https://github.com/grassknoted/Modified-Two-Stream-Fusion-for-Real-time-ActionRecognition
공식 코드Real-time Action Recognition for Fine-Grained Actions and The Hand Wash Dataset
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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.
Real-time Action Recognition for Fine-Grained Actions and The Hand Wash Dataset
Beyond the Self: Using Grounded Affordances to Interpret and Describe Others' Actions
Actor and Observer: Joint Modeling of First and Third-Person Videos
Cross-Architecture Self-supervised Video Representation Learning
TransAction: ICL-SJTU Submission to EPIC-Kitchens Action Anticipation Challenge 2021
AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation
Simplifying Open-Set Video Domain Adaptation with Contrastive Learning
SpATr: MoCap 3D Human Action Recognition based on Spiral Auto-encoder and Transformer Network
H-MoRe: Learning Human-centric Motion Representation for Action Analysis
Resolving Lexical Bias in Edit Scoping with Projector Editor Networks
Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNet
Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action Recognition
ActionFormer: Localizing Moments of Actions with Transformers
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences
Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition
PokeMQA: Programmable knowledge editing for Multi-hop Question Answering
Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models
Collaborative Spatio-temporal Feature Learning for Video Action Recognition
Skeleton-based Action Recognition with Convolutional Neural Networks
Multiple Human Tracking using Multi-Cues including Primitive Action Features
Time Sensitive Knowledge Editing through Efficient Finetuning
Pose-aware Attention Network for Flexible Motion Retargeting by Body Part
DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework
Sparse Autoencoders Find Highly Interpretable Features in Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.