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/SuyashSonawane/fakedetector
https://github.com/Sy-Zhang/Geometric-Feature-Release
https://github.com/TAMU-VITA/Privacy-AdversarialLearning
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study
https://github.com/THHHomas/mls
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/TaatiTeam/STARS
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences
https://github.com/TaatiTeam/stgcn_parkinsonism_prediction
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Taeuk-Jang/pointcompletion
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Tangshitao/ClipShots_basline
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/Teddy00888/arn_mindspore
Few-shot Action Recognition with Permutation-invariant Attention
https://github.com/Tencent/ActionDetection-DBG
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/The-AI-Summer/self-attention-cv
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/ThomasDupiereux/ADLproject
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
https://github.com/ThomasDupiereux/EnGAN
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
https://github.com/Ti-Oluwanimi/Violence_Detection
SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence
https://github.com/Tirth27/Skin-Cancer-Classification-using-Deep-Learning
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/TobiasLee/Text-Classification
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/TravisLeeTS/grabcvchallenge
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Trustworthy-AI-Group/TransferAttack
Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping
https://github.com/TsingZ0/FL-IoT
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
https://github.com/Tudouu/stgcn_light_op
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/TyngJiunKuo/deep-learning-project
https://github.com/VEDANTGHODKE/Hand-Gesture-Recognition-Using-Neural-Networks
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/VaanHUANG/CSCI5210HW1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/VinayBhupalam/melanoma-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/VinnyCSY/BHaRNet
https://github.com/VipulDhariwal/eluvio_data_challenge
A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
https://github.com/Visual-AI/FROSTER
FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition
https://github.com/VivekUdupa/CPSC8810-DeepLearning_vkoodli_shonnah
https://github.com/WangChen0902/BSN
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/WangYueFt/dgcnn
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/WantingZhao/my_GAT
https://github.com/WaqasSultani/AnomalyDetectionCVPR2018
https://github.com/WavesUR/embedded_TSM
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/Westlake-AI/openmixup
Masked Feature Prediction for Self-Supervised Visual Pre-Training
https://github.com/Whiffe/Custom-ava-dataset_Custom-Spatio-Temporally-Action-Video-Dataset
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.