Learning with Label Noise for Image Retrieval by Selecting Interactions - Naver Labs Europe

Learning with label noise for image retrieval by selecting interactions

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Overview:

Learning with noisy labels is an active research area for image classification. However, the effect of noisy labels on image retrieval has been less studied. In this work, we propose a noise-resistant method for image retrieval named Teacher-based Selection of Interactions, T-SINT, which identifies noisy interactions, i.e. elements in the distance matrix, and selects correct positive and negative interactions to be considered in the retrieval loss by using a teacher-based training setup which contributes to the stability. As a result, it consistently outperforms state-of-the-art methods on high noise rates across benchmark datasets with synthetic noise and more realistic noise.

Image Retrieval Pipeline

  • Model f extracts descriptors.
  • Distance functions obtain a pairwise distance matrix.
  • Loss: Contrastive Margin Loss.
  • Positive interactions between clean samples are likely to have a small distance value.
  • For noisy interactions, the distance value will be larger.

Teacher-based Approach

  • Inspired by knowledge distillation (Mean Teacher).
  • Teacher trained on open-domain images: ViT backbone of CLIP model.
  • Used to estimate the distributions of positive and negative interactions.
  • With the help of a cutting value, we select which percentile of interactions to keep and to discard.
  • Resulting mask will exclude noisy interactions in the loss function.
  • The teacher model is updated to an exponential moving average of the parameters of the main model.
  • The cutting value is updated with a moving average.

Take-home Messages:

T-SINT works on both realistic and simulated label noise.
T-SINT outperforms the state of the of art at low and mid-levels of noise (up to 50% of uniform noise).
T-SINT is the only image retrieval method robust to high levels of label noise (70% of uniform noise).

News:

Paper accepted at Winter Conference on Applications of Computer Vision (WACV) 2022

Bibtex:

@InProceedings{ibrahimi2022tsint,
author = {Ibrahimi, Sarah and Sors, Arnaud and Rezende, Rafael S. and Clinchant, St\’{e}phane},
title = {Learning with Label Noise for Image Retrieval by Selecting Interactions},
booktitle = {WACV},
year = {2022}

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