https://github.com/chenxinyan-sg/wifi-csi-sensing-benchmark
공식 코드SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing
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/chenyirui/gim
공식 코드GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization
https://github.com/chihyaoma/Activity-Recognition-with-CNN-and-RNN
공식 코드TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
https://github.com/christianbresciani/nscep-spiking-NN
공식 코드https://github.com/christianll9/deepstay
공식 코드DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision
https://github.com/chuong/cattle_identification_action_recognition
공식 코드https://github.com/clic-lab/ciff
공식 코드Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/cltl/pepper
공식 코드Leolani: a reference machine with a theory of mind for social communication
https://github.com/cofly2014/tsa-mlt
공식 코드Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition
https://github.com/contextkit/mydigitalfootprint
공식 코드MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edge
https://github.com/crashbugger/mindbridge
공식 코드MindBridge: Scalable and Cross-Model Knowledge Editing via Memory-Augmented Modality
https://github.com/cruiseresearchgroup/ESPRESSO
공식 코드ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data
https://github.com/csarron/MobiRNN-EMDL17
공식 코드MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU
https://github.com/cseeyangchen/c2vl
공식 코드https://github.com/csiro-robotics/TCE
공식 코드Temporally Coherent Embeddings for Self-Supervised Video Representation Learning
https://github.com/cvgmi/nestedgrassmann
공식 코드Nested Grassmannians for Dimensionality Reduction with Applications
https://github.com/cvlab-columbia/hyperfuture
공식 코드https://github.com/cybercore-co-ltd/VidConv
공식 코드VidConv: A modernized 2D ConvNet for Efficient Video Recognition
https://github.com/cyfml/opstl
공식 코드Exploring Self-supervised Skeleton-based Action Recognition in Occluded Environments
https://github.com/czh-98/STAR
공식 코드STAR: Skeleton-aware Text-based 4D Avatar Generation with In-Network Motion Retargeting
https://github.com/czhaneva/mst-gcn
공식 코드Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
https://github.com/czhaneva/skelemixclr
공식 코드https://github.com/damnull/lagcn
공식 코드Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition
https://github.com/dandoge/palm
공식 코드https://github.com/danideniz/davishanddataset-events
공식 코드Event-based Vision for Early Prediction of Manipulation Actions
https://github.com/darkszchao/big-little_nn_strategies
공식 코드Big-Little Adaptive Neural Networks on Low-Power Near-Subthreshold Processors
https://github.com/daspraty/stg-gradcam
공식 코드Towards a geometric understanding of Spatio Temporal Graph Convolution Networks
https://github.com/data-iitd/proactive
공식 코드ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences
https://github.com/datamllab/autovideo
공식 코드https://github.com/daveishan/spact
공식 코드SPAct: Self-supervised Privacy Preservation for Action Recognition
https://github.com/daveishan/timebalance
공식 코드https://github.com/davelailai/sparse-st-gcn
공식 코드Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?
https://github.com/davidsvy/fractal_video
공식 코드Pre-training for Action Recognition with Automatically Generated Fractal Datasets
https://github.com/dawei-liang/AudioAR_Research_Codes
공식 코드https://github.com/dbouchabou/Fully-Convolutional-Network-Smart-Homes
공식 코드https://github.com/dbouchabou/Generative-Pretrained-Embedding-and-Hierarchical-Representation-to-Unlock-ADL-Rhythm-in-Smart-Homes
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.