Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection

• URL: http://arxiv.org/abs/2104.05702v1
• Date: Mon, 12 Apr 2021 17:58:30 GMT
• Title: Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
• Authors: Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Anima Anandkumar, Sanja Fidler, Jose M. Alvarez
• Abstract summary: We show that long-tailed detection differs from classification since multiple classes may be present in one image. We introduce an object-centric memory replay strategy based on dynamic, episodic memory banks. Our method outperforms state-of-the-art long-tailed detection and segmentation methods on LVIS v0.5 across various backbones.
• Score: 114.00301664929911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection. To deal with this challenge, image resampling is typically introduced as a simple but effective approach. However, we observe that long-tailed detection differs from classification since multiple classes may be present in one image. As a result, image resampling alone is not enough to yield a sufficiently balanced distribution at the object level. We address object-level resampling by introducing an object-centric memory replay strategy based on dynamic, episodic memory banks. Our proposed strategy has two benefits: 1) convenient object-level resampling without significant extra computation, and 2) implicit feature-level augmentation from model updates. We show that image-level and object-level resamplings are both important, and thus unify them with a joint resampling strategy (RIO). Our method outperforms state-of-the-art long-tailed detection and segmentation methods on LVIS v0.5 across various backbones.

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