Object State Change Classification
6 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Object State Change Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Object State Change Classification models and implementations
Ego4D
Most implemented papers
Egocentric Video-Language Pretraining
showlab/egovlp
•
•
Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention.
EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
Masked Video Autoencoder (MVA) approaches have demonstrated their potential by significantly outperforming previous video representation learning methods.
Egocentric Video-Language Pretraining @ Ego4D Challenge 2022
showlab/egovlp
•
•
In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural Language Query (NLQ), Moment Query (MQ), Object State Change Classification (OSCC), and PNR Localization (PNR).
Object State Change Classification in Egocentric Videos using the Divided Space-Time Attention Mechanism
md-mohaiminul/objectstatechange
•
•
This report describes our submission called "TarHeels" for the Ego4D: Object State Change Classification Challenge.
Learning State-Aware Visual Representations from Audible Interactions
HimangiM/RepLAI
•
•
However, learning representations from videos can be challenging.
Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022
In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4D Challenge 2022.
