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Temporal Action Localization
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Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ybdai7/NAMET-massive-editing
공식 코드NAMET: Robust Massive Model Editing via Noise-Aware Memory Optimization
https://github.com/ycwfs/fmi-tal
공식 코드https://github.com/yellowtownhz/STIGCN
공식 코드Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/yf-li15/DisenHCN
공식 코드DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
https://github.com/yfsong0709/EfficientGCNv1
공식 코드Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
https://github.com/yfsong0709/RA-GCNv2
공식 코드Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition
https://github.com/yfzhang/vehicle-motion-forecasting
공식 코드https://github.com/yhZhai/WSCL
공식 코드Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning
https://github.com/yijieyang23/skelet-gcn
공식 코드Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformation
https://github.com/yilliajing/action-ood
공식 코드https://github.com/yilliajing/skeleton-ood
공식 코드https://github.com/yingsen1/unimd
공식 코드UniMD: Towards Unifying Moment Retrieval and Temporal Action Detection
https://github.com/yjiangcm/lte
공식 코드https://github.com/yjxiong/action-detection
공식 코드A Pursuit of Temporal Accuracy in General Activity Detection
https://github.com/yjxiong/caffe
공식 코드https://github.com/yjxiong/temporal-segment-networks
공식 코드Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/youngzhou1999/smartpretrain
공식 코드SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
https://github.com/youshaamurhij/ofmpnet
공식 코드OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment
https://github.com/yucornetto/CAKES
공식 코드CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks
https://github.com/yuhao-nie/stanford-solar-forecasting-dataset
공식 코드https://github.com/yujieouo/pstl
공식 코드Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences
https://github.com/yujieouo/smie
공식 코드Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization
https://github.com/yujmo/czu_mhad
공식 코드https://github.com/yuxiaochen1103/DG-STA
공식 코드Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention
https://github.com/yuxiaochen1103/Hi-TRS
공식 코드Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning
https://github.com/yyjmjc/temporal-emotion-localization-in-videos
공식 코드https://github.com/yysijie/st-gcn
공식 코드Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/yzqin/dexmv-sim
공식 코드DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
https://github.com/zaamad/Deep-Multilevel-Multimodal-Fusion
공식 코드Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial Sensors
https://github.com/zaamad/Gated-Average-Fusion
공식 코드https://github.com/zahrag/gg3dhar
공식 코드Hierarchical growing grid networks for skeleton based action recognition
https://github.com/zce1112zslx/ike
공식 코드https://github.com/zepingyu0512/arithmetic-mechanism
공식 코드Interpreting Arithmetic Mechanism in Large Language Models through Comparative Neuron Analysis
https://github.com/zepingyu0512/neuron-attribution
공식 코드https://github.com/zeroyuhuang/transformer-patcher
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.