Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification

• URL: http://arxiv.org/abs/2209.06585v1
• Date: Wed, 14 Sep 2022 12:06:47 GMT
• Title: Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification
• Authors: Kirill Prokofiev and Vladislav Sovrasov
• Abstract summary: We revisit two popular approaches to multilabel classification: transformer-based heads and labels relations information graph processing branches. Although transformer-based heads are considered to achieve better results than graph-based branches, we argue that with the proper training strategy graph-based methods can demonstrate just a small accuracy drop.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-label image classification allows predicting a set of labels from a given image. Unlike multiclass classification, where only one label per image is assigned, such setup is applicable for a broader range of applications. In this work we revisit two popular approaches to multilabel classification: transformer-based heads and labels relations information graph processing branches. Although transformer-based heads are considered to achieve better results than graph-based branches, we argue that with the proper training strategy graph-based methods can demonstrate just a small accuracy drop, while spending less computational resources on inference. In our training strategy, instead of Asymmetric Loss (ASL), which is the de-facto standard for multilabel classification, we introduce its modification acting in the angle space. It implicitly learns a proxy feature vector on the unit hypersphere for each class, providing a better discrimination ability, than binary cross entropy loss does on unnormalized features. With the proposed loss and training strategy, we obtain SOTA results among single modality methods on widespread multilabel classification benchmarks such as MS-COCO, PASCAL-VOC, NUS-Wide and Visual Genome 500. Source code of our method is available as a part of the OpenVINO Training Extensions https://github.com/openvinotoolkit/deep-object-reid/tree/multilabel

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