Fine-grained Action Recognition
14 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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MTL-AQA
Latest papers with no code
Understanding Video Transformers via Universal Concept Discovery
Concretely, we seek to explain the decision-making process of video transformers based on high-level, spatiotemporal concepts that are automatically discovered.
Flow Dynamics Correction for Action Recognition
Various research studies indicate that action recognition performance highly depends on the types of motions being extracted and how accurate the human actions are represented.
M$^3$Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition
Due to the scarcity of manually annotated data required for fine-grained video understanding, few-shot fine-grained (FS-FG) action recognition has gained significant attention, with the aim of classifying novel fine-grained action categories with only a few labeled instances.
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition
Graph convolutional networks have been widely used in skeleton-based action recognition.
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition
We design a novel Dynamic Spatio-Temporal Specialization (DSTS) module, which consists of specialized neurons that are only activated for a subset of samples that are highly similar.
Combined CNN Transformer Encoder for Enhanced Fine-grained Human Action Recognition
Fine-grained action recognition is a challenging task in computer vision.
Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021
Based on an existing method for video domain adaptation, i. e., TA3N, we propose to learn hand-centric features by leveraging the hand bounding box information for UDA on fine-grained action recognition.
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications.
Adaptive Recursive Circle Framework for Fine-grained Action Recognition
It inherits the operators and parameters of the original layer but is slightly different in the use of those operators and parameters.
Object Properties Inferring from and Transfer for Human Interaction Motions
In this paper, we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone.
