7 citations · 14 across the 16 of their papers we have counts for
17 papers
FDSML: Formula-Driven Supervised Metric Learning with Parameter-Aware Triplet Loss
Hina Otake, Shinei Arakawa, Keitaro Tanaka +3
Formula-Driven Supervised Learning (FDSL) enables vision pre-training without real images, but existing methods cast pre-training as classification on discretized labels, discardin…
Visual General Intelligence: A White Paper
Hirokatsu Kataoka, Yoshihiro Fukuhara, Yonglong Tian +18
This paper reconsiders intelligence from a vision-centered perspective and examines whether intelligence emerging from visual experience and learning may provide a pathway toward A…
Beyond Single Object: Learning 3D Relations with Large Language Models
Kohsuke Ide, Ryousuke Yamada, Yue Qiu +4
We address a fundamental gap in 3D-LLMs: existing models focus on single-object/scene description, struggling with detailed, inter-object comparison. We propose a framework for det…
Seeing Red, Thinking Bad: Color Bias in Vision Language Models
Kohsuke Ide, Ryousuke Yamada, Yoshihiro Fukuhara +2
Vision language models (VLMs) are increasingly used in industrial decision-making systems, such as recruitment support and recommendation. This motivates careful analysis of how VL…
Feed-forward Motion In-betweening for Any 4D
Hiroki Nishizawa, Hubert P. H. Shum, Yoshihiro Fukuhara +2
4D dynamics (3D geometry evolving over time) is a fundamental representation of the physical world and plays a crucial role in world modeling (e.g., animation and games). Owing to…
MoireMix: A Formula-Based Data Augmentation for Improving Image Classification Robustness
Yuto Matsuo, Yoshihiro Fukuhara, Yuki M. Asano +3
Data augmentation is a key technique for improving the robustness of image classification models. However, many recent approaches rely on diffusion-based synthesis or complex featu…
3D sans 3D Scans: Scalable Pre-training from Video-Generated Point Clouds
Ryousuke Yamada, Kohsuke Ide, Yoshihiro Fukuhara +4
Despite recent progress in 3D self-supervised learning, collecting large-scale 3D scene scans remains expensive and labor-intensive. In this work, we investigate whether 3D represe…
Industrial Synthetic Segment Pre-training
Shinichi Mae, Hirokatsu Kataoka, Ryousuke Yamada +3
Vision Foundation Models (VFMs) have made remarkable progress and are increasingly being applied to segmentation tasks in real-world industrial settings. However, VFMs pre-trained…
Detect Fake with Fake: Leveraging Synthetic Data-driven Representation for Synthetic Image Detection
Hina Otake, Yoshihiro Fukuhara, Yoshiki Kubotani +1
Are general-purpose visual representations acquired solely from synthetic data useful for detecting fake images? In this work, we show the effectiveness of synthetic data-driven re…
Enhancing Inverse Problem Solutions with Accurate Surrogate Simulators and Promising Candidates
Akihiro Fujii, Hideki Tsunashima, Yoshihiro Fukuhara +2
Deep-learning inverse techniques have attracted significant attention in recent years. Among them, the neural adjoint (NA) method, which employs a neural network surrogate simulato…
Scapegoat Generation for Privacy Protection from Deepfake
Gido Kato, Yoshihiro Fukuhara, Mariko Isogawa +3
To protect privacy and prevent malicious use of deepfake, current studies propose methods that interfere with the generation process, such as detection and destruction approaches.…
Neural Density-Distance Fields
Itsuki Ueda, Yoshihiro Fukuhara, Hirokatsu Kataoka +3
The success of neural fields for 3D vision tasks is now indisputable. Following this trend, several methods aiming for visual localization (e.g., SLAM) have been proposed to estima…
RLTutor: Reinforcement Learning Based Adaptive Tutoring System by Modeling Virtual Student with Fewer Interactions
Yoshiki Kubotani, Yoshihiro Fukuhara, Shigeo Morishima
A major challenge in the field of education is providing review schedules that present learned items at appropriate intervals to each student so that memory is retained over time.…
ATRO: Adversarial Training with a Rejection Option
Masahiro Kato, Zhenghang Cui, Yoshihiro Fukuhara
This paper proposes a classification framework with a rejection option to mitigate the performance deterioration caused by adversarial examples. While recent machine learning algor…
What Do Adversarially Robust Models Look At?
Takahiro Itazuri, Yoshihiro Fukuhara, Hirokatsu Kataoka +1
In this paper, we address the open question: "What do adversarially robust models look at?" Recently, it has been reported in many works that there exists the trade-off between sta…
Automatic Paper Summary Generation from Visual and Textual Information
Shintaro Yamamoto, Yoshihiro Fukuhara, Ryota Suzuki +2
Due to the recent boom in artificial intelligence (AI) research, including computer vision (CV), it has become impossible for researchers in these fields to keep up with the expone…
Understanding Fake Faces
Ryota Natsume, Kazuki Inoue, Yoshihiro Fukuhara +3
Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven p…