https://github.com/evm7/ego4dlta-icvae
공식 코드Intention-Conditioned Long-Term Human Egocentric Action Forecasting
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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.
Intention-Conditioned Long-Term Human Egocentric Action Forecasting
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
Learning Spatiotemporal Features with 3D Convolutional Networks
Audio-Visual Instance Discrimination with Cross-Modal Agreement
EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone
On Compositions of Transformations in Contrastive Self-Supervised Learning
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
A Closer Look at Spatiotemporal Convolutions for Action Recognition
Is Space-Time Attention All You Need for Video Understanding?
Video Classification with Channel-Separated Convolutional Networks
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
EgoAdapt: A multi-stream evaluation study of adaptation to real-world egocentric user video
GliTr: Glimpse Transformers with Spatiotemporal Consistency for Online Action Prediction
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
The effectiveness of MAE pre-pretraining for billion-scale pretraining
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
Actor-identified Spatiotemporal Action Detection --- Detecting Who Is Doing What in Videos
Deep Architectures for Content Moderation and Movie Content Rating
Spatiotemporal Residual Networks for Video Action Recognition
Two-Stream Convolutional Networks for Action Recognition in Videos
Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.