Action Understanding

15 papers with code • 1 benchmarks • 4 datasets

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Most implemented papers

YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos

AIM3-RUC/YouMakeup_Baseline 12 Apr 2020

The goal of the YouMakeup VQA Challenge 2020 is to provide a common benchmark for fine-grained action understanding in domain-specific videos e. g. makeup instructional videos.

Detailed 2D-3D Joint Representation for Human-Object Interaction

DirtyHarryLYL/DJ-RN CVPR 2020

In light of these, we propose a detailed 2D-3D joint representation learning method.

Online Spatiotemporal Action Detection and Prediction via Causal Representations

sahasuman/bmvc2016_code 31 Aug 2020

In this thesis, we focus on video action understanding problems from an online and real-time processing point of view.

Video Action Understanding

hutch-matt/vau-tutorial 13 Oct 2020

Many believe that the successes of deep learning on image understanding problems can be replicated in the realm of video understanding.

Temporal Relational Modeling with Self-Supervision for Action Segmentation

redwang/DTGRM 14 Dec 2020

The main reason is that large number of nodes (i. e., video frames) makes GCNs hard to capture and model temporal relations in videos.

Win-Fail Action Recognition

ParitoshParmar/Win-Fail-Action-Recognition 15 Feb 2021

We introduce a first of its kind paired win-fail action understanding dataset with samples from the following domains: "General Stunts," "Internet Wins-Fails," "Trick Shots," and "Party Games."

Home Action Genome: Cooperative Compositional Action Understanding

nishantrai18/homage CVPR 2021

However, there remains a lack of studies that extend action composition and leverage multiple viewpoints and multiple modalities of data for representation learning.

PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging

reyuwei/PIANO_model 21 Jun 2021

Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare.

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.