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Constant Rate Schedule: Constant-Rate Distributional Change for Efficient Training and Sampling in Diffusion Models

Autor: Okada, Shuntaro, Doi, Kenji, Yoshihashi, Ryota, Kataoka, Hirokatsu, Tanaka, Tomohiro

We propose a noise schedule that ensures a constant rate of change in the probability distribution of diffused data throughout the diffusion process. To obtain this noise schedule, we measure the rate of change in the probability distribution of the

Externí odkaz: http://arxiv.org/abs/2411.12188

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Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation

Autor: Yoshihashi, Ryota, Otsuka, Yuya, Doi, Kenji, Tanaka, Tomohiro, Kataoka, Hirokatsu

The advance of generative models for images has inspired various training techniques for image recognition utilizing synthetic images. In semantic segmentation, one promising approach is extracting pseudo-masks from attention maps in text-to-image di

Externí odkaz: http://arxiv.org/abs/2309.01369

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Context-Free TextSpotter for Real-Time and Mobile End-to-End Text Detection and Recognition

Autor: Yoshihashi, Ryota, Tanaka, Tomohiro, Doi, Kenji, Fujino, Takumi, Yamashita, Naoaki

In the deployment of scene-text spotting systems on mobile platforms, lightweight models with low computation are preferable. In concept, end-to-end (E2E) text spotting is suitable for such purposes because it performs text detection and recognition

Externí odkaz: http://arxiv.org/abs/2106.05611

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Smart City Data Analysis via Visualization of Correlated Attribute Patterns

Autor: Sasaki, Yuya, Hori, Keizo, Nishihara, Daiki, Ohashi, Sora, Wakuta, Yusuke, Harada, Kei, Onizuka, Makoto, Arase, Yuki, Shimojo, Shinji, Doi, Kenji, Hongdi, He, Peng, Zhong-Ren

Publikováno v: EDBT 2021

Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute

Externí odkaz: http://arxiv.org/abs/2104.06701

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