Papers with Code 아카이브
Action Localization
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Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CVPRUSFTampa/EventSegmentation
공식 코드A Perceptual Prediction Framework for Self Supervised Event Segmentation
https://github.com/Chain123/Meta-HAR
공식 코드Meta-HAR: Federated Representation Learning for Human Activity Recognition
https://github.com/Charrrrrlie/SGA-INTERACT
공식 코드https://github.com/Chiaraplizz/ST-TR
공식 코드Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks
https://github.com/Cogito2012/CarCrashDataset
공식 코드Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning
https://github.com/Cogito2012/OpenTAL
공식 코드https://github.com/Cogito2012/UString
공식 코드Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning
https://github.com/DaphneAntotsiou/task-oriented-hand-retargeting
공식 코드Task-Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation
https://github.com/DmZhukov/CrossTask
공식 코드Cross-task weakly supervised learning from instructional videos
https://github.com/DrMahdiRezaei/DeepSOCIAL
공식 코드DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
https://github.com/Droliven/DHMP_jittor
공식 코드Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space
https://github.com/Droliven/MSRGCN
공식 코드MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction
https://github.com/EHZ9NIWI7/MSF-GZSSAR
공식 코드Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action Recognition
https://github.com/ENSCMA2/flextape
공식 코드"Flex Tape Can't Fix That": Bias and Misinformation in Edited Language Models
https://github.com/Efstathia-Soufleri/PKD
공식 코드Advancing Compressed Video Action Recognition through Progressive Knowledge Distillation
https://github.com/FingerRec/Self-Supervised-Temporal-Discriminative-Representation-Learning-for-Video-Action-Recognition
공식 코드Self-supervised Temporal Discriminative Learning for Video Representation Learning
https://github.com/Flowerfan/SF-Net
공식 코드SF-Net: Single-Frame Supervision for Temporal Action Localization
https://github.com/Frostinassiky/gtad
공식 코드https://github.com/FuchenUSTC/AherNet
공식 코드https://github.com/GUO-W/MultiMotion
공식 코드https://github.com/Garfield-kh/TM2D
공식 코드TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration
https://github.com/Gauffret/TrajectorySet
공식 코드Revisiting hand-crafted feature for action recognition: a set of improved dense trajectories
https://github.com/Gbouna/RE-TCN
공식 코드Action Recognition in Real-World Ambient Assisted Living Environment
https://github.com/GuansongPang/deviation-network
공식 코드https://github.com/HanLingsgjk/CSCV
공식 코드ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video
https://github.com/Hasnat79/UAL_Bench
공식 코드UAL-Bench: The First Comprehensive Unusual Activity Localization Benchmark
https://github.com/HsinYingLee/OPN
공식 코드https://github.com/HuCui2022/DSTSA-GCN_Gesture
공식 코드https://github.com/HumamAlwassel/DETAD
공식 코드https://github.com/HumamAlwassel/RefineLoc
공식 코드RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization
https://github.com/HumamAlwassel/TSP
공식 코드TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks
https://github.com/HumamAlwassel/action-search
공식 코드Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization
https://github.com/IBM/action-recognition-pytorch
공식 코드Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
https://github.com/IBM/bLVNet-TAM
공식 코드https://github.com/IIT-PAVIS/subspace-clustering-action-recognition
공식 코드Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.