Fine-grained Action Recognition

11 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

ParitoshParmar/MTL-AQA 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

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.

HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

ParitoshParmar/HalluciNet 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.

Revealing Single Frame Bias for Video-and-Language Learning

jayleicn/singularity 7 Jun 2022

Training an effective video-and-language model intuitively requires multiple frames as model inputs.

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.

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%.

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.

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.

Sharing Pain: Using Pain Domain Transfer for Video Recognition of Low Grade Orthopedic Pain 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 clean experimental pain in the orthopedic dataset.

Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning

acewjh/fsfg 15 Aug 2021

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.