Object-Region Video Transformers

TL;DR

Object-Region Video Transformers (ORViT) introduce an object-centric approach to video understanding by integrating object representations into transformer layers, improving performance across multiple action recognition and detection tasks.

Contribution

The paper proposes ORViT, a novel object-centric extension to video transformers that incorporates object appearance and dynamics early in the network, enhancing spatio-temporal representations.

Findings

Significant performance gains on multiple datasets and tasks.

Effective integration of object regions and dynamics into transformer architecture.

Improved action recognition and detection accuracy.

Abstract

Recently, video transformers have shown great success in video understanding, exceeding CNN performance; yet existing video transformer models do not explicitly model objects, although objects can be essential for recognizing actions. 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 representations starting from early layers and propagate them into the transformer-layers, thus affecting the spatio-temporal representations throughout the network. Our ORViT block consists of two object-level streams: appearance and dynamics. In the appearance stream, an "Object-Region Attention" module 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 four tasks and five datasets: compositional and few-shot action recognition on SomethingElse, spatio-temporal action detection on AVA, and standard action recognition on Something-Something V2, Diving48 and Epic-Kitchen100. We show strong performance improvement 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 \url{https://roeiherz.github.io/ORViT/}