Object-Region Video Transformers

• URL: http://arxiv.org/abs/2110.06915v1
• Date: Wed, 13 Oct 2021 17:51:46 GMT
• Title: Object-Region Video Transformers
• Authors: Roei Herzig, Elad Ben-Avraham, Karttikeya Mangalam, Amir Bar, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson
• Abstract summary: We present Object-Region Transformers Video (ORViT), an emphobject-centric approach that extends transformer video layers with object representations. Our ORViT block consists of two object-level streams: appearance and dynamics. We show strong improvement in performance across all tasks and considered, demonstrating the value of a model that incorporates object representations into a transformer architecture.
• Score: 100.23380634952083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evidence from cognitive psychology suggests that understanding spatio-temporal object interactions and dynamics can be essential for recognizing actions in complex videos. Therefore, action recognition models are expected to benefit from explicit modeling of objects, including their appearance, interaction, and dynamics. Recently, video transformers have shown great success in video understanding, exceeding CNN performance. Yet, existing video transformer models do not explicitly model objects. In this work, we present Object-Region Video Transformers (ORViT), an \emph{object-centric} approach that extends video transformer layers with a block that directly incorporates object representations. The key idea is to fuse object-centric spatio-temporal representations throughout multiple transformer layers. Our ORViT block consists of two object-level streams: appearance and dynamics. In the appearance stream, an ``Object-Region Attention'' element applies self-attention over the patches and \emph{object regions}. In this way, visual object regions interact with uniform patch tokens and enrich them with contextualized object information. We further model object dynamics via a separate ``Object-Dynamics Module'', which captures trajectory interactions, and show how to integrate the two streams. We evaluate our model on standard and compositional action recognition on Something-Something V2, standard action recognition on Epic-Kitchen100 and Diving48, and spatio-temporal action detection on AVA. We show strong improvement in performance across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a transformer architecture. For code and pretrained models, visit the project page at https://roeiherz.github.io/ORViT/.

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