https://github.com/princeton-vl/SimpleView
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
Learning Transferable Visual Models From Natural Language Supervision
Is Space-Time Attention All You Need for Video Understanding?
Learning Transferable Visual Models From Natural Language Supervision
Is Space-Time Attention All You Need for Video Understanding?
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
RED: Reinforced Encoder-Decoder Networks for Action Anticipation
Learning Transferable Visual Models From Natural Language Supervision
Gimme Signals: Discriminative signal encoding for multimodal activity recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Learning Transferable Visual Models From Natural Language Supervision
Benchmarking Deep Reinforcement Learning for Continuous Control
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Im2Flow: Motion Hallucination from Static Images for Action Recognition
Im2Flow: Motion Hallucination from Static Images for Action Recognition
Im2Flow: Motion Hallucination from Static Images for Action Recognition
Benchmarking Deep Reinforcement Learning for Continuous Control
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.