# TinyVIRAT: Low-resolution Video Action Recognition

arXiv: https://arxiv.org/abs/2007.07355 · HTML: https://mlsanity.com/paper/2007.07355/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [End-to-End Semi-Supervised Learning for Video Action Detection](https://mlsanity.com/paper/2203.04251/) (2022)
- [Video Action Detection: Analysing Limitations and Challenges](https://mlsanity.com/paper/2204.07892/) (2022)
- [AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning](https://mlsanity.com/paper/2303.01589/) (2023)
- [PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition](https://mlsanity.com/paper/2304.06866/) (2023)
- [Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images](https://mlsanity.com/paper/2303.10435/) (2023)
- [Percept, Chat, and then Adapt: Multimodal Knowledge Transfer of Foundation Models for Open-World Video Recognition](https://mlsanity.com/paper/2402.18951/) (2024)
- [Low-Resolution Action Recognition for Tiny Actions Challenge](https://mlsanity.com/paper/2209.14711/) (2022)
- [TinyAction Challenge: Recognizing Real-world Low-resolution Activities in Videos](https://mlsanity.com/paper/2107.11494/) (2021)
- [MITFAS: Mutual Information based Temporal Feature Alignment and Sampling for Aerial Video Action Recognition](https://mlsanity.com/paper/2303.02575/) (2023)
- [Performance Evaluation of Action Recognition Models on Low Quality Videos](https://mlsanity.com/paper/2204.09166/) (2022)
- [Everything is a Video: Unifying Modalities through Next-Frame Prediction](https://mlsanity.com/paper/2411.10503/) (2024)
- [Punching Bag vs. Punching Person: Motion Transferability in Videos](https://mlsanity.com/paper/2508.00085/) (2025)
- ["Knights": First Place Submission for VIPriors21 Action Recognition Challenge at ICCV 2021](https://mlsanity.com/paper/2110.07758/) (2021)
- [LARNet: Latent Action Representation for Human Action Synthesis](https://mlsanity.com/paper/2110.10899/) (2021)
- [VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain Generalization](https://mlsanity.com/paper/2404.19652/) (2024)

## Abstract (arXiv)

The existing research in action recognition is mostly focused on high-quality videos where the action is distinctly visible. In real-world surveillance environments, the actions in videos are captured at a wide range of resolutions. Most activities occur at a distance with a small resolution and recognizing such activities is a challenging problem. In this work, we focus on recognizing tiny actions in videos. We introduce a benchmark dataset, TinyVIRAT, which contains natural low-resolution activities. The actions in TinyVIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging. We propose a novel method for recognizing tiny actions in videos which utilizes a progressive generative approach to improve the quality of low-resolution actions. The proposed method also consists of a weakly trained attention mechanism which helps in focusing on the activity regions in the video. We perform extensive experiments to benchmark the proposed TinyVIRAT dataset and observe that the proposed method significantly improves the action recognition performance over baselines. We also evaluate the proposed approach on synthetically resized action recognition datasets and achieve state-of-the-art results when compared with existing methods. The dataset and code is publicly available at https://github.com/UgurDemir/Tiny-VIRAT.