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20202026
most citedSimple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos

20 citations · 42 across the 10 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence

NVIDIA, :, Amala Sanjay Deshmukh +204

We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…

cs.LG2024

Neural Language of Thought Models

Yi-Fu Wu, Minseung Lee, Sungjin Ahn

The Language of Thought Hypothesis suggests that human cognition operates on a structured, language-like system of mental representations. While neural language models can naturall…

cs.LG2023★ 7 cited

An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning

Jaesik Yoon, Yi-Fu Wu, Heechul Bae +1

Unsupervised object-centric representation (OCR) learning has recently drawn attention as a new paradigm of visual representation. This is because of its potential of being an effe…

cs.LG2021★ 5 cited

Generative Video Transformer: Can Objects be the Words?

Yi-Fu Wu, Jaesik Yoon, Sungjin Ahn

Transformers have been successful for many natural language processing tasks. However, applying transformers to the video domain for tasks such as long-term video generation and sc…

cs.LG2020★ 9 cited

Improving Generative Imagination in Object-Centric World Models

Zhixuan Lin, Yi-Fu Wu, Skand Peri +3

The remarkable recent advances in object-centric generative world models raise a few questions. First, while many of the recent achievements are indispensable for making a general…

cs.LG2020

SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition

Zhixuan Lin, Yi-Fu Wu, Skand Vishwanath Peri +5

The ability to decompose complex multi-object scenes into meaningful abstractions like objects is fundamental to achieve higher-level cognition. Previous approaches for unsupervise…