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Masahiro Suzuki

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4 papers

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TMLR Journal 2025 Journal Article

Efficient Object-Centric Representation Learning using Masked Generative Modeling

Akihiro Nakano
Masahiro Suzuki
Yutaka Matsuo

Learning object-centric representations from visual inputs in an unsupervised manner has drawn focus to solve more complex tasks, such as reasoning and reinforcement learning. However, current state-of-the-art methods, relying on autoregressive transformers or diffusion models to generate scenes from object-centric representations, suffer from computational inefficiency due to their sequential or iterative nature. This computational bottleneck limits their practical application and hinders scaling to more complex downstream tasks. To overcome this, we propose MOGENT, an efficient object-centric learning framework based on masked generative modeling. MOGENT conditions a masked bidirectional transformer on learned object slots and employs a parallel iterative decoding scheme to generate scenes, enabling efficient compositional generation. Experiments show that MOGENT significantly improves computational efficiency, accelerating the generation process by up to 67x and 17x compared to autoregressive models and diffusion-based models, respectively. Importantly, the efficiency is attained while maintaining strong or competitive performance on object segmentation and compositional generation tasks.

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NeurIPS Conference 2025 Conference Paper

Inference-Time Text-to-Video Alignment with Diffusion Latent Beam Search

Yuta Oshima
Masahiro Suzuki
Yutaka Matsuo
Hiroki Furuta

The remarkable progress in text-to-video diffusion models enables the generation of photorealistic videos, although the content of these generated videos often includes unnatural movement or deformation, reverse playback, and motionless scenes. Recently, an alignment problem has attracted huge attention, where we steer the output of diffusion models based on some measure of the content's goodness. Because there is a large room for improvement of perceptual quality along the frame direction, we should address which metrics we should optimize and how we can optimize them in the video generation. In this paper, we propose diffusion latent beam search with lookahead estimator, which can select a better diffusion latent to maximize a given alignment reward at inference time. We then point out that improving perceptual video quality with respect to alignment to prompts requires reward calibration by weighting existing metrics. This is because when humans or vision language models evaluate outputs, many previous metrics to quantify the naturalness of video do not always correlate with the evaluation. We demonstrate that our method improves the perceptual quality evaluated on the calibrated reward, VLMs, and human assessment, without model parameter update, and outputs the best generation compared to greedy search and best-of-N sampling under much more efficient computational cost. The experiments highlight that our method is beneficial to many capable generative models, and provide a practical guideline: we should prioritize the inference-time compute allocation into enabling the lookahead estimator and increasing the search budget, rather than expanding the denoising steps.

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TMLR Journal 2025 Journal Article

The Geometry of Phase Transitions in Diffusion Models: Tubular Neighbourhoods and Singularities

Manato Yaguchi
Kotaro Sakamoto
Ryosuke Sakamoto
Masato Tanabe
Masatomo Akagawa
Yusuke Hayashi
Masahiro Suzuki
Yutaka Matsuo

Diffusion models undergo phase transitions during the generative process where data features suddenly emerge in the final stages. The current study aims to elucidate this critical phenomenon from the geometrical perspective. We employ the concept of ``injectivity radius'', a quantity that characterises the structure of the data manifold. Through theoretical and empirical evidence, we demonstrate that phase transitions in the generative process of diffusion models are closely related to the injectivity radius. Our findings offer a novel perspective on phase transitions in diffusion models, with potential implications for improving performance and sampling efficiency.

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NeurIPS Conference 2024 Conference Paper

ADOPT: Modified Adam Can Converge with Any $\beta_2$ with the Optimal Rate

Shohei Taniguchi
Keno Harada
Gouki Minegishi
Yuta Oshima
Seong Cheol Jeong
Go Nagahara
Tomoshi Iiyama
Masahiro Suzuki

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. , $\beta_2$, in a problem-dependent manner. There have been many attempts to fix the non-convergence (e. g. , AMSGrad), but they require an impractical assumption that the gradient noise is uniformly bounded. In this paper, we propose a new adaptive gradient method named ADOPT, which achieves the optimal convergence rate of $\mathcal{O} ( 1 / \sqrt{T} )$ with any choice of $\beta_2$ without depending on the bounded noise assumption. ADOPT addresses the non-convergence issue of Adam by removing the current gradient from the second moment estimate and changing the order of the momentum update and the normalization by the second moment estimate. We also conduct intensive numerical experiments, and verify that our ADOPT achieves superior results compared to Adam and its variants across a wide range of tasks, including image classification, generative modeling, natural language processing, and deep reinforcement learning. The implementation is available at https: //github. com/iShohei220/adopt.

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