https://github.com/microsoft/2D-TAN
Learning Sparse 2D Temporal Adjacent Networks for Temporal Action Localization
Papers with Code 아카이브
Action Localization
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Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/VLM-Video-Action-Localization
Open-Vocabulary Action Localization with Iterative Visual Prompting
https://github.com/microsoft/computervision-recipes
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/mikekestemont/deepscript
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mindspore-ai/models/blob/master/research/cv/glore_res
https://github.com/mindspore-ai/models/blob/master/research/cv/glore_res200
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
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
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
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
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.