Awesome-Online-Action-Detection

KO | EN

gitlite — search

#javascript #hacktoberfest #ai #android #deep-learning #linux #docker #nodejs #cli #kubernetes #awesome-list #security #mcp #open-source

Awesome-Online-Action-Detection

(★ 72)

Awesome Online Action Detection

Awesome-Online-Action-Detection 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Awesome Online Action Detection: [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

### Survey 
- [Survey-2022] [**Online human action detection and anticipation in videos: A survey**](https://doi.org/10.1016/j.neucom.2022.03.069) - Xuejiao Hu et al, `Neurocomputing 2022`.

### 2025 
- [TOAD] [**Text-driven Online Action Detection**](https://arxiv.org/pdf/2501.13518) - Manuel Benavent-Lledo et al, `ArXiv 2025`.

### 2024 
- [OV-OAD] [**Does Video-Text Pretraining Help Open-Vocabulary Online Action Detection?**](https://openreview.net/pdf?id=PWzB2V2b6R) - Qingsong Zhao et al, `NeurIPS 2024`. [[code]](https://github.com/ZQSIAT/OV-OAD)
- [OnlineTAS] [**OnlineTAS: An Online Baseline for Temporal Action Segmentation**](https://arxiv.org/pdf/2411.01122) - Qing Zhong et al, `NeurIPS 2024`. 
- [HAT] [**HAT: History-Augmented Anchor Transformer for Online Temporal Action Localization**](https://arxiv.org/pdf/2408.06437) - Sakib Reza et al, `ECCV 2024`. [[code]](https://github.com/sakibreza/ECCV24-HAT)
- [ActionSwitch] [**ActionSwitch: Class-agnostic Detection of Simultaneous Actions in Streaming Videos**](https://arxiv.org/pdf/2407.12987) - Hyolim Kang et al, `ECCV 2024`.
- [OA-M] [**Object Aware Egocentric Online Action Detection**](https://arxiv.org/pdf/2406.01079) - Joungbin An et al, `ArXiv 2024`.
- [MALT] [**MALT: Multi-scale Action Learning Transformer for Online Action Detection**](https://arxiv.org/pdf/2405.20892) - Zhipeng Yang et al, `ArXiv 2024`.
- [JOADAA] [**JOADAA: joint online action detection and action anticipation**](https://openaccess.thecvf.com/content/WACV2024/papers/Guermal_JOADAA_Joint_Online_Action_Detection_and_Action_Anticipation_WACV_2024_paper.pdf) - Mohammed Guermal et al, `WACV 2024`.

### 2023 
- [MiniROAD] [**MiniROAD: Minimal RNN Framework for Online Action Detection**](https://openaccess.thecvf.com/content/ICCV2023/papers/An_MiniROAD_Minimal_RNN_Framework_for_Online_Action_Detection_ICCV_2023_paper.pdf) - Joungbin An et al, `ICCV 2023`. [[code]](https://github.com/jbistanbul/MiniROAD)
- [E2E-LOAD] [**E2E-LOAD: End-to-End Long-form Online Action Detection**](https://arxiv.org/abs/2306.07703) - Shuqiang Cao et al, `ICCV 2023`. [[code]](https://github.com/sqiangcao99/E2E-LOAD)
- [MAT] [**Memory-and-Anticipation Transformer for Online Action Understanding**](https://arxiv.org/abs/2308.07893) - Jiahao Wang et al, `ICCV 2023`. [[code]](https://github.com/Echo0125/Memory-and-Anticipation-Transformer)
- [Contrastive-based] [**Online Action Detection with Learning Future Representations by Contrastive Learning**](https://ieeexplore.ieee.org/abstract/document/10220027) - Haitao Leng et al, `ICME 2023`.
- [HCM] [**HCM: Online Action Detection With Hard Video Clip Mining**](https://ieeexplore.ieee.org/abstract/document/10246422) - Siyu Liu et al, `TMM 2023`.
- [DFAformer] [**DFAformer: A Dual Filtering Auxiliary Transformer for Efficient Online Action Detection in Streaming Videos**](https://link.springer.com/chapter/10.1007/978-981-99-8537-1_11) - Shicheng Jing et al, `PRCV 2023`.
 
