https://github.com/opeco17/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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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.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Learning Spatiotemporal Features with 3D Convolutional Networks
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Masked Feature Prediction for Self-Supervised Visual Pre-Training
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Benchmarking Deep Reinforcement Learning for Continuous Control
Sparse Autoencoders Find Highly Interpretable Features in Language Models
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Unsupervised Learning of Object Keypoints for Perception and Control
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
Generate, Segment and Refine: Towards Generic Manipulation Segmentation
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
TEA: Temporal Excitation and Aggregation for Action Recognition
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
Learning Transferable Visual Models From Natural Language Supervision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.