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, ie. 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.
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Learning with label noise for image retrieval by selecting interactions
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| Sarah Ibrahimi, Arnaud Sors, Rafael Sampaio De Rezende, Stéphane Clinchant |
| Winter Conference on Applications of Computer Vision (WACV), Hawaii, USA, 4-8 January, 2022 |
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).
@InProceedings{Ibrahimi_2022_WACV, author = {Ibrahimi, Sarah and Sors, Arnaud and de Rezende, Rafael Sampaio and Clinchant, St\’ephane}, title = {Learning With Label Noise for Image Retrieval by Selecting Interactions}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {2181-2190} }
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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