Region-Based Representations Revisited
Michal Shlapentokh-Rothman, Ansel Blume, Yao Xiao, Yuqun Wu, Sethuraman TV, Heyi Tao, Jae Yong Lee, Wilfredo Torres
Abstract
We investigate whether region-based representations are effective for recognition. Regions were once a mainstay in recognition approaches but pixel and patch-based features are now used almost exclusively. We show that recent class-agnostic segmenters like SAM can be effectively combined with strong unsupervised representations like DINOv2 and used for a wide variety of tasks including semantic segmentation object-based image retrieval and multi-image analysis. Once the masks and features are extracted these representations even with linear decoders enable competitive performance making them well suited to applications that require custom queries. The compactness of the representation also makes it well-suited to video analysis and other problems requiring inference across many images.
BibTeX
@inproceedings{cvpr2024_regionbasedrepre,
title = {Region-Based Representations Revisited},
author = {Michal Shlapentokh-Rothman and Ansel Blume and Yao Xiao and Yuqun Wu and Sethuraman TV and Heyi Tao and Jae Yong Lee and Wilfredo Torres and Yu-Xiong Wang and Derek Hoiem},
booktitle = {CVPR 2024},
year = {2024}
}