### 2022 
- [SCOAD] [**SCOAD: Single-frame Click Supervision for Online Action Detection**](https://openaccess.thecvf.com/content/ACCV2022/papers/Ye_SCOAD_Single-frame_Click_Supervision_for_Online_Action_Detection_ACCV_2022_paper.pdf) - Na Ye et al, `ACCV 2022`.[[code]](https://github.com/zstarN70/SCOAD)
- [TwinLSTM] [**TwinLSTM: Two-channel LSTM Network for Online Action Detection**](https://www.computer.org/csdl/proceedings-article/icpr/2022/09956717/1IHoVAhk2Ag) - Yunfei Han et al, `ICPR 2022`.
- [Uncertainty-Based] [**Uncertainty-Based Spatial-Temporal Attention for Online Action Detection**](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136640068.pdf) - Hongji Guo et al, `ECCV 2022`.
- [TeSTra] [**Real-time Online Video Detection with Temporal Smoothing Transformers**](https://arxiv.org/abs/2209.09236) - Yue Zhao et al, `ECCV 2022`.[[code]](https://github.com/zhaoyue-zephyrus/TeSTra)
- [CWC-Trans] [**A Circular Window-based Cascade Transformer for Online Action Detection**](https://arxiv.org/abs/2208.14209) - Shuqiang Cao et al, `ArXiv 2022`.
- [GateHUB] [**GateHUB: Gated History Unit with Background Suppression for Online Action Detection**](https://arxiv.org/abs/2206.04668) - Junwen Chen et al, `CVPR 2022`.
- [Colar] [**Colar: Effective and Efficient Online Action Detection by Consulting Exemplars**](https://arxiv.org/abs/2203.01057v2) - Le Yang et al, `CVPR 2022`.[[code]](https://github.com/VividLe/Online-Action-Detection)
- [PPKD] [**Progressive Privileged Knowledge Distillation for Online Action Detection**](https://arxiv.org/abs/2011.09158) - Peisen Zhao et al, `PR 2022`.
- [Continual Transformers] [**Continual Transformers: Redundancy-Free Attention for Online Inference**](https://arxiv.org/abs/2201.06268) - Lukas Hedegaard et al, `ArXiv 2022`.[[code]](https://github.com/lukashedegaard/continual-transformers)

### 2021 
- [OadTR] [**OadTR: Online Action Detection with Transformers**](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_OadTR_Online_Action_Detection_With_Transformers_ICCV_2021_paper.pdf) - Xiang Wang et al, `ICCV 2021`.[[code]](https://github.com/wangxiang1230/OadTR)
- [LSTR] [**Long Short-Term Transformer for Online Action Detection**](https://proceedings.neurips.cc/paper/2021/file/08b255a5d42b89b0585260b6f2360bdd-Paper.pdf) - Mingze Xu et al, `NeurIPS 2021`.[[code]](https://github.com/amazon-research/long-short-term-transformer)
- [TFN] [**Temporal Filtering Networks for Online Action Detection**](https://www.sciencedirect.com/science/article/pii/S0031320320304982) - Hyunjun Eun et al, `PR 2021`.
- [WOAD] [**WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos**](https://openaccess.thecvf.com/content/CVPR2021/html/Gao_WOAD_Weakly_Supervised_Online_Action_Detection_in_Untrimmed_Videos_CVPR_2021_paper.html) - Mingfei Gao et al, `CVPR 2021`.[[code]](https://github.com/salesforce/woad-pytorch)
- [IDN-Extend] [**Learning to Discriminate Information for Online Action Detection: Analysis and Application**](https://arxiv.org/abs/2109.03393) - Sumin Lee et al, `ArXiv 2021`.
- [IED] [**Information Elevation Network for Fast Online Action Detection**](https://arxiv.org/abs/2109.13572) - Sunah Min et al, `ArXiv 2021`.
- [empirical study] [**An empirical study on temporal modeling for online action detection**](https://arxiv.org/abs/2001.07501) - Wen Wang et al, `Complex & Intelligent Systems 2021`.
- [**Exploring Temporal Context and Human Movement Dynamics for Online Action Detection in Videos**](http://cvsp.cs.ntua.gr/publications/confr/Vasileiou_EUSIPCO21_Enhancing_temporal_context_for_online_action_detection_in_videos_Paper.pdf) - Vasiliki I. Vasileiou et al, `EUSIPCO 2021`.
- [FATSnet] [**Temporally Smooth Online Action Detection using Cycle-consistent Future Anticipation**](https://arxiv.org/abs/2104.08030) - Young Hwi Kim et al, `PR 2021`. [[code]](https://github.com/YHKimGithub/FATSnet)

