Papers with Code 아카이브
Action Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/frostinassiky/denoiseloc
공식 코드https://github.com/fuankarion/active-speakers-context
공식 코드https://github.com/fuankarion/end-to-end-asd
공식 코드https://github.com/fuankarion/maas
공식 코드https://github.com/georgesterpu/Taris
공식 코드https://github.com/getalp/FedAli
공식 코드FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal Transport
https://github.com/getalp/PerCom2021-FL
공식 코드A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison
https://github.com/getalp/lightweight-transformer-models-for-har-on-mobile-devices
공식 코드Lightweight Transformers for Human Activity Recognition on Mobile Devices
https://github.com/getalp/self-supervised-learning-har
공식 코드Comparing Self-Supervised Learning Techniques for Wearable Human Activity Recognition
https://github.com/getalp/smartcomp2023-har-supervised-pretraining
공식 코드Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity
https://github.com/githubofhyl97/skeattnclr
공식 코드Part Aware Contrastive Learning for Self-Supervised Action Recognition
https://github.com/gmouyes/seal
공식 코드Semantically Encoding Activity Labels for Context-Aware Human Activity Recognition
https://github.com/google-research/google-research
공식 코드https://github.com/google-research/scenic
공식 코드https://github.com/google/graph_distillation
공식 코드Graph Distillation for Action Detection with Privileged Modalities
https://github.com/gorjanradevski/revisiting-spatial-temporal-layouts
공식 코드Revisiting spatio-temporal layouts for compositional action recognition
https://github.com/gramuah/ia
공식 코드https://github.com/griko/vanpy
공식 코드https://github.com/gsig/temporal-fields
공식 코드https://github.com/guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs
공식 코드Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
https://github.com/gurkirt/3D-RetinaNet
공식 코드ROAD: The ROad event Awareness Dataset for Autonomous Driving
https://github.com/gurkirt/AMTNet
공식 코드https://github.com/gurkirt/actNet-inAct
공식 코드Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge
https://github.com/gurkirt/road-dataset
공식 코드ROAD: The ROad event Awareness Dataset for Autonomous Driving
https://github.com/guxm2021/MM_ALT
공식 코드https://github.com/hamzaamrani/Deep-Inertial-Sensory-Clustering
공식 코드Unsupervised Deep Learning-based clustering for Human Activity Recognition
https://github.com/handhand123/prsa-net
공식 코드Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation
https://github.com/hanielwang/audio-visual-tad
공식 코드Centre Stage: Centricity-based Audio-Visual Temporal Action Detection
https://github.com/hanielwang/refining_boundary_head
공식 코드https://github.com/haohuang2003/sfhead
공식 코드Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action Recognition
https://github.com/harkiratbehl/OJLA
공식 코드https://github.com/heleiqiu/sttformer
공식 코드Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition
https://github.com/helloworld1973/DNA-DA
공식 코드https://github.com/hfawaz/ijcnn19attacks
공식 코드Adversarial Attacks on Deep Neural Networks for Time Series Classification
https://github.com/hikvision-research/skelact
공식 코드Skeleton-based Action Recognition with Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.