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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…
Hyperbolic Dataset Distillation
Wenyuan Li, Guang Li, Keisuke Maeda +2
To address the computational and storage challenges posed by large-scale datasets in deep learning, dataset distillation has been proposed to synthesize a compact dataset that repl…
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…
StarMAP: Global Neighbor Embedding for Faithful Data Visualization
Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa +1
Neighbor embedding is widely employed to visualize high-dimensional data; however, it frequently overlooks the global structure, e.g., intercluster similarities, thereby impeding a…
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…