Simultaneous Detection and Interaction Reasoning for Object-Centric Action Recognition

TL;DR

This paper introduces an end-to-end object-centric action recognition framework that simultaneously detects objects and reasons about their interactions, improving performance over traditional multi-stage methods.

Contribution

The proposed method jointly performs object detection and interaction reasoning in a single stage, reducing reliance on external detectors and multi-stage training, and enhancing action recognition accuracy.

Findings

Outperforms state-of-the-art on Something-Else and Ikea-Assembly datasets.

Effectively captures interactive objects crucial for action recognition.

Demonstrates robustness in few-shot and compositional action tasks.

Abstract

The interactions between human and objects are important for recognizing object-centric actions. Existing methods usually adopt a two-stage pipeline, where object proposals are first detected using a pretrained detector, and then are fed to an action recognition model for extracting video features and learning the object relations for action recognition. However, since the action prior is unknown in the object detection stage, important objects could be easily overlooked, leading to inferior action recognition performance. In this paper, we propose an end-to-end object-centric action recognition framework that simultaneously performs Detection And Interaction Reasoning in one stage. Particularly, after extracting video features with a base network, we create three modules for concurrent object detection and interaction reasoning. First, a Patch-based Object Decoder generates proposals from video patch tokens. Then, an Interactive Object Refining and Aggregation identifies important objects for action recognition, adjusts proposal scores based on position and appearance, and aggregates object-level info into a global video representation. Lastly, an Object Relation Modeling module encodes object relations. These three modules together with the video feature extractor can be trained jointly in an end-to-end fashion, thus avoiding the heavy reliance on an off-the-shelf object detector, and reducing the multi-stage training burden. We conduct experiments on two datasets, Something-Else and Ikea-Assembly, to evaluate the performance of our proposed approach on conventional, compositional, and few-shot action recognition tasks. Through in-depth experimental analysis, we show the crucial role of interactive objects in learning for action recognition, and we can outperform state-of-the-art methods on both datasets.