Fine-grained Action Recognition
14 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Fine-grained Action Recognition
MTL-AQA
Latest papers with no code
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction
Considering that the different outcomes are closely connected to the subtle differences in actions, fine-grained action recognition is a practical method for action outcome prediction.
Learning Discriminative Motion Features Through Detection
Our network learns to spatially sample features from Frame B in order to maximize pose detection accuracy in Frame A.
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition
Based on the findings from the model interpretation analysis, we propose a targeted refinement technique, which can generalize to various other recognition models.
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition
The HOK descriptors are then generated from the higher-order co-occurrences of these feature maps, and are then used as input to a video-level classifier.
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation
We propose a model for action segmentation which combines low-level spatiotemporal features with a high-level segmental classifier.
Interaction Part Mining: A Mid-Level Approach for Fine-Grained Action Recognition
Secondly, these object regions are matched and tracked across frames to form a large spatio-temporal graph based on the appearance matching and the dense motion trajectories through them.
