9 citations · 24 across the 15 of their papers we have counts for
16 papers
LEARN: A Story-Driven Layout-to-Image Generation Framework for STEM Instruction
Maoquan Zhang, Bisser Raytchev, Xiujuan Sun
LEARN is a layout-aware diffusion framework designed to generate pedagogically aligned illustrations for STEM education. It leverages a curated BookCover dataset that provides narr…
Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging
Maoquan Zhang, Bisser Raytchev, Xiujuan Sun
Medical image segmentation requires not only accuracy but also robustness under challenging imaging conditions. In this study, we show that a carefully configured DeepLabv3 model c…
Manipulating Vehicle 3D Shapes through Latent Space Editing
JiangDong Miao, Tatsuya Ikeda, Bisser Raytchev +6
Although 3D object editing has the potential to significantly influence various industries, recent research in 3D generation and editing has primarily focused on converting text an…
Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning
Sora Iwamoto, Bisser Raytchev, Toru Tamaki +1
In this paper we propose a novel method which leverages the uncertainty measures provided by Bayesian deep networks through curriculum learning so that the uncertainty estimates ar…
Rephrasing visual questions by specifying the entropy of the answer distribution
Kento Terao, Toru Tamaki, Bisser Raytchev +2
Visual question answering (VQA) is a task of answering a visual question that is a pair of question and image. Some visual questions are ambiguous and some are clear, and it may be…
An Entropy Clustering Approach for Assessing Visual Question Difficulty
Kento Terao, Toru Tamaki, Bisser Raytchev +2
We propose a novel approach to identify the difficulty of visual questions for Visual Question Answering (VQA) without direct supervision or annotations to the difficulty. Prior wo…