Context-Transformer: Tackling Object Confusion for Few-Shot Detection

• URL: http://arxiv.org/abs/2003.07304v1
• Date: Mon, 16 Mar 2020 16:17:11 GMT
• Title: Context-Transformer: Tackling Object Confusion for Few-Shot Detection
• Authors: Ze Yang (1), Yali Wang (1), Xianyu Chen (1), Jianzhuang Liu (2), Yu Qiao (1 and 3) ((1) ShenZhen Key Lab of Computer Vision and Pattern Recognition, SIAT-SenseTime Joint Lab, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, (2) Huawei Noah's Ark Lab, (3) SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society)
• Abstract summary: We propose a novel Context-Transformer within a concise deep transfer framework. Context-Transformer can effectively leverage source-domain object knowledge as guidance. It can adaptively integrate these relational clues to enhance the discriminative power of detector.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches.

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