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

Chang, Nadine and Yu, Zhiding and Wang, Yu-Xiong and Anandkumar, Animashree and Fidler, Sanja and Alvarez, Jose M. (2021) Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210510-134322482

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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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2104.05702arXivDiscussion Paper
Record Number:CaltechAUTHORS:20210510-134322482
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210510-134322482
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109038
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:10 May 2021 20:56
Last Modified:10 May 2021 20:56

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