Fine-grained Action Recognition

8 papers with code • 0 benchmarks • 1 datasets

This task has no description! Would you like to contribute one?


Greatest papers with code

Analysis of Hand Segmentation in the Wild

aurooj/Hand-Segmentation-in-the-Wild CVPR 2018

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.

Fine-grained Action Recognition Hand Segmentation +1

Multi-Modal Domain Adaptation for Fine-Grained Action Recognition

jonmun/MM-SADA-code CVPR 2020

We then combine adversarial training with multi-modal self-supervision, showing that our approach outperforms other UDA methods by 3%.

Fine-grained Action Recognition Optical Flow Estimation +1

Sharing Pain: Using Domain Transfer Between Pain Types for Recognition of Sparse Pain Expressions in Horses

sofiabroome/painface-recognition 21 May 2021

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 acute pain in the orthopedic dataset.

Fine-grained Action Recognition

Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold

maharshi95/Pose2vec 6 Dec 2018

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.

Fine-grained Action Recognition Representation Learning

Attention-Based Context Aware Reasoning for Situation Recognition

thilinicooray/context-aware-reasoning-for-sr 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.

Fine-grained Action Recognition Question Answering +2

Few-shot Action Recognition with Prototype-centered Attentive Learning

tobyperrett/few-shot-action-recognition 20 Jan 2021

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.

Fine-grained Action Recognition Meta-Learning

HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

ParitoshParmar/HalluciNet--PyTorch 10 Dec 2019

The hallucination task is treated as an auxiliary task, which can be used with any other action related task in a multitask learning setting.

Action Anticipation Action Classification +5