Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection

• URL: http://arxiv.org/abs/2304.12161v1
• Date: Mon, 24 Apr 2023 15:14:16 GMT
• Title: Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection
• Authors: Berkan Demirel, Orhun Bu\u{g}ra Baran, Ramazan Gokberk Cinbis
• Abstract summary: Few-shot object detection is an emerging topic in the area of few-shot learning and object detection. We propose a training scheme that allows learning inductive biases that can boost few-shot detection. The proposed approach yields interpretable loss functions, as opposed to highly parametric and complex few-shot meta-models.
• Score: 7.262048441360132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot object detection, the problem of modelling novel object detection categories with few training instances, is an emerging topic in the area of few-shot learning and object detection. Contemporary techniques can be divided into two groups: fine-tuning based and meta-learning based approaches. While meta-learning approaches aim to learn dedicated meta-models for mapping samples to novel class models, fine-tuning approaches tackle few-shot detection in a simpler manner, by adapting the detection model to novel classes through gradient based optimization. Despite their simplicity, fine-tuning based approaches typically yield competitive detection results. Based on this observation, we focus on the role of loss functions and augmentations as the force driving the fine-tuning process, and propose to tune their dynamics through meta-learning principles. The proposed training scheme, therefore, allows learning inductive biases that can boost few-shot detection, while keeping the advantages of fine-tuning based approaches. In addition, the proposed approach yields interpretable loss functions, as opposed to highly parametric and complex few-shot meta-models. The experimental results highlight the merits of the proposed scheme, with significant improvements over the strong fine-tuning based few-shot detection baselines on benchmark Pascal VOC and MS-COCO datasets, in terms of both standard and generalized few-shot performance metrics.

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