Video Prediction Models as General Visual Encoders

TL;DR

This paper investigates using open-source video prediction models as general visual encoders for downstream tasks like instance segmentation, leveraging their ability to encode spatial and temporal information effectively.

Contribution

It introduces a novel approach of employing video prediction models as encoders, inspired by human vision principles, to improve scene understanding and segmentation.

Findings

Pre-trained video generative models can be adapted for segmentation tasks.

Latent spaces of video models encode meaningful motion and spatial information.

Promising results in leveraging generative models for downstream computer vision tasks.

Abstract

This study explores the potential of open-source video conditional generation models as encoders for downstream tasks, focusing on instance segmentation using the BAIR Robot Pushing Dataset. The researchers propose using video prediction models as general visual encoders, leveraging their ability to capture critical spatial and temporal information which is essential for tasks such as instance segmentation. Inspired by human vision studies, particularly Gestalts principle of common fate, the approach aims to develop a latent space representative of motion from images to effectively discern foreground from background information. The researchers utilize a 3D Vector-Quantized Variational Autoencoder 3D VQVAE video generative encoder model conditioned on an input frame, coupled with downstream segmentation tasks. Experiments involve adapting pre-trained video generative models, analyzing their latent spaces, and training custom decoders for foreground-background segmentation. The findings demonstrate promising results in leveraging generative pretext learning for downstream tasks, working towards enhanced scene analysis and segmentation in computer vision applications.