Paper Info
Title
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality.
Abstract
Group Activity Recognition detects the activity collectively performed by a group of actors, which requires compositional reasoning of actors and objects. We approach the task by modeling the video as tokens that represent the multi-scale semantic concepts in the video. We propose COMPOSER, a Multiscale Transformer based architecture that performs attention-based reasoning over tokens at each scale and learns group activity compositionally. In addition, prior works suffer from scene biases with privacy and ethical concerns. We only use the keypoint modality which reduces scene biases and prevents acquiring detailed visual data that may contain private or biased information of users. We improve the multiscale representations in COMPOSER by clustering the intermediate scale representations, while maintaining consistent cluster assignments between scales. Finally, we use techniques such as auxiliary prediction and data augmentations tailored to the keypoint signals to aid model training. We demonstrate the model’s strength and interpretability on two widely-used datasets (Volleyball and Collective Activity). COMPOSER achieves up to \(+5.4\%\) improvement with just the keypoint modality (Code is available at https://github.com/hongluzhou/composer.).
Year
DOI
Venue
2022
10.1007/978-3-031-19833-5_15
European Conference on Computer Vision
Keywords
DocType
Citations 
Keypoint-only group activity recognition,Compositionality,Multiscale representations,Transformer,Video understanding
Conference
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Honglu Zhou1163.64
Asim Kadav235617.92
Aviv Shamsian300.34
Shijie Geng400.34
Farley Lai500.34
Long Zhao6306.23
Ting Liu700.68
Mubbasir Kapadia854658.07
Hans Peter Graf900.34