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

Autor:	Chang, Nadine, Yu, Zhiding, Wang, Yu-Xiong, Anandkumar, Anima, Fidler, Sanja, Alvarez, Jose M.
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Code is available at https://github.com/NVlabs/RIO. Comment: Accepted to ICML 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2104.05702 Zobrazit plný text záznamu View this record from Arxiv