47 citations · 164 across the 53 of their papers we have counts for
38 papers · 1 filter
Physics-Guided Flow-Map Matching for Precipitation Nowcasting
Shunya Nagashima, Takumi Bannai, Makoto Misaizu +3
Precipitation nowcasting, generating future radar fields from past observations, is critical for flood warning and disaster response. It is also a demanding benchmark for spatiotem…
Hierarchical Federated Learning with Dynamic Clustering and Adaptive Regularization for Robust Infrastructure Inspection
Yuhu Feng, Keisuke Maeda, Takahiro Ogawa +1
The deployment of data-driven computer vision models for structural health monitoring (SHM) is heavily constrained by the data silo dilemma due to stringent privacy and security re…
SAS: Semantic-aware Sampling for Generative Dataset Distillation
Mingzhuo Li, Guang Li, Linfeng Ye +4
Deep neural networks have achieved impressive performance across a wide range of tasks, but this success often comes with substantial computational and storage costs due to large-s…
Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models
Bincheng Peng, Guang Li, Ping Liu +2
Dataset distillation compresses a large training set into a small synthetic set that preserves downstream training utility. While most existing methods target training networks fro…
FD: A Dedicated Framework for Fine-Grained Dataset Distillation
Hongxu Ma, Guang Li, Shijie Wang +5
Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong results on general benchmarks. Decoup…
ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imaging
Linfeng Ye, Shayan Mohajer Hamidi, Zhixiang Chi +5
Attention-based multiple instance learning (MIL) has emerged as a powerful framework for whole slide image (WSI) diagnosis, leveraging attention to aggregate instance-level feature…
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…
Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks
Mingzhuo Li, Guang Li, Linfeng Ye +4
In this paper, we propose difficulty-guided sampling (DGS) to bridge the target gap between the distillation objective and the downstream task, therefore improving the performance…
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…
Decoupled Audio-Visual Dataset Distillation
Wenyuan Li, Guang Li, Keisuke Maeda +2
Audio-Visual Dataset Distillation aims to compress large-scale datasets into compact subsets while preserving the performance of the original data. However, conventional Distributi…
Otter: Mitigating Background Distractions of Wide-Angle Few-Shot Action Recognition with Enhanced RWKV
Wenbo Huang, Jinghui Zhang, Zhenghao Chen +6
Wide-angle videos in few-shot action recognition (FSAR) effectively express actions within specific scenarios. However, without a global understanding of both subjects and backgrou…
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…
Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling
Mingzhuo Li, Guang Li, Jiafeng Mao +3
To alleviate the reliance of deep neural networks on large-scale datasets, dataset distillation aims to generate compact, high-quality synthetic datasets that can achieve comparabl…
Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing
Yuhu Feng, Keisuke Maeda, Takahiro Ogawa +1
Egocentric video gaze estimation requires models to capture individual gaze patterns while adapting to diverse user data. Our approach leverages a transformer-based architecture, i…
Triplet Synthesis For Enhancing Composed Image Retrieval via Counterfactual Image Generation
Kenta Uesugi, Naoki Saito, Keisuke Maeda +2
Composed Image Retrieval (CIR) provides an effective way to manage and access large-scale visual data. Construction of the CIR model utilizes triplets that consist of a reference i…
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…
Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence
Wenbo Huang, Jinghui Zhang, Guang Li +6
In few-shot action recognition (FSAR), long sub-sequences of video naturally express entire actions more effectively. However, the high computational complexity of mainstream Trans…
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…