73 citations · 212 across the 46 of their papers we have counts for
8 papers · 1 filter
Test-time Cost-and-Quality Controllable Arbitrary-Scale Super-Resolution with Variable Fourier Components
Kazutoshi Akita, Norimichi Ukita
Super-resolution (SR) with arbitrary scale factor and cost-and-quality controllability at test time is essential for various applications. While several arbitrary-scale SR methods…
Size-Variable Virtual Try-On with Physical Clothes Size
Yohei Yamashita, Chihiro Nakatani, Norimichi Ukita
This paper addresses a new virtual try-on problem of fitting any size of clothes to a reference person in the image domain. While previous image-based virtual try-on methods can pr…
Burst Super-Resolution with Diffusion Models for Improving Perceptual Quality
Kyotaro Tokoro, Kazutoshi Akita, Norimichi Ukita
While burst LR images are useful for improving the SR image quality compared with a single LR image, prior SR networks accepting the burst LR images are trained in a deterministic…
Inpainting-Driven Mask Optimization for Object Removal
Kodai Shimosato, Norimichi Ukita
This paper proposes a mask optimization method for improving the quality of object removal using image inpainting. While many inpainting methods are trained with a set of random ma…
Time-series Initialization and Conditioning for Video-agnostic Stabilization of Video Super-Resolution using Recurrent Networks
Hiroshi Mori, Norimichi Ukita
A Recurrent Neural Network (RNN) for Video Super Resolution (VSR) is generally trained with randomly clipped and cropped short videos extracted from original training videos due to…
Depth Estimation fusing Image and Radar Measurements with Uncertain Directions
Masaya Kotani, Takeru Oba, Norimichi Ukita
This paper proposes a depth estimation method using radar-image fusion by addressing the uncertain vertical directions of sparse radar measurements. In prior radar-image fusion wor…
NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI
Yuki Kondo, Riku Miyata, Fuma Yasue +2
In this paper, we analyze and discuss ShadowFormer in preparation for the NTIRE2023 Shadow Removal Challenge [1], implementing five key improvements: image alignment, the introduct…
Learning Group Activity Features Through Person Attribute Prediction
Chihiro Nakatani, Hiroaki Kawashima, Norimichi Ukita
This paper proposes Group Activity Feature (GAF) learning in which features of multi-person activity are learned as a compact latent vector. Unlike prior work in which the manual a…