DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot
Object Detection
- URL: http://arxiv.org/abs/2303.09674v1
- Date: Thu, 16 Mar 2023 22:37:09 GMT
- Title: DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot
Object Detection
- Authors: Jiawei Ma, Yulei Niu, Jincheng Xu, Shiyuan Huang, Guangxing Han,
Shih-Fu Chang
- Abstract summary: Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data.
Existing approaches enhance few-shot generalization with the sacrifice of base-class performance.
We propose a new training framework, DiGeo, to learn Geometry-aware features of inter-class separation and intra-class compactness.
- Score: 39.937724871284665
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Generalized few-shot object detection aims to achieve precise detection on
both base classes with abundant annotations and novel classes with limited
training data. Existing approaches enhance few-shot generalization with the
sacrifice of base-class performance, or maintain high precision in base-class
detection with limited improvement in novel-class adaptation. In this paper, we
point out the reason is insufficient Discriminative feature learning for all of
the classes. As such, we propose a new training framework, DiGeo, to learn
Geometry-aware features of inter-class separation and intra-class compactness.
To guide the separation of feature clusters, we derive an offline simplex
equiangular tight frame (ETF) classifier whose weights serve as class centers
and are maximally and equally separated. To tighten the cluster for each class,
we include adaptive class-specific margins into the classification loss and
encourage the features close to the class centers. Experimental studies on two
few-shot benchmark datasets (VOC, COCO) and one long-tail dataset (LVIS)
demonstrate that, with a single model, our method can effectively improve
generalization on novel classes without hurting the detection of base classes.
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