# Collaborative Attention Mechanism for Multi-View Action Recognition

arXiv: https://arxiv.org/abs/2009.06599 · HTML: https://mlsanity.com/paper/2009.06599/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Hypergraph-based Multi-View Action Recognition using Event Cameras](https://mlsanity.com/paper/2403.19316/) (2024)
- [Action Selection Learning for Multi-label Multi-view Action Recognition](https://mlsanity.com/paper/2410.03302/) (2024)
- [MultiSensor-Home: A Wide-area Multi-modal Multi-view Dataset for Action Recognition and Transformer-based Sensor Fusion](https://mlsanity.com/paper/2504.02287/) (2025)
- [Multi-view knowledge distillation transformer for human action recognition](https://mlsanity.com/paper/2303.14358/) (2023)
- [Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey](https://mlsanity.com/paper/2510.09731/) (2025)

## Abstract (arXiv)

Multi-view action recognition (MVAR) leverages complementary temporal information from different views to improve the learning performance. Obtaining informative view-specific representation plays an essential role in MVAR. Attention has been widely adopted as an effective strategy for discovering discriminative cues underlying temporal data. However, most existing MVAR methods only utilize attention to extract representation for each view individually, ignoring the potential to dig latent patterns based on mutual-support information in attention space. To this end, we propose a collaborative attention mechanism (CAM) for solving the MVAR problem in this paper. The proposed CAM detects the attention differences among multi-view, and adaptively integrates frame-level information to benefit each other. Specifically, we extend the long short-term memory (LSTM) to a Mutual-Aid RNN (MAR) to achieve the multi-view collaboration process. CAM takes advantages of view-specific attention pattern to guide another view and discover potential information which is hard to be explored by itself. It paves a novel way to leverage attention information and enhances the multi-view representation learning. Extensive experiments on four action datasets illustrate the proposed CAM achieves better results for each view and also boosts multi-view performance.