47 citations · 154 across the 34 of their papers we have counts for
26 papers · 1 filter
Scene2Sound: Auditory-Grounded Soundscape Generation for 3D Gaussian Worlds
Masaki Yoshida, Ren Togo, Takahiro Ogawa +1
3D Gaussian Splatting (3DGS) turns captured or generated imagery into photorealistic 3D world simulations that users can freely explore, yet these worlds remain silent. Because exi…
Personalized Longitudinal Medical Report Generation via Temporally-Aware Federated Adaptation
He Zhu, Ren Togo, Takahiro Ogawa +8
Longitudinal medical report generation is clinically important yet remains challenging due to strict privacy constraints and the evolving nature of disease progression. Although fe…
Foreground-Aware Dataset Distillation via Dynamic Patch Selection
Longzhen Li, Guang Li, Ren Togo +3
In this paper, we propose a foreground-aware dataset distillation method that enhances patch selection in a content-adaptive manner. With the rising computational cost of training…
Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness
Ren Tasai, Guang Li, Ren Togo +9
We propose a novel continual self-supervised learning (CSSL) framework for simultaneously learning diverse features from multi-window-obtained chest computed tomography (CT) images…
Adaptive Shared Experts with LoRA-Based Mixture of Experts for Multi-Task Learning
Minghao Yang, Ren Togo, Guang Li +2
Mixture-of-Experts (MoE) has emerged as a powerful framework for multi-task learning (MTL). However, existing MoE-MTL methods often rely on single-task pretrained backbones and suf…
Objectness Similarity: Capturing Object-Level Fidelity in 3D Scene Evaluation
Yuiko Uchida, Ren Togo, Keisuke Maeda +2
This paper presents Objectness SIMilarity (OSIM), a novel evaluation metric for 3D scenes that explicitly focuses on "objects," which are fundamental units of human visual percepti…
Dual-Model Weight Selection and Self-Knowledge Distillation for Medical Image Classification
Ayaka Tsutsumi, Guang Li, Ren Togo +3
We propose a novel medical image classification method that integrates dual-model weight selection with self-knowledge distillation (SKD). In real-world medical settings, deploying…
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images
Ren Tasai, Guang Li, Ren Togo +7
We propose a novel continual self-supervised learning method (CSSL) considering medical domain knowledge in chest CT images. Our approach addresses the challenge of sequential lear…
Generative Dataset Distillation Based on Self-knowledge Distillation
Longzhen Li, Guang Li, Ren Togo +3
Dataset distillation is an effective technique for reducing the cost and complexity of model training while maintaining performance by compressing large datasets into smaller, more…
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering
He Zhu, Ren Togo, Takahiro Ogawa +1
Conventional medical artificial intelligence (AI) models face barriers in clinical application and ethical issues owing to their inability to handle the privacy-sensitive character…
Cross-domain Multi-step Thinking: Zero-shot Fine-grained Traffic Sign Recognition in the Wild
Yaozong Gan, Guang Li, Ren Togo +3
In this study, we propose Cross-domain Multi-step Thinking (CdMT) to improve zero-shot fine-grained traffic sign recognition (TSR) performance in the wild. Zero-shot fine-grained T…
Generative Dataset Distillation Based on Diffusion Model
Duo Su, Junjie Hou, Guang Li +4
This paper presents our method for the generative track of The First Dataset Distillation Challenge at ECCV 2024. Since the diffusion model has become the mainstay of generative mo…
Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition
Yaozong Gan, Guang Li, Ren Togo +3
Recent multimodal large language models (MLLM) such as GPT-4o and GPT-4v have shown great potential in autonomous driving. In this paper, we propose a cross-domain few-shot in-cont…
Zero-shot Composed Image Retrieval Considering Query-target Relationship Leveraging Masked Image-text Pairs
Huaying Zhang, Rintaro Yanagi, Ren Togo +2
This paper proposes a novel zero-shot composed image retrieval (CIR) method considering the query-target relationship by masked image-text pairs. The objective of CIR is to retriev…
Reinforcing Pre-trained Models Using Counterfactual Images
Xiang Li, Ren Togo, Keisuke Maeda +2
This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. Deep learning classification models are often traine…
Generative Dataset Distillation: Balancing Global Structure and Local Details
Longzhen Li, Guang Li, Ren Togo +3
In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a…
Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach
Taro Togo, Ren Togo, Keisuke Maeda +2
This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism, aimed at dynamically managing class information for be…
Prompt-based Personalized Federated Learning for Medical Visual Question Answering
He Zhu, Ren Togo, Takahiro Ogawa +1
We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns in traditional medical visual question answering (VQ…
Interpretable Visual Question Answering Referring to Outside Knowledge
He Zhu, Ren Togo, Takahiro Ogawa +1
We present a novel multimodal interpretable VQA model that can answer the question more accurately and generate diverse explanations. Although researchers have proposed several met…
Union-set Multi-source Model Adaptation for Semantic Segmentation
Zongyao Li, Ren Togo, Takahiro Ogawa +1
This paper solves a generalized version of the problem of multi-source model adaptation for semantic segmentation. Model adaptation is proposed as a new domain adaptation problem w…
RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images
Guang Li, Ren Togo, Takahiro Ogawa +1
In this study, we propose a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representations from X-ray images. Our method adopts a new maski…
Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing
Guang Li, Ren Togo, Takahiro Ogawa +1
Background and objective: Sharing of medical data is required to enable the cross-agency flow of healthcare information and construct high-accuracy computer-aided diagnosis systems…
Dataset Complexity Assessment Based on Cumulative Maximum Scaled Area Under Laplacian Spectrum
Guang Li, Ren Togo, Takahiro Ogawa +1
Dataset complexity assessment aims to predict classification performance on a dataset with complexity calculation before training a classifier, which can also be used for classifie…
Dataset Distillation Using Parameter Pruning
Guang Li, Ren Togo, Takahiro Ogawa +1
In this study, we propose a novel dataset distillation method based on parameter pruning. The proposed method can synthesize more robust distilled datasets and improve distillation…