Is an Object-Centric Video Representation Beneficial for Transfer?

• URL: http://arxiv.org/abs/2207.10075v1
• Date: Wed, 20 Jul 2022 17:59:44 GMT
• Title: Is an Object-Centric Video Representation Beneficial for Transfer?
• Authors: Chuhan Zhang, Ankush Gupta, Andrew Zisserman
• Abstract summary: We introduce a new object-centric video recognition model on a transformer architecture. We show that the object-centric model outperforms prior video representations.
• Score: 86.40870804449737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The objective of this work is to learn an object-centric video representation, with the aim of improving transferability to novel tasks, i.e., tasks different from the pre-training task of action classification. To this end, we introduce a new object-centric video recognition model based on a transformer architecture. The model learns a set of object-centric summary vectors for the video, and uses these vectors to fuse the visual and spatio-temporal trajectory `modalities' of the video clip. We also introduce a novel trajectory contrast loss to further enhance objectness in these summary vectors. With experiments on four datasets -- SomethingSomething-V2, SomethingElse, Action Genome and EpicKitchens -- we show that the object-centric model outperforms prior video representations (both object-agnostic and object-aware), when: (1) classifying actions on unseen objects and unseen environments; (2) low-shot learning to novel classes; (3) linear probe to other downstream tasks; as well as (4) for standard action classification.

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