https://github.com/scu-zjz/forensichub
공식 코드ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 4
- 보존된 평가 기록
- 10
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/scu-zjz/imdlbenco
공식 코드IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization
https://github.com/scut-cszcl/sfusion
공식 코드SFusion: Self-attention based N-to-One Multimodal Fusion Block
https://github.com/sdimi/Step2heart
공식 코드Learning Generalizable Physiological Representations from Large-scale Wearable Data
https://github.com/secure-and-intelligent-systems-lab/semanticvideotransformer
공식 코드End-to-End Semantic Video Transformer for Zero-Shot Action Recognition
https://github.com/seleucia/goca
공식 코드GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning
https://github.com/sengopal/cv-activity-classification
공식 코드Multi class activity classification in videos using Motion History Image generation
https://github.com/seohongpark/fql
공식 코드https://github.com/seonokkim/3dyoga90
공식 코드3DYoga90: A Hierarchical Video Dataset for Yoga Pose Understanding
https://github.com/sez-harn/sez-harn
공식 코드SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition Network
https://github.com/sfadingz/tse_gcn
공식 코드Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition
https://github.com/shahroudy/NTURGB-D
공식 코드NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
https://github.com/shahzadnit/ez-clip
공식 코드https://github.com/shanakarg/stjd-spatio-temporal-joint-density-driven-learning-for-skeleton-based-action-recognition
공식 코드Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition
https://github.com/shanice-l/st-cubism
공식 코드Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
https://github.com/shankarp8/knowledge_distillation
공식 코드https://github.com/shark0-0/vg4d
공식 코드https://github.com/shash29-dev/STEP
공식 코드DeepSTEP: Simultaneous Tracking and Estimation of Pose for Animals and Humans
https://github.com/shibshib/Moe-VRD
공식 코드https://github.com/shininglab/text-editing-as-imitation-game
공식 코드https://github.com/shiyemin/shuttlenet
공식 코드Learning long-term dependencies for action recognition with a biologically-inspired deep network
https://github.com/shiyin-lc/dd-gcn
공식 코드DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action Recognition
https://github.com/shngjz/wildcamera
공식 코드Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
https://github.com/showlab/egovlp
공식 코드https://github.com/shubham-goel/4D-Humans
공식 코드Humans in 4D: Reconstructing and Tracking Humans with Transformers
https://github.com/signal-park/resolution-adaptive-spectrogram
공식 코드Resolution-Adaptive Micro-Doppler Spectrogram for Human Activity Recognition
https://github.com/signetlabdei/sharp
공식 코드https://github.com/silentview/emcid
공식 코드https://github.com/sislab-unitn/dyknow
공식 코드DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs
https://github.com/sitzikbs/3dincaction
공식 코드https://github.com/skelemoa/mugl
공식 코드MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion
https://github.com/skelemoa/ntu-x
공식 코드NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human Actions
https://github.com/skelemoa/psumnet
공식 코드PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action Recognition
https://github.com/skelemoa/quovadis
공식 코드https://github.com/skelemoa/synse-zsl
공식 코드Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition
https://github.com/skelemoa/tal-hmo
공식 코드Hear Me Out: Fusional Approaches for Audio Augmented Temporal Action Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.