2 citations · 2 across the 7 of their papers we have counts for
10 papers
The Composition Gap in Dataset Distillation
Guang Li, Takahiro Ogawa, Miki Haseyama
Dataset distillation compresses a training set into a small synthetic set, usually evaluated one at a time. In federated and data-governance settings, several parties distill their…
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…
Latent Dataset Distillation for Human Motion Prediction
Ge Tian, Guang Li, Takahiro Ogawa +1
Dataset distillation (DD) compresses a large training set into a compact synthetic set while preserving downstream training utility. Although DD has been widely studied for images…
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…
Importance-Aware Adaptive Dataset Distillation
Guang Li, Ren Togo, Takahiro Ogawa +1
Herein, we propose a novel dataset distillation method for constructing small informative datasets that preserve the information of the large original datasets. The development of…
Gromov-Wasserstein Autoencoders
Nao