1 citations · 1 across the 7 of their papers we have counts for
9 papers
What Keeps Vision-Language Models Looking at the Image?
Hiroto Osaka, Shohei Taniguchi, Gouki Minegishi +3
When do vision-language models need direct access to the image while generating an answer? We study image dependence during answer generation by examining how the visual informatio…
SUNTA: Hierarchical Video Prediction with Surprise-based Chunking
Tomoshi Iiyama, Masahiro Suzuki, Yutaka Matsuo
Hierarchical state-space models (HSSMs) offer a promising approach to long-horizon prediction by segmenting sequences into temporal chunks. However, their performance hinges on how…
When Object-Centric World Models Meet Policy Learning: From Pixels to Policies, and Where It Breaks
Stefano Ferraro, Akihiro Nakano, Masahiro Suzuki +1
Object-centric world models (OCWM) aim to decompose visual scenes into object-level representations, providing structured abstractions that could improve compositional generalizati…
CLIP-like Model as a Foundational Density Ratio Estimator
Fumiya Uchiyama, Rintaro Yanagi, Shohei Taniguchi +5
Density ratio estimation is a core concept in statistical machine learning because it provides a unified mechanism for tasks such as importance weighting, divergence estimation, an…
ADOPT: Modified Adam Can Converge with Any with the Optimal Rate
Shohei Taniguchi, Keno Harada, Gouki Minegishi +7
Adam is one of the most popular optimization algorithms in deep learning. However, it is known that Adam does not converge in theory unless choosing a hyperparameter, i.e., ,…
Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases
Cristian Meo, Akihiro Nakano, Mircea Lică +7
Unsupervised object-centric learning from videos is a promising approach towards learning compositional representations that can be applied to various downstream tasks, such as pre…
Enhancing Unimodal Latent Representations in Multimodal VAEs through Iterative Amortized Inference
Yuta Oshima, Masahiro Suzuki, Yutaka Matsuo
Multimodal variational autoencoders (VAEs) aim to capture shared latent representations by integrating information from different data modalities. A significant challenge is accura…
The Embodied World Model Based on LLM with Visual Information and Prediction-Oriented Prompts
Wakana Haijima, Kou Nakakubo, Masahiro Suzuki +1
In recent years, as machine learning, particularly for vision and language understanding, has been improved, research in embedded AI has also evolved. VOYAGER is a well-known LLM-b…
SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces
Yuta Oshima, Shohei Taniguchi, Masahiro Suzuki +1
Given the remarkable achievements in image generation through diffusion models, the research community has shown increasing interest in extending these models to video generation.…