# ARID: A New Dataset for Recognizing Action in the Dark

arXiv: https://arxiv.org/abs/2006.03876 · HTML: https://mlsanity.com/paper/2006.03876/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2020.

## Built on by (most cited first, 30 of 43)

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- [EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression](https://mlsanity.com/paper/2204.04138/) (2022)
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- [Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition](https://mlsanity.com/paper/2203.04559/) (2022)
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- [Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment](https://mlsanity.com/paper/2109.09964/) (2021)
- [Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network](https://mlsanity.com/paper/2107.04941/) (2021)
- [The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation](https://mlsanity.com/paper/2308.09139/) (2023)
- [Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2211.10412/) (2022)
- [Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training](https://mlsanity.com/paper/2312.02914/) (2023)
- [Similarity Min-Max: Zero-Shot Day-Night Domain Adaptation](https://mlsanity.com/paper/2307.08779/) (2023)
- [DisMo: Disentangled Motion Representations for Open-World Motion Transfer](https://mlsanity.com/paper/2511.23428/) (2025)
- [Delta Sampling R-BERT for limited data and low-light action recognition](https://mlsanity.com/paper/2107.05202/) (2021)
- [Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation](https://mlsanity.com/paper/2208.05187/) (2022)
- [Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation](https://mlsanity.com/paper/2303.10451/) (2023)
- [Percept, Chat, and then Adapt: Multimodal Knowledge Transfer of Foundation Models for Open-World Video Recognition](https://mlsanity.com/paper/2402.18951/) (2024)
- [Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement](https://mlsanity.com/paper/2405.11478/) (2024)
- [Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation](https://mlsanity.com/paper/2210.03792/) (2022)
- [Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2202.09545/) (2022)
- [Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation](https://mlsanity.com/paper/2204.06187/) (2022)
- [Improving Transfer Learning with a Dual Image and Video Transformer for Multi-label Movie Trailer Genre Classification](https://mlsanity.com/paper/2210.07983/) (2022)
- [Leveraging Content and Context Cues for Low-Light Image Enhancement](https://mlsanity.com/paper/2412.07693/) (2024)
- [Video Domain Incremental Learning for Human Action Recognition in Home Environments](https://mlsanity.com/paper/2412.16946/) (2024)
- [OwlSight: A Robust Illumination Adaptation Framework for Dark Video Human Action Recognition](https://mlsanity.com/paper/2503.23266/) (2025)
- [Co-STAR: Collaborative Curriculum Self-Training with Adaptive Regularization for Source-Free Video Domain Adaptation](https://mlsanity.com/paper/2504.11669/) (2025)
- [Do as we do: Multiple Person Video-To-Video Transfer](https://mlsanity.com/paper/2104.04721/) (2021)
- [Adversarial Domain Adaptation for Action Recognition Around the Clock](https://mlsanity.com/paper/2210.17412/) (2022)
- [Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight](https://mlsanity.com/paper/2212.02053/) (2022)
- [Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation](https://mlsanity.com/paper/2303.10452/) (2023)
- [Unlimited Knowledge Distillation for Action Recognition in the Dark](https://mlsanity.com/paper/2308.09327/) (2023)

## Abstract (arXiv)

The task of action recognition in dark videos is useful in various scenarios, e.g., night surveillance and self-driving at night. Though progress has been made in the action recognition task for videos in normal illumination, few have studied action recognition in the dark. This is partly due to the lack of sufficient datasets for such a task. In this paper, we explored the task of action recognition in dark videos. We bridge the gap of the lack of data for this task by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset. It consists of over 3,780 video clips with 11 action categories. To the best of our knowledge, it is the first dataset focused on human actions in dark videos. To gain further understandings of our ARID dataset, we analyze the ARID dataset in detail and exhibited its necessity over synthetic dark videos. Additionally, we benchmarked the performance of several current action recognition models on our dataset and explored potential methods for increasing their performances. Our results show that current action recognition models and frame enhancement methods may not be effective solutions for the task of action recognition in dark videos.