Chop & Learn: Recognizing and Generating Object-State Compositions

Nirat Saini, Hanyu Wang, Archana Swaminathan, Vinoj Jayasundara, Bo He, Kamal Gupta, Abhinav Shrivastava; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 20247-20258

Abstract

Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions. In this paper, we study the task of cutting objects in different styles and the resulting object state changes. We propose a new benchmark suite Chop & Learn, to accommodate the needs of learning objects and different cut styles using multiple viewpoints. We also propose a new task of Compositional Image Generation, which can transfer learned cut styles to different objects, by generating novel object-state images. Moreover, we also use the videos for Compositional Action Recognition, and show valuable uses of this dataset for multiple video tasks. Project website: https://chopnlearn.github.io.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Saini_2023_ICCV, author = {Saini, Nirat and Wang, Hanyu and Swaminathan, Archana and Jayasundara, Vinoj and He, Bo and Gupta, Kamal and Shrivastava, Abhinav}, title = {Chop \& Learn: Recognizing and Generating Object-State Compositions}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {20247-20258} }