https://github.com/maoyunyao/mamp
공식 코드Masked Motion Predictors are Strong 3D Action Representation Learners
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/map-learning/tl2la
공식 코드Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data
https://github.com/mariusbock/context_har
공식 코드DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition
https://github.com/mariusbock/tal_for_har
공식 코드Temporal Action Localization for Inertial-based Human Activity Recognition
https://github.com/mariusbock/wear
공식 코드WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition
https://github.com/martinxm/gap
공식 코드Generative Action Description Prompts for Skeleton-based Action Recognition
https://github.com/martinxm/lst
공식 코드Generative Action Description Prompts for Skeleton-based Action Recognition
https://github.com/mcg-nju/basictad
공식 코드BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection
https://github.com/mcg-nju/mgsampler
공식 코드MGSampler: An Explainable Sampling Strategy for Video Action Recognition
https://github.com/mcg-nju/pointtad
공식 코드PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points
https://github.com/mcgill-nlp/logicalknowedit
공식 코드Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness
https://github.com/mdeff/cnn_graph
공식 코드Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/mduhme/fusion-gcn
공식 코드Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks
https://github.com/meaten/motionaug
공식 코드MotionAug: Augmentation with Physical Correction for Human Motion Prediction
https://github.com/mediabrain-sjtu/auxformer
공식 코드Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction
https://github.com/mediabrain-sjtu/eqmotion
공식 코드EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
https://github.com/mediabrain-sjtu/jrtransformer
공식 코드Joint-Relation Transformer for Multi-Person Motion Prediction
https://github.com/mediabrain-sjtu/spgsn
공식 코드Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
https://github.com/mengyuanchen21/cvpr2022-ftcl
공식 코드Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action Localization
https://github.com/metrics-lab/st-fmri
공식 코드Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
https://github.com/mgiant/2p-gcn
공식 코드Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition
https://github.com/mhnasseri/sort_oh
공식 코드Simple online and real-time tracking with occlusion handling
https://github.com/michigan-traffic-lab/DECODE
공식 코드DECODE: Domain-aware Continual Domain Expansion for Motion Prediction
https://github.com/michigannlp/vlog_action_localization
공식 코드When Did It Happen? Duration-informed Temporal Localization of Narrated Actions in Vlogs
https://github.com/microsoft/SGN
공식 코드Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
https://github.com/microsoft/UniVL
공식 코드UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation
https://github.com/microsoft/View-Adaptive-Neural-Networks-for-Skeleton-based-Human-Action-Recognition
공식 코드https://github.com/microsoft/tensorwatch
공식 코드A System for Real-Time Interactive Analysis of Deep Learning Training
https://github.com/mimbres/SeqSkip
공식 코드Sequential Skip Prediction with Few-shot in Streamed Music Contents
https://github.com/mingu6/action_seg_ot
공식 코드Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation
https://github.com/miulab/editllm-survey
공식 코드https://github.com/mjkwon2021/cat-net
공식 코드Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
https://github.com/mjy1111/bake
공식 코드Untying the Reversal Curse via Bidirectional Language Model Editing
https://github.com/mjy1111/peak
공식 코드Neighboring Perturbations of Knowledge Editing on Large Language Models
https://github.com/mjy1111/prune
공식 코드https://github.com/mkaep/transformer-explainability
공식 코드Attention Please: What Transformer Models Really Learn for Process Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.