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Paper Abstract and Keywords
Presentation 2026-02-19 14:30
A Note on Object-Aware Dataset Distillation with Adaptive Patch Selection
Longzhen Li, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, rapid advances in artificial intelligence have led to an increasing demand for large-scale model training, which in turn has resulted in prohibitively high computational and storage costs. Modern AI systems typically rely on massive datasets, causing substantial memory consumption and extremely long training times. Although existing dataset distillation methods aim to improve training efficiency by compressing large datasets into compact synthetic sets, they often suffer from limited image realism, complex training pipelines, and unstable optimization. In this paper, we propose a novel object-aware dataset distillation framework based on an adaptive patch selection strategy. We create patches from real images, which allows our method to maintain the visual realism of the distilled images while eliminating a significant amount of optimization work. Specifically, we leverage Grounded SAM2 to identify the primary object in each image during the distillation process, and dynamically select informative patches according to the data distribution characteristics of the original samples. The distilled data are then synthesized from these optimal patches. By explicitly preserving object-centric semantic information, our method retains more discriminative content in the distilled images. Extensive experiments demonstrate that our approach consistently achieves higher accuracy than conventional methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Dataset distillation / Grounded SAM2 / Adaptive patch selection / / / / /  
Reference Info. ITE Tech. Rep., vol. 50, no. 5, ME2026-12, pp. 51-54, Feb. 2026.
Paper # ME2026-12 
Date of Issue 2026-02-12 (MMS, ME, AIT, SIP) 
ISSN Online edition: ISSN 2424-1970
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Conference Information
Committee IEICE-IE IEICE-ITS MMS ME AIT SIP  
Conference Date 2026-02-19 - 2026-02-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ME 
Conference Code 2026-02-IE-ITS-MMS-ME-AIT-SIP 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Note on Object-Aware Dataset Distillation with Adaptive Patch Selection 
Sub Title (in English)  
Keyword(1) Dataset distillation  
Keyword(2) Grounded SAM2  
Keyword(3) Adaptive patch selection  
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1st Author's Name Longzhen Li  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Guang Li  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Ren Togo  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Keisuke Maeda  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
5th Author's Name Takahiro Ogawa  
5th Author's Affiliation Hokkaido University (Hokkaido Univ.)
6th Author's Name Miki Haseyama  
6th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2026-02-19 14:30:00 
Presentation Time 15 minutes 
Registration for ME 
Paper # MMS2026-12, ME2026-12, AIT2026-12, SIP2026-12 
Volume (vol) vol.50 
Number (no) no.5 
Page pp.51-54 
#Pages
Date of Issue 2026-02-12 (MMS, ME, AIT, SIP) 


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