https://github.com/aikuniverse/tcte-net
공식 코드Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition
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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.
Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition
Gimme Signals: Discriminative signal encoding for multimodal activity recognition
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning
PREGO: online mistake detection in PRocedural EGOcentric videos
A Bag-of-Words Equivalent Recurrent Neural Network for Action Recognition
Multi-Temporal Convolutions for Human Action Recognition in Videos
Learn to cycle: Time-consistent feature discovery for action recognition
3DVNet: Multi-View Depth Prediction and Volumetric Refinement
Hybrid Relation Guided Set Matching for Few-shot Action Recognition
CLIP-guided Prototype Modulating for Few-shot Action Recognition
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition
MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition
TAda! Temporally-Adaptive Convolutions for Video Understanding
Self-supervised Video Representation Learning by Context and Motion Decoupling
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
Learning from Failure: Training Debiased Classifier from Biased Classifier
Time Is MattEr: Temporal Self-supervision for Video Transformers
Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models
MaCLR: Motion-aware Contrastive Learning of Representations for Videos
Online Action Recognition for Human Risk Prediction with Anticipated Haptic Alert via Wearables
MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory
Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.