Learning Compatible Embeddings, ICCV 2021

Related tags

Deep Learning compatibility face-recognition iccv2021
Overview

LCE

Learning Compatible Embeddings, ICCV 2021

by Qiang Meng, Chixiang Zhang, Xiaoqiang Xu and Feng Zhou

Paper: Arxiv

LCE

We cannot release source codes publicly for the interests of the company. This repo (modified from https://github.com/guxinqian/Simple-ReID) are the codes for the ReID experiments as well as core modules/steps. It should be not hard to implement the method in face recognition based on given codes.

Bibtex

@inproceedings{meng2021lce,
  title={Learning Compatible Embeddings},
  author={Meng, Qiang and Zhang, Chixiang and Xu, Xiaoqiang and Zhou, Feng},
  booktitle=ICCV,
  year=2021
}

An example

  1. Prepare the Market1501 dataset
  2. Train the base model by ./scripts/train.sh.
  3. Train the LCE model by .scripts/train_lce.sh.
  4. Evaluate the performances by ./scripts/test.sh.
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Comments
  • Boundary loss for weak model

    Boundary loss for weak model

    Very interesting and useful work, thank you!

    My question is about Boundary loss: You propose to learn transformation from one model to another with intra-class compactness restriction on the mapped embeddings. Let's consider the case when the 2nd model has a lightweight architecture, and consequently provides worse recognition quality compared to the 1st model. From my perspective, in such case it would be hard to learn transformation (2 -> 1) which ensures at least the same intra-class compactness as the 1st model has. Could it be useful to relax boundary loss for models of poor recognition quality?

    Thank you!

    opened by mnikitin 1
Owner
Qiang Meng
Qiang Meng
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