Chop & Learn: Recognizing and Generating Object-State Compositions
Nirat Saini Hanyu Wang Archana Swaminathan Vinoj Jayasundara Bo He Kamal Gupta Abhinav Shrivastava
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.