mlsanityData to 14 Oct 2026

VONet: Unsupervised Video Object Learning With Parallel U-Net Attention and Object-wise Sequential VAE

arXiv 2401.11110 ↗First posted 20 Jan 2024cs.CV cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 35 of its 39 references that are in the map. First posted on arXiv 20 Jan 2024.

Builds on

35 of 39 references.

References · 35

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23 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Temporally Consistent Object-Centric Learning by Contrasting Slotscited by 31
  2. 2026Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularizationcited by 0