Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection

• URL: http://arxiv.org/abs/2103.17115v1
• Date: Tue, 30 Mar 2021 05:34:49 GMT
• Title: Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection
• Authors: Hanzhe Hu, Shuai Bai, Aoxue Li, Jinshi Cui, Liwei Wang
• Abstract summary: Few-shot object detection is challenging since the fine-grained feature of novel object can be easily overlooked with only a few data available. We propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to tackle the few-shot detection problem.
• Score: 18.04185751827619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional deep learning based methods for object detection require a large amount of bounding box annotations for training, which is expensive to obtain such high quality annotated data. Few-shot object detection, which learns to adapt to novel classes with only a few annotated examples, is very challenging since the fine-grained feature of novel object can be easily overlooked with only a few data available. In this work, aiming to fully exploit features of annotated novel object and capture fine-grained features of query object, we propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to tackle the few-shot detection problem. Built on the meta-learning based framework, Dense Relation Distillation module targets at fully exploiting support features, where support features and query feature are densely matched, covering all spatial locations in a feed-forward fashion. The abundant usage of the guidance information endows model the capability to handle common challenges such as appearance changes and occlusions. Moreover, to better capture scale-aware features, Context-aware Aggregation module adaptively harnesses features from different scales for a more comprehensive feature representation. Extensive experiments illustrate that our proposed approach achieves state-of-the-art results on PASCAL VOC and MS COCO datasets. Code will be made available at https://github.com/hzhupku/DCNet.

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