### 2020 
- [IDN] [**Learning to Discriminate Information for Online Action Detection**](https://openaccess.thecvf.com/content_CVPR_2020/papers/Eun_Learning_to_Discriminate_Information_for_Online_Action_Detection_CVPR_2020_paper.pdf) - Hyunjun Eun et al, `CVPR 2020`.[[code]](https://github.com/hjeun/idu)
- [LAP-Net] [**LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection**](https://arxiv.org/abs/2011.07915) - Sanqing Qu et al, `ArXiv 2020`.
- [BF-I3D] [**Online Action Detection in Streaming Videos with Time Buffers**](https://arxiv.org/abs/2011.07915) - Bowen Zhan et al, `ArXiv 2020`.
- [evaluation protocol] [**Rethinking Online Action Detection in Untrimmed Videos: A Novel Online Evaluation Protocol**](https://arxiv.org/abs/2003.12041) - Marcos Baptista Rios et al, `IEEE Access 2020`.[[code]](https://github.com/gramuah/ia)

### 2019 
- [TRN] [**Temporal Recurrent Networks for Online Action Detection**](https://openaccess.thecvf.com/content_ICCV_2019/papers/Xu_Temporal_Recurrent_Networks_for_Online_Action_Detection_ICCV_2019_paper.pdf) - Mingze Xu et al, `ICCV 2019`.[[code]](https://github.com/xumingze0308/TRN.pytorch)
- [StartNet] [**StartNet: Online Detection of Action Start in Untrimmed Videos**](https://openaccess.thecvf.com/content_ICCV_2019/papers/Gao_StartNet_Online_Detection_of_Action_Start_in_Untrimmed_Videos_ICCV_2019_paper.pdf) - Mingfei Gao et al, `ICCV 2019`.

### 2018 
- [**Modeling temporal structure with LSTM for online action detection**](https://ieeexplore.ieee.org/document/8354277) - Roeland De Geest et al, `WACV 2018`.
- [**Online Detection of Action Start in Untrimmed, Streaming Video**](https://openaccess.thecvf.com/content_ECCV_2018/papers/Zheng_Shou_Online_Detection_of_ECCV_2018_paper.pdf) - Zheng Shou et al, `ECCV 2018`.[[code]](https://github.com/junting/odas)
- [HDD Dataset] [**Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning**](https://openaccess.thecvf.com/content_cvpr_2018/papers/Ramanishka_Toward_Driving_Scene_CVPR_2018_paper.pdf) - Vasili Ramanishka et al, `CVPR 2018`.

### 2017 
- [RED] [**RED: Reinforced Encoder-Decoder Networks for Action Anticipation**](http://www.bmva.org/bmvc/2017/papers/paper092/paper092.pdf) - Jiyang Gao et al, `BMVC 2017`.

### 2016 
- [Online action detection & TVseries Dataset] [**Online Action Detection**](https://arxiv.org/abs/1604.06506) - Roeland De Geest et al, `ECCV 2016`.

### Comments are welcome for additional notes！

Awesome-Online-Action-Detection

Awesome-Online-Action-Detection 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Awesome-Online-Action-Detection

Awesome-Online-Action-Detection 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README