A Dataset To Evaluate The Representations Learned By Video Prediction Models

02/25/2018
by   Ryan Szeto, et al.
0

We present a parameterized synthetic dataset called Moving Symbols to support the objective study of video prediction networks. Using several instantiations of the dataset in which variation is explicitly controlled, we highlight issues in an existing state-of-the-art approach and propose the use of a performance metric with greater semantic meaning to improve experimental interpretability. Our dataset provides canonical test cases that will help the community better understand, and eventually improve, the representations learned by such networks in the future. Code is available at https://github.com/rszeto/moving-symbols.

READ FULL TEXT
research
05/15/2023

Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings

Languages are dynamic entities, where the meanings associated with words...
research
05/26/2021

DFPN: Deformable Frame Prediction Network

Learned frame prediction is a current problem of interest in computer vi...
research
08/28/2023

RefSearch: A Search Engine for Refactoring

Developers often refactor source code to improve its quality during soft...
research
07/26/2023

ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography

Aortic stenosis (AS) is a common heart valve disease that requires accur...
research
10/23/2022

EUREKA: EUphemism Recognition Enhanced through Knn-based methods and Augmentation

We introduce EUREKA, an ensemble-based approach for performing automatic...
research
04/21/2023

HabitatDyn Dataset: Dynamic Object Detection to Kinematics Estimation

The advancement of computer vision and machine learning has made dataset...
research
01/13/2023

Analyzing and Improving the Pyramidal Predictive Network for Future Video Frame Prediction

The pyramidal predictive network (PPNV1) proposes an interesting tempora...

Please sign up or login with your details

Forgot password? Click here to reset