Related papers: The Change You Want to See

The Change You Want to See

URL: http://arxiv.org/abs/2209.14341v1
Date: Wed, 28 Sep 2022 18:10:09 GMT
Title: The Change You Want to See
Authors: Ragav Sachdeva, Andrew Zisserman
Abstract summary: Given two images of the same scene, being able to automatically detect the changes in them has practical applications in a variety of domains. We tackle the change detection problem with the goal of detecting "object-level" changes in an image pair despite differences in their viewpoint and illumination.
Score: 91.3755431537592
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We live in a dynamic world where things change all the time. Given two images of the same scene, being able to automatically detect the changes in them has practical applications in a variety of domains. In this paper, we tackle the change detection problem with the goal of detecting "object-level" changes in an image pair despite differences in their viewpoint and illumination. To this end, we make the following four contributions: (i) we propose a scalable methodology for obtaining a large-scale change detection training dataset by leveraging existing object segmentation benchmarks; (ii) we introduce a co-attention based novel architecture that is able to implicitly determine correspondences between an image pair and find changes in the form of bounding box predictions; (iii) we contribute four evaluation datasets that cover a variety of domains and transformations, including synthetic image changes, real surveillance images of a 3D scene, and synthetic 3D scenes with camera motion; (iv) we evaluate our model on these four datasets and demonstrate zero-shot and beyond training transformation generalization.

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