# Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition

arXiv: https://arxiv.org/abs/1909.08297 · HTML: https://mlsanity.com/paper/1909.08297/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 13 of 13)

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- [Urban Regional Function Guided Traffic Flow Prediction](https://mlsanity.com/paper/2303.09789/) (2023)
- [Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval](https://mlsanity.com/paper/2101.00820/) (2021)
- [CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning](https://mlsanity.com/paper/2306.17462/) (2023)
- [MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments](https://mlsanity.com/paper/2402.00290/) (2024)
- [Self-supervised Contrastive Learning for Audio-Visual Action Recognition](https://mlsanity.com/paper/2204.13386/) (2022)
- [VCD: Visual Causality Discovery for Cross-Modal Question Reasoning](https://mlsanity.com/paper/2304.08083/) (2023)
- [Adversarial Domain Adaptation for Action Recognition Around the Clock](https://mlsanity.com/paper/2210.17412/) (2022)
- [ActAR: Actor-Driven Pose Embeddings for Video Action Recognition](https://mlsanity.com/paper/2204.08671/) (2022)
- [Dark Transformer: A Video Transformer for Action Recognition in the Dark](https://mlsanity.com/paper/2407.12805/) (2024)

## Abstract (arXiv)

Recently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognition. Motivated by the idea of transfer learning, an infrared human action recognition framework using auxiliary data from visible light is proposed to solve the problem of limited infrared action data. In the proposed framework, we first construct a novel Cross-Dataset Feature Alignment and Generalization (CDFAG) framework to map the infrared data and visible light data into a common feature space, where Kernel Manifold Alignment (KEMA) and a dual alignedto-generalized encoders (AGE) model are employed to represent the feature. Then, a support vector machine (SVM) is trained, using both the infrared data and visible light data, and can classify the features derived from infrared data. The proposed method is evaluated on InfAR, which is a publicly available infrared human action dataset. To build up auxiliary data, we set up a novel visible light action dataset XD145. Experimental results show that the proposed method can achieve state-of-the-art performance compared with several transfer learning and domain adaptation methods.