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Activity Recognition

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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/dmlc/dgl/tree/master/examples/pytorch/arma

Graph Neural Networks with convolutional ARMA filters

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/sgc

Simplifying Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/sgc

Simplifying Graph Convolutional Networks

https://github.com/doansangg/CGAN-PyTorch

Conditional Generative Adversarial Nets

https://github.com/doc-doc/NExT-OE

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/donghao51/aeo

Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models

https://github.com/donghao51/moosa

Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models

https://github.com/donghao51/simmmdg

Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models

https://github.com/dongzhuoyao/3D-ResNets-PyTorch

Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?

https://github.com/donshen/pointnet.phasedetection

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/doronharitan/human_activity_recognition_LRCN

UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

https://github.com/dreamh1gh/awesome-srl

Visual Semantic Role Labeling

https://github.com/drv-agwl/ViViT-pytorch

ViViT: A Video Vision Transformer

https://github.com/ds4dm/sGat

Graph Attention Networks

https://github.com/ds4dm/sparse-gcn

Graph Attention Networks

https://github.com/duyhlzu/GMG

Unsupervised Learning of Video Representations using LSTMs

https://github.com/dwtstore/sfm1

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/dzb1998/pyGAT

Graph Attention Networks

https://github.com/dzwallkilled/IEforAR

Investigation of Different Skeleton Features for CNN-based 3D Action Recognition

https://github.com/ed-fish/spatio-temporal-contrastive-film

Spatiotemporal Contrastive Video Representation Learning

https://github.com/ed-fish/spatio-temporal-contrastive-video

Spatiotemporal Contrastive Video Representation Learning

https://github.com/ehsannowroozi/RDFS

Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples

https://github.com/eify/open_clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/eknim/gradcamplusplus

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

https://github.com/ekosman/AnomalyDetectionCVPR2018-Pytorch

Real-world Anomaly Detection in Surveillance Videos

https://github.com/eladb3/orvit

Object-Region Video Transformers

https://github.com/eliasboughosn/Spatial-Temporal-Graph-Convolutional-Networks

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/elliottd/compareImageDescriptionMeasures

CIDEr: Consensus-based Image Description Evaluation

https://github.com/emartinezs44/SparkGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/encounter1997/fp-detr

ImageNet-21K Pretraining for the Masses

https://github.com/epic-kitchens/C1-Action-Recognition-TSN-TRN-TSM

Rescaling Egocentric Vision

https://github.com/epic-kitchens/annotations

Scaling Egocentric Vision: The EPIC-KITCHENS Dataset

https://github.com/epic-kitchens/epic-kitchens-55-action-models

An Evaluation of Action Recognition Models on EPIC-Kitchens

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.