Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/pathonai/litewebagent
공식 코드LiteWebAgent: The Open-Source Suite for VLM-Based Web-Agent Applications
https://github.com/patrickltobing/cyclevae-vc-neuralvoco
공식 코드https://github.com/pedro-dm-gomes/agar
공식 코드AGAR: Attention Graph-RNN for Adaptative Motion Prediction of Point Clouds of Deformable Objects
https://github.com/pengwei-iie/cogintac
공식 코드https://github.com/perathambkk/lipreading
공식 코드SpotFast Networks with Memory Augmented Lateral Transformers for Lipreading
https://github.com/percom-submission/PerCom-2023
공식 코드Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition
https://github.com/pha123661/SA-DVAE
공식 코드https://github.com/philipph77/ts-moco
공식 코드TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
https://github.com/philipposg/osdd
공식 코드Detecting Object States vs Detecting Objects: A New Dataset and a Quantitative Experimental Study
https://github.com/pic4ser/act
공식 코드Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition
https://github.com/pic4ser/mpose2021
공식 코드Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition
https://github.com/pic4sercentre/mpose2021
공식 코드Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition
https://github.com/piergiaj/AViD
공식 코드https://github.com/piergiaj/mlb-youtube
공식 코드https://github.com/piergiaj/representation-flow-cvpr19
공식 코드https://github.com/pkorus/neural-imaging
공식 코드https://github.com/pkunlp-icler/ike
공식 코드https://github.com/pmorerio/admd
공식 코드Learning with privileged information via adversarial discriminative modality distillation
https://github.com/polimi-ispl/icpr2020dfdc
공식 코드https://github.com/prabuddha1/acpe
공식 코드https://github.com/prijatelj/human-activity-recognition-in-an-open-world
공식 코드https://github.com/princeton-nlp/calm-textgame
공식 코드Keep CALM and Explore: Language Models for Action Generation in Text-based Games
https://github.com/princeton-nlp/mquake
공식 코드MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
https://github.com/princetonvisualai/DomainBiasMitigation
공식 코드Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
https://github.com/prinshul/GWSDR
공식 코드https://github.com/pritamqu/CrissCross
공식 코드Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity
https://github.com/pritamqu/XKD
공식 코드XKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learning
https://github.com/proteus1991/pscc-net
공식 코드https://github.com/prouast/deep-intake-detection
공식 코드Learning deep representations for video-based intake gesture detection
https://github.com/ptoupas/harflow3d
공식 코드HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices
https://github.com/purduedigitaltwin/m2dar
공식 코드M$^2$DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer
https://github.com/pvtokmakov/video_cluster
공식 코드Unsupervised Learning of Video Representations via Dense Trajectory Clustering
https://github.com/q1xiangchen/vmps
공식 코드https://github.com/qilimk/biggan-am
공식 코드https://github.com/qinghua-zhou/stealth-edits
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.