47 citations · 164 across the 56 of their papers we have counts for
43 papers · 1 filter
PSM: Dataset Distillation Based on Precise Statistical Matching by Difficulty
Hongxu Ma, Guang Li, Shijie Wang +6
Dataset distillation (DD) condenses a large original dataset into a small distilled dataset with high training utility. Decoupled statistical matching methods substantially reduce…
FORUM: Frozen Outputs Reconciled Using Model Agreement for Visual Grounding
Taiyo Sato, Takamasa Sanda, Keisuke Maeda +3
Frozen multimodal large language models (MLLMs) now solve standard referring expression comprehension with a single prompted call, yet on adversarial benchmarks with same-category…
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…
DeCO: Discriminative Evidence Composition for Fine-Grained Dataset Distillation
Chuixuan Fan, Guang Li, Shijie Wang +5
Dataset distillation compresses a large training set into a compact synthetic set while preserving its downstream utility. However, existing methods primarily preserve global image…
Efficient Video Dataset Distillation via Cluster-Guided Prototype Blending
Chongle Ren, Guang Li, Wenbo Huang +3
Video dataset distillation aims to compress a large video dataset into a compact surrogate set that preserves its training utility. Most existing approaches synthesize condensed vi…
Self-Supervised Representation-Guided Generative Dataset Distillation
Mingzhuo Li, Guang Li, Linfeng Ye +4
Dataset distillation compresses a large training set into a compact synthetic set while retaining its downstream utility. Most existing methods target randomly initialized networks…
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…
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…
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…