124 citations · 666 across the 43 of their papers we have counts for
7 papers · 1 filter
SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning
Risa Shinoda, Ryo Hayamizu, Kodai Nakashima +3
Pre-training is a strong strategy for enhancing visual models to efficiently train them with a limited number of labeled images. In semantic segmentation, creating annotation masks…
Pre-training Vision Transformers with Very Limited Synthesized Images
Ryo Nakamura, Hirokatsu Kataoka, Sora Takashima +3
Formula-driven supervised learning (FDSL) is a pre-training method that relies on synthetic images generated from mathematical formulae such as fractals. Prior work on FDSL has sho…
Visual Atoms: Pre-training Vision Transformers with Sinusoidal Waves
Sora Takashima, Ryo Hayamizu, Nakamasa Inoue +2
Formula-driven supervised learning (FDSL) has been shown to be an effective method for pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre-training…
Informative Sample-Aware Proxy for Deep Metric Learning
Aoyu Li, Ikuro Sato, Kohta Ishikawa +2
Among various supervised deep metric learning methods proxy-based approaches have achieved high retrieval accuracies. Proxies, which are class-representative points in an embedding…
Pre-training Vision Transformers with Formula-driven Supervised Learning
Hirokatsu Kataoka, Sora Takashima, Ryo Hayamizu +6
In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k and can approach that of the JFT-300M d…
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering
Shun Iwase, Xingyu Liu, Rawal Khirodkar +2
We present RePOSE, a fast iterative refinement method for 6D object pose estimation. Prior methods perform refinement by feeding zoomed-in input and rendered RGB images into a CNN…
Epipolar-Guided Deep Object Matching for Scene Change Detection
Kento Doi, Ryuhei Hamaguchi, Shun Iwase +3
This paper describes a viewpoint-robust object-based change detection network (OBJ-CDNet). Mobile cameras such as drive recorders capture images from different viewpoints each time…