https://github.com/maoyunyao/mamp
공식 코드Masked Motion Predictors are Strong 3D Action Representation Learners
Papers with Code 아카이브
Action Detection
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Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/marcocominelli/csi-vae
공식 코드Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data
https://github.com/marianne-m/brouhaha-vad
공식 코드https://github.com/mariusbock/context_har
공식 코드DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition
https://github.com/mariusbock/dl-for-har
공식 코드https://github.com/mariusbock/dl-for-har/tree/main/tutorial_notebooks
공식 코드https://github.com/mariusbock/leakage_har
공식 코드Label Leakage in Federated Inertial-based Human Activity Recognition
https://github.com/mariusbock/tal_for_har
공식 코드Temporal Action Localization for Inertial-based Human Activity Recognition
https://github.com/mariusbock/weak_har
공식 코드Weak-Annotation of HAR Datasets using Vision Foundation Models
https://github.com/mariusbock/wear
공식 코드WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition
https://github.com/marthadais/aisclassification
공식 코드https://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/mattia-brt/fine_tuning_llm
공식 코드Optimizing Large Language Models for ESG Activity Detection in Financial Texts
https://github.com/maxxu05/relcon
공식 코드RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data
https://github.com/mcg-nju/basictad
공식 코드BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection
https://github.com/mcg-nju/evad
공식 코드Efficient Video Action Detection with Token Dropout and Context Refinement
https://github.com/mcg-nju/pointtad
공식 코드PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points
https://github.com/mcg-nju/vit-tad
공식 코드Adapting Short-Term Transformers for Action Detection in Untrimmed Videos
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/mehrdadfazli/HHAR-Net
공식 코드HHAR-net: Hierarchical Human Activity Recognition using Neural Networks
https://github.com/merlresearch/tssep
공식 코드TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings
https://github.com/metrics-lab/st-fmri
공식 코드Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
https://github.com/mgciszewski/improving_state_estimation_2022
공식 코드https://github.com/microsoft/SGN
공식 코드Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
https://github.com/microsoft/View-Adaptive-Neural-Networks-for-Skeleton-based-Human-Action-Recognition
공식 코드https://github.com/microsoft/robustlearn
공식 코드Out-of-Distribution Representation Learning for Time Series Classification
https://github.com/micts/acgcn
공식 코드https://github.com/midas-research/audino
공식 코드https://github.com/mims-harvard/tfc-pretraining
공식 코드Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
https://github.com/mindspore-lab/models
공식 코드No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding
https://github.com/mkunes/w2v2_audioframeclassification
공식 코드Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using wav2vec 2.0
https://github.com/ml-postech/skeleton-anonymization
공식 코드https://github.com/mlgig/amee
공식 코드Robust Explainer Recommendation for Time Series Classification
https://github.com/mmalekzadeh/dana
공식 코드DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.