https://github.com/mostafa-saad/deep-activity-rec
공식 코드A Hierarchical Deep Temporal Model for Group Activity Recognition
Papers with Code 아카이브
Activity Recognition
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- 11
- 평가표
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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/mpeven/ntu_rgb
공식 코드https://github.com/muyang99/mptsnet
공식 코드https://github.com/mv-lab/youtube8m-19
공식 코드Multi-attention Networks for Temporal Localization of Video-level Labels
https://github.com/mwray/Multi-Verb-Labels
공식 코드https://github.com/mxahan/nev-ncd
공식 코드https://github.com/myuansun/eventrpg
공식 코드EventRPG: Event Data Augmentation with Relevance Propagation Guidance
https://github.com/mzolfaghari/ECO-efficient-video-understanding
공식 코드ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/naraysa/gzsl-od
공식 코드Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition
https://github.com/nardien/agent-distillation
공식 코드Distilling LLM Agent into Small Models with Retrieval and Code Tools
https://github.com/naver-ai/tc-clip
공식 코드Leveraging Temporal Contextualization for Video Action Recognition
https://github.com/navidfoumani/tsc_survey
공식 코드Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
https://github.com/nawidsayed/Cross-and-Learn
공식 코드https://github.com/nazmul-karim170/FIP-Fisher-Backdoor-Removal
공식 코드Fisher Information guided Purification against Backdoor Attacks
https://github.com/nazmul-karim170/NFT-Augmented-Backdoor-Purification
공식 코드Augmented Neural Fine-Tuning for Efficient Backdoor Purification
https://github.com/nbahador/-fine_tuned_gpt2_model_on_epilepsy_related_pubmed_abstracts
공식 코드https://github.com/ncgarcia/DMCL
공식 코드DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
https://github.com/ncgarcia/modality-distillation
공식 코드Modality Distillation with Multiple Stream Networks for Action Recognition
https://github.com/necla-ml/cpr
공식 코드Self-supervised Video Representation Learning with Cascade Positive Retrieval
https://github.com/nervjack2/propertyneurons
공식 코드https://github.com/neufieldrobotics/neuflow
공식 코드NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices
https://github.com/neuroailab/VIE
공식 코드Unsupervised Learning from Video with Deep Neural Embeddings
https://github.com/newsdata/SoccerDB
공식 코드SoccerDB: A Large-Scale Database for Comprehensive Video Understanding
https://github.com/ngk03/dl_ppg_review
공식 코드A Review of Deep Learning Methods for Photoplethysmography Data
https://github.com/nifangbaage/explicit-visual-prompt
공식 코드Explicit Visual Prompting for Low-Level Structure Segmentations
https://github.com/nihaomiao/wacv23_tsnet
공식 코드Cross-identity Video Motion Retargeting with Joint Transformation and Synthesis
https://github.com/ningwang2049/laiar
공식 코드https://github.com/nipdep/SEZ-HARN
공식 코드SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition Network
https://github.com/nishantrai18/CoCon
공식 코드https://github.com/nishantrai18/homage
공식 코드Home Action Genome: Cooperative Compositional Action Understanding
https://github.com/niveck/LLMafia
공식 코드Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
https://github.com/nkatzz/OLED
공식 코드https://github.com/nkatzz/ORL
공식 코드Online Learning Probabilistic Event Calculus Theories in Answer Set Programming
https://github.com/nkliuyifang/me-gcn
공식 코드Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition
https://github.com/nlpkeg/D4S
공식 코드Reasons and Solutions for the Decline in Model Performance after Editing
https://github.com/nmegha2601/anticipatr
공식 코드Rethinking Learning Approaches for Long-Term Action Anticipation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.