# OwlSight: A Robust Illumination Adaptation Framework for Dark Video Human Action Recognition

arXiv: https://arxiv.org/abs/2503.23266 · HTML: https://mlsanity.com/paper/2503.23266/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 71 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Builds on (most cited first, 30 of 33)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://mlsanity.com/paper/1711.10305/) (2017)
- [ActionCLIP: A New Paradigm for Video Action Recognition](https://mlsanity.com/paper/2109.08472/) (2021)
- [A Short Note on the Kinetics-700 Human Action Dataset](https://mlsanity.com/paper/1907.06987/) (2019)
- [Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement](https://mlsanity.com/paper/2303.06705/) (2023)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [Moments in Time Dataset: one million videos for event understanding](https://mlsanity.com/paper/1801.03150/) (2018)
- [Fine-tuned CLIP Models are Efficient Video Learners](https://mlsanity.com/paper/2212.03640/) (2022)
- [MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video](https://mlsanity.com/paper/2203.00859/) (2022)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer](https://mlsanity.com/paper/2211.09552/) (2022)
- [UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles](https://mlsanity.com/paper/2104.00946/) (2021)
- [Temporal Interlacing Network](https://mlsanity.com/paper/2001.06499/) (2020)
- [ARID: A New Dataset for Recognizing Action in the Dark](https://mlsanity.com/paper/2006.03876/) (2020)
- [Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic Segmentation](https://mlsanity.com/paper/2205.00858/) (2022)
- [Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping](https://mlsanity.com/paper/2007.09187/) (2020)
- [You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement](https://mlsanity.com/paper/2402.05809/) (2024)
- [FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Table](https://mlsanity.com/paper/2308.06749/) (2023)
- [XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition](https://mlsanity.com/paper/2403.01560/) (2024)

## Built on by (most cited first, 2 of 2)

- [DART: Difficulty-Adaptive Routing for Zero-Shot Video Temporal Grounding](https://mlsanity.com/paper/2607.00672/) (2026)
- [StreamTalk: Streaming Co-Speech Gesture Generation with Key-Pose Anchoring](https://mlsanity.com/paper/2608.01643/) (2026)

## Abstract (arXiv)

Human action recognition in low-light environments is crucial for various real-world applications. However, the existing approaches overlook the full utilization of brightness information throughout the training phase, leading to suboptimal performance. To address this limitation, we propose OwlSight, a biomimetic-inspired framework with whole-stage illumination enhancement to interact with action classification for accurate dark video human action recognition. Specifically, OwlSight incorporates a Time-Consistency Module (TCM) to capture shallow spatiotemporal features meanwhile maintaining temporal coherence, which are then processed by a Luminance Adaptation Module (LAM) to dynamically adjust the brightness based on the input luminance distribution. Furthermore, a Reflect Augmentation Module (RAM) is presented to maximize illumination utilization and simultaneously enhance action recognition via two interactive paths. Additionally, we build Dark-101, a large-scale dataset comprising 18,310 dark videos across 101 action categories, significantly surpassing existing datasets (e.g., ARID1.5 and Dark-48) in scale and diversity. Extensive experiments demonstrate that the proposed OwlSight achieves state-of-the-art performance across four low-light action recognition benchmarks. Notably, it outperforms previous best approaches by 5.36% on ARID1.5 and 1.72% on Dark-101, highlighting its effectiveness in challenging dark environments.