# IndGIC: Supervised Action Recognition under Low Illumination

arXiv: https://arxiv.org/abs/2308.15345 · HTML: https://mlsanity.com/paper/2308.15345/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning](https://mlsanity.com/paper/2201.04676/) (2022)
- [TEA: Temporal Excitation and Aggregation for Action Recognition](https://mlsanity.com/paper/2004.01398/) (2020)
- [Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation](https://mlsanity.com/paper/2003.13045/) (2020)
- [MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask](https://mlsanity.com/paper/2003.10955/) (2020)
- [PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding](https://mlsanity.com/paper/1703.07475/) (2017)
- [SMART Frame Selection for Action Recognition](https://mlsanity.com/paper/2012.10671/) (2020)
- [Deep Equilibrium Optical Flow Estimation](https://mlsanity.com/paper/2204.08442/) (2022)
- [UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning](https://mlsanity.com/paper/2012.00212/) (2020)
- [AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures](https://mlsanity.com/paper/1905.13209/) (2019)
- [V4D:4D Convolutional Neural Networks for Video-level Representation Learning](https://mlsanity.com/paper/2002.07442/) (2020)
- [Self-supervising Action Recognition by Statistical Moment and Subspace Descriptors](https://mlsanity.com/paper/2001.04627/) (2020)
- [M&M Mix: A Multimodal Multiview Transformer Ensemble](https://mlsanity.com/paper/2206.09852/) (2022)
- [3D CNNs with Adaptive Temporal Feature Resolutions](https://mlsanity.com/paper/2011.08652/) (2020)
- [NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition](https://mlsanity.com/paper/2207.10388/) (2022)
- [Delta Sampling R-BERT for limited data and low-light action recognition](https://mlsanity.com/paper/2107.05202/) (2021)

## Abstract (arXiv)

Technologies of human action recognition in the dark are gaining more and more attention as huge demand in surveillance, motion control and human-computer interaction. However, because of limitation in image enhancement method and low-lighting video datasets, e.g. labeling cost, existing methods meet some problems. Some video-based approached are effect and efficient in specific datasets but cannot generalize to most cases while others methods using multiple sensors rely heavily to prior knowledge to deal with noisy nature from video stream. In this paper, we proposes action recognition method using deep multi-input network. Furthermore, we proposed a Independent Gamma Intensity Corretion (Ind-GIC) to enhance poor-illumination video, generating one gamma for one frame to increase enhancement performance. To prove our method is effective, there is some evaluation and comparison between our method and existing methods. Experimental results show that our model achieves high accuracy in on ARID dataset.