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cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…