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.
Abstract (translated)
本研究探讨了开源视频条件生成模型的潜在作为下游任务的编码器的作用，重点关注使用BAIR机器人推动数据集进行实例分割。研究人员提出，使用视频预测模型作为通用的视觉编码器，利用其捕获关键的空间和时间信息，这对于诸如实例分割等任务至关重要。受到人类视觉研究尤其是Gestalts原理的启发，该方法旨在开发一个从图像到运动的潜在空间表示，有效地将前景与背景信息区分开来。研究人员使用基于输入帧的3D Vector-Quantized Variational Autoencoder 3D VQVAE视频生成编码器模型进行条件，并附加下游分割任务。实验包括调整预训练视频生成模型，分析其潜在空间，并为前景-背景分割训练自定义解码器。研究结果表明，通过利用生成式预训练模型对下游任务进行条件，在计算机视觉应用中取得了有前景的进展，加强了对场景分析和分割的优化。
URL
https://arxiv.org/abs/2405.16382
