Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sming256/OpenTAD
공식 코드Harnessing Temporal Causality for Advanced Temporal Action Detection
https://github.com/sofiabroome/painface-recognition
공식 코드https://github.com/sofiabroome/temporal-shape-dataset
공식 코드https://github.com/soheilb/multimodal_dictionary_learning
공식 코드Multimodal Task-Driven Dictionary Learning for Image Classification
https://github.com/songxiaoshuai/postedit
공식 코드https://github.com/sp-uhh/mp-gtf
공식 코드A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet
https://github.com/sperimental3/charon
공식 코드Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
https://github.com/sprkrd/sat_strips_learn
공식 코드https://github.com/srijandas07/VPN
공식 코드VPN: Learning Video-Pose Embedding for Activities of Daily Living
https://github.com/srijandas07/clip_baseline_lta_ego4d
공식 코드Video + CLIP Baseline for Ego4D Long-term Action Anticipation
https://github.com/srijandas07/vpnplusplus
공식 코드VPN++: Rethinking Video-Pose embeddings for understanding Activities of Daily Living
https://github.com/srvCodes/continual-learning-benchmark
공식 코드Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis
https://github.com/sshao2013/convboost
공식 코드ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition
https://github.com/stan-hua/temporal_hydronephrosis
공식 코드https://github.com/stanfordnlp/pyvene
공식 코드pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
https://github.com/steveliao93/gcn_logsigrnn
공식 코드Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition
https://github.com/stilllearningsoumya/acconotate
공식 코드Accoustate: Auto-annotation of IMU-generated Activity Signatures under Smart Infrastructure
https://github.com/stnoah1/infogcn2
공식 코드https://github.com/stnoah1/sode
공식 코드https://github.com/stoa-xh91/acfl
공식 코드Skeleton-based Action Recognition via Adaptive Cross-Form Learning
https://github.com/stoneMo/YOWOv2
공식 코드Towards Improving Spatiotemporal Action Recognition in Videos
https://github.com/stonybrooknlp/action-anticipation-lmtovideo
공식 코드https://github.com/strohmayerj/datta
공식 코드https://github.com/strohmayerj/wiflexformer
공식 코드https://github.com/suakaw/bdq_privacyar
공식 코드Privacy-Preserving Action Recognition via Motion Difference Quantization
https://github.com/sunilhoho/VideoMS
공식 코드EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
https://github.com/sunilhoho/everest
공식 코드EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
https://github.com/sunzhihao18/ForgerySleuth
공식 코드ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
https://github.com/superz678/mg-had
공식 코드https://github.com/svip-lab/SVIP-Sequence-VerIfication-for-Procedures-in-Videos
공식 코드https://github.com/swathikirans/GSM
공식 코드https://github.com/swathikirans/LSTA
공식 코드LSTA: Long Short-Term Attention for Egocentric Action Recognition
https://github.com/swathikirans/ego-rnn
공식 코드Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition
https://github.com/swathikirans/gsf
공식 코드https://github.com/sycny/rae
공식 코드Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.