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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models
Surg-3M: A Dataset and Foundation Model for Perception in Surgical Settings
LLaVAction: evaluating and training multi-modal large language models for action recognition
Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework
Body-Hand Modality Expertized Networks with Cross-attention for Fine-grained Skeleton Action Recognition
LEGION: Learning to Ground and Explain for Synthetic Image Detection
Towards Achieving Perfect Multimodal Alignment
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions
Towards Scalable Modeling of Compressed Videos for Efficient Action Recognition
VA-AR: Learning Velocity-Aware Action Representations with Mixture of Window Attention
SurgRAW: Multi-Agent Workflow with Chain-of-Thought Reasoning for Surgical Intelligence
Natural Humanoid Robot Locomotion with Generative Motion Prior
A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects
ICPR 2024 Competition on Rider Intention Prediction
BiasEdit: Debiasing Stereotyped Language Models via Model Editing
Joint Image-Instance Spatial-Temporal Attention for Few-shot Action Recognition
SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models
Exploiting Edited Large Language Models as General Scientific Optimizers
Multi-Grained Feature Pruning for Video-Based Human Pose Estimation
MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification
Understanding the Limits of Lifelong Knowledge Editing in LLMs
Gate-Shift-Pose: Enhancing Action Recognition in Sports with Skeleton Information
Semi-Supervised Audio-Visual Video Action Recognition with Audio Source Localization Guided Mixup
MindBridge: Scalable and Cross-Model Knowledge Editing via Memory-Augmented Modality
Precise Localization of Memories: A Fine-grained Neuron-level Knowledge Editing Technique for LLMs
Learning to Generate Long-term Future Narrations Describing Activities of Daily Living
A Simple and Efficient Baseline for Video Action Recognition
Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports
QORT-Former: Query-optimized Real-time Transformer for Understanding Two Hands Manipulating Objects
MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge
GeoEdit: Geometric Knowledge Editing for Large Language Models
Learning to Generalize without Bias for Open-Vocabulary Action Recognition
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