Fine-grained Action Recognition
16 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Fine-grained Action Recognition
MTL-AQA
Most implemented papers
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
ParitoshParmar/MTL-AQA
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CVPR 2019
Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality?
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
In this work we propose a novel temporal pose-sequence modeling framework, which can embed the dynamics of 3D human-skeleton joints to a continuous latent space in an efficient manner.
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
The hallucination task is treated as an auxiliary task, which can be used with any other action related task in a multitask learning setting.
Revealing Single Frame Bias for Video-and-Language Learning
Training an effective video-and-language model intuitively requires multiple frames as model inputs.
Analysis of Hand Segmentation in the Wild
In the quest for robust hand segmentation methods, we evaluated the performance of the state of the art semantic segmentation methods, off the shelf and fine-tuned, on existing datasets.
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition
jonmun/MM-SADA-code
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CVPR 2020
We then combine adversarial training with multi-modal self-supervision, showing that our approach outperforms other UDA methods by 3%.
Attention-Based Context Aware Reasoning for Situation Recognition
thilinicooray/context-aware-reasoning-for-sr
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CVPR 2020
However, existing query-based reasoning methods have not considered handling of inter-dependent queries which is a unique requirement of semantic role prediction in SR.
Few-shot Action Recognition with Prototype-centered Attentive Learning
tobyperrett/few-shot-action-recognition
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Extensive experiments on four standard few-shot action benchmarks show that our method clearly outperforms previous state-of-the-art methods, with the improvement particularly significant (10+\%) on the most challenging fine-grained action recognition benchmark.
Sharing Pain: Using Pain Domain Transfer for Video Recognition of Low Grade Orthopedic Pain in Horses
sofiabroome/painface-recognition
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Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset.
Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning
Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications, whereas the data of rare fine-grained categories is very limited.
