https://github.com/Sunnydreamrain/IndRNN
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
Few-shot Action Recognition with Permutation-invariant Attention
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Is Space-Time Attention All You Need for Video Understanding?
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Learning Spatiotemporal Features with 3D Convolutional Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Masked Feature Prediction for Self-Supervised Visual Pre-Training
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.