1 citations · 1 across the 17 of their papers we have counts for
5 papers · 2 filters
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…
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…
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…
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…