  Unsupervised video object learning seeks to decompose video scenes into
structural object representations without any supervision from depth, optical
flow, or segmentation. We present VONet, an innovative approach that is
inspired by MONet. While utilizing a U-Net architecture, VONet employs an
efficient and effective parallel attention inference process, generating
attention masks for all slots simultaneously. Additionally, to enhance the
temporal consistency of each mask across consecutive video frames, VONet
develops an object-wise sequential VAE framework. The integration of these
innovative encoder-side techniques, in conjunction with an expressive
transformer-based decoder, establishes VONet as the leading unsupervised method
for object learning across five MOVI datasets, encompassing videos of diverse
complexities. Code is available at https://github.com/hnyu/vonet.
