Papers with Code 아카이브

Temporal Action Localization

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 8
평가표: 14
보존된 평가 기록: 125

Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/mindspore-ai/models/blob/master/research/cv/glore_res200

Graph-Based Global Reasoning Networks

https://github.com/mindspore-ai/models/tree/master/official/cv/vgg16

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mindspore-ai/models/tree/master/official/gnn/gat

Graph Attention Networks

https://github.com/mindspore-ai/models/tree/master/research/cv/r2plus1d

A Closer Look at Spatiotemporal Convolutions for Action Recognition

https://github.com/mindspore-ai/models/tree/master/research/cv/stnet

StNet: Local and Global Spatial-Temporal Modeling for Action Recognition

https://github.com/mindspore-ai/models/tree/master/research/cv/tsm

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/mindspore-ai/models/tree/master/research/cv/tsn

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

https://github.com/mindspore-ai/models/tree/master/research/cv/vgg19

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mindspore-courses/MindSpore-classification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/vgg.py

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/minhncedutw/pointnet1_keras

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

https://github.com/minostauros/VIAR

Unsupervised Learning of View-invariant Action Representations

https://github.com/mitya8128/experiments_notes

Graph Attention Networks

https://github.com/mjhucla/TF-mRNN

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mjhucla/mRNN-CR

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mjoannou/audiovisual-moments-in-time

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mlzxzhou/keras-gnm

Graph Attention Networks

https://github.com/mnecipkurt/tsg19

Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach

https://github.com/moabitcoin/ig65m-pytorch

Large-scale weakly-supervised pre-training for video action recognition

https://github.com/modelhub-ai/vgg-19

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/modestyachts/ImageNetV2

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mohamedameen93/German-Traffic-Sign-Classification-Using-TensorFlow

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mohsenzand/reschunk

Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction

https://github.com/motional/nuplan-devkit

NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

https://github.com/mpuu00001/Siformer

Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition

https://github.com/mr-bulb/DL_basic_github

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mryoo/pooled_time_series

Pooled Motion Features for First-Person Videos

https://github.com/mtlouie-unm/alome-tsn

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

https://github.com/musifahamran/FYP

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/musikisomorphie/swd

Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs

https://github.com/myc159/Deep-Taylor-Decomposition

Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

https://github.com/mzolfaghari/chained-multistream-networks

Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection

https://github.com/nagadomi/kaggle-cifar10-torch7

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/nageshpindi/human-motion-prediction-master

On human motion prediction using recurrent neural networks

https://github.com/nasib-ullah/video-captioning-models-in-Pytorch

Describing Videos by Exploiting Temporal Structure

https://github.com/natepuppy/HCN-pytorch

Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation

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