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Spatio-Temporal Action Localization
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Minimalistic Video Saliency Prediction via Efficient Decoder & Spatio Temporal Action Cues
Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives
End-to-End Spatio-Temporal Action Localisation with Video Transformers
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
Unmasked Teacher: Towards Training-Efficient Video Foundation Models
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
E^2TAD: An Energy-Efficient Tracking-based Action Detector
MM-SEAL: A Large-scale Video Dataset of Multi-person Multi-grained Spatio-temporally Action Localization
Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
KORSAL: Key-point Detection based Online Real-Time Spatio-Temporal Action Localization
Relation Modeling in Spatio-Temporal Action Localization
ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos
Relevance Detection in Cataract Surgery Videos by Spatio-Temporal Action Localization
Unsupervised Domain Adaptation for Spatio-Temporal Action Localization
CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization
1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
Video action detection by learning graph-based spatio-temporal interactions
Three Branches: Detecting Actions With Richer Features
Submission to ActivityNet Challenge 2019: Task B Spatio-temporal Action Localization
Trimmed Action Recognition, Dense-Captioning Events in Videos, and Spatio-temporal Action Localization with Focus on ActivityNet Challenge 2019
Improving Action Localization by Progressive Cross-stream Cooperation
Spatio-Temporal Action Localization in a Weakly Supervised Setting
Actor-Centric Relation Network
Spatio-Temporal Instance Learning: Action Tubes from Class Supervision
YH Technologies at ActivityNet Challenge 2018
Modeling Spatio-Temporal Human Track Structure for Action Localization
Action Tubelet Detector for Spatio-Temporal Action Localization
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection
Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection
Learning to track for spatio-temporal action localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.