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.

本研究探讨了开源视频条件生成模型作为下游任务的编码器的潜力，重点研究了使用 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视觉编码器，利用其捕捉关键的时空信息的能力，对实例分割等任务至关重要。受人类视觉研究的启发，尤其是共同命运的格式原则，该方法旨在开发一个能够有效区分前景与背景信息的运动性的潜在空间。研究人员利用一个基于 3D 向量量化变分自编码器（3D VQVAE）的视频生成编码器模型，以输入帧作为条件，并结合下游分割任务。实验涉及对预训练视频生成模型进行自适应，分析它们的潜在空间，并训练用于前景 - 背景分割的自定义解码器。研究结果展示了利用生成式前提学习进行下游任务的有希望结果，为计算机视觉应用中的场景分析和分割工作提供了进展。