https://github.com/deepintostreams/gcn-devlstm
공식 코드GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition
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/desehuileng0o0/ikem
공식 코드Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision
https://github.com/dginhac/ACDnet
공식 코드https://github.com/di0002ya/rfnet
공식 코드RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognition
https://github.com/dingfengshi/tridet
공식 코드TriDet: Temporal Action Detection with Relative Boundary Modeling
https://github.com/dingfengshi/tridetplus
공식 코드Temporal Action Localization with Enhanced Instant Discriminability
https://github.com/dluvizon/deephar
공식 코드Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognition
https://github.com/dmbee/fcn-core
공식 코드Personalized Activity Recognition with Deep Triplet Embeddings
https://github.com/dot27/temporal-tessellation
공식 코드Temporal Tessellation: A Unified Approach for Video Analysis
https://github.com/dotori-hj/digit
공식 코드https://github.com/dotori-hj/te-tad
공식 코드TE-TAD: Towards Full End-to-End Temporal Action Detection via Time-Aligned Coordinate Expression
https://github.com/dr-pato/ssgd
공식 코드Low-Latency Speech Separation Guided Diarization for Telephone Conversations
https://github.com/echo0125/memory-and-anticipation-transformer
공식 코드Memory-and-Anticipation Transformer for Online Action Understanding
https://github.com/edunlp/nemo-multistage-classroom-diarization
공식 코드https://github.com/egruttadauria98/sspavaldo
공식 코드Online speaker diarization of meetings guided by speech separation
https://github.com/emretaha/siamese-emd-for-amd-change
공식 코드https://github.com/epic-kitchens/epic-kitchens-100-annotations
공식 코드https://github.com/epic-kitchens/epic-kitchens-100-narrator
공식 코드https://github.com/ericssonresearch/uda-4-tsc
공식 코드Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark
https://github.com/ermongroup/Wifi_Activity_Recognition
공식 코드https://github.com/eth-siplab/Finding_Order_in_Chaos
공식 코드Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
https://github.com/event-ahu/hardvs
공식 코드HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
https://github.com/event-ahu/uncertainty_aware_mobileformer
공식 코드Uncertainty-aware Bridge based Mobile-Former Network for Event-based Pattern Recognition
https://github.com/fabienbaradel/glimpse_clouds
공식 코드Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
https://github.com/fabienbaradel/object_level_visual_reasoning
공식 코드https://github.com/facebookresearch/SlowFast
공식 코드https://github.com/fandulu/DD-Net
공식 코드Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/fandulu/asad
공식 코드Actor-identified Spatiotemporal Action Detection --- Detecting Who Is Doing What in Videos
https://github.com/fidsinn/sporttaskme22
공식 코드Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks
https://github.com/firdavsn/SurgicalFeedbackAI
공식 코드Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment
https://github.com/firework8/brl
공식 코드Balanced Representation Learning for Long-tailed Skeleton-based Action Recognition
https://github.com/floft/calda
공식 코드CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning
https://github.com/fmthoker/skeleton-contrast
공식 코드https://github.com/fpetitjean/DBA
공식 코드https://github.com/francescodaghero/ultracompactbnn
공식 코드Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors
https://github.com/frankxu2004/cooking-procedural-extraction
공식 코드A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.