Meta-DETR: Few-Shot Object Detection via Unified Image-Level Meta-Learning

• URL: http://arxiv.org/abs/2103.11731v2
• Date: Thu, 25 Mar 2021 04:54:43 GMT
• Title: Meta-DETR: Few-Shot Object Detection via Unified Image-Level Meta-Learning
• Authors: Gongjie Zhang, Zhipeng Luo, Kaiwen Cui, Shijian Lu
• Abstract summary: Few-shot object detection aims at detecting novel objects with only a few annotated examples. This paper presents a novel meta-detector framework, namely Meta-DETR, which eliminates region-wise prediction. It instead meta-learns object localization and classification at image level in a unified and complementary manner.
• Score: 39.50529982746885
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Few-shot object detection aims at detecting novel objects with only a few annotated examples. Prior works have proved meta-learning a promising solution, and most of them essentially address detection by meta-learning over regions for their classification and location fine-tuning. However, these methods substantially rely on initially well-located region proposals, which are usually hard to obtain under the few-shot settings. This paper presents a novel meta-detector framework, namely Meta-DETR, which eliminates region-wise prediction and instead meta-learns object localization and classification at image level in a unified and complementary manner. Specifically, it first encodes both support and query images into category-specific features and then feeds them into a category-agnostic decoder to directly generate predictions for specific categories. To facilitate meta-learning with deep networks, we design a simple but effective Semantic Alignment Mechanism (SAM), which aligns high-level and low-level feature semantics to improve the generalization of meta-learned representations. Experiments over multiple few-shot object detection benchmarks show that Meta-DETR outperforms state-of-the-art methods by large margins.

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