# Cross-view Action Recognition via Contrastive View-invariant Representation

arXiv: https://arxiv.org/abs/2305.01733 · HTML: https://mlsanity.com/paper/2305.01733/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 68 references that are in the map.
- First posted on arXiv 2 May 2023.

## Builds on (most cited first, 30 of 31)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [A Short Note about Kinetics-600](https://mlsanity.com/paper/1808.01340/) (2018)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Cross-view Action Modeling, Learning and Recognition](https://mlsanity.com/paper/1405.2941/) (2014)
- [A New Representation of Skeleton Sequences for 3D Action Recognition](https://mlsanity.com/paper/1703.03492/) (2017)
- [A Short Note on the Kinetics-700-2020 Human Action Dataset](https://mlsanity.com/paper/2010.10864/) (2020)
- [View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition](https://mlsanity.com/paper/1804.07453/) (2018)
- [PYSKL: Towards Good Practices for Skeleton Action Recognition](https://mlsanity.com/paper/2205.09443/) (2022)
- [Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning](https://mlsanity.com/paper/2108.01959/) (2021)
- [Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates](https://mlsanity.com/paper/1706.08276/) (2017)
- [Synthetic Humans for Action Recognition from Unseen Viewpoints](https://mlsanity.com/paper/1912.04070/) (2019)
- [Unsupervised Learning of Long-Term Motion Dynamics for Videos](https://mlsanity.com/paper/1701.01821/) (2017)
- [VPN++: Rethinking Video-Pose embeddings for understanding Activities of Daily Living](https://mlsanity.com/paper/2105.08141/) (2021)
- [Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points](https://mlsanity.com/paper/1802.07898/) (2018)
- [Unsupervised Learning of View-invariant Action Representations](https://mlsanity.com/paper/1809.01844/) (2018)
- [Learning an Augmented RGB Representation with Cross-Modal Knowledge Distillation for Action Detection](https://mlsanity.com/paper/2108.03619/) (2021)
- [GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition](https://mlsanity.com/paper/2111.13089/) (2021)
- [Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos](https://mlsanity.com/paper/1603.07120/) (2016)
- [Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks](https://mlsanity.com/paper/1702.08652/) (2017)
- [Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition](https://mlsanity.com/paper/1809.00421/) (2018)
- [Learning a Deep Model for Human Action Recognition from Novel Viewpoints](https://mlsanity.com/paper/1602.00828/) (2016)
- [Skeleton-based Action Recognition via Adaptive Cross-Form Learning](https://mlsanity.com/paper/2206.15085/) (2022)
- [DYAN: A Dynamical Atoms-Based Network for Video Prediction](https://mlsanity.com/paper/1803.07201/) (2018)
- [Adding Attentiveness to the Neurons in Recurrent Neural Networks](https://mlsanity.com/paper/1807.04445/) (2018)

## Built on by (most cited first, 1 of 1)

- [Cross-View Cross-Modal Unsupervised Domain Adaptation for Driver Monitoring System](https://mlsanity.com/paper/2511.12196/) (2025)

## Abstract (arXiv)

Cross view action recognition (CVAR) seeks to recognize a human action when observed from a previously unseen viewpoint. This is a challenging problem since the appearance of an action changes significantly with the viewpoint. Applications of CVAR include surveillance and monitoring of assisted living facilities where is not practical or feasible to collect large amounts of training data when adding a new camera. We present a simple yet efficient CVAR framework to learn invariant features from either RGB videos, 3D skeleton data, or both. The proposed approach outperforms the current state-of-the-art achieving similar levels of performance across input modalities: 99.4% (RGB) and 99.9% (3D skeletons), 99.4% (RGB) and 99.9% (3D Skeletons), 97.3% (RGB), and 99.2% (3D skeletons), and 84.4%(RGB) for the N-UCLA, NTU-RGB+D 60, NTU-RGB+D 120, and UWA3DII datasets, respectively.