80742b287989705faab60dd6ffff96c7353a6/sampling_argmax/models/simplepose.py#L87

opened by FlorinShum 0

Owner

JeffLi

jeff.lee.sjtu[at]gmail[dot]com

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Code for "Localization with Sampling-Argmax", NeurIPS 2021

Related tags

Overview

Localization with Sampling-Argmax

Code is coming soon!

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Comments

I wonder why "training with only sample outperforms soft-argmax"

Annealing Schedule

issue？

why custom the backward() for predicted score_map in this form ?

Owner

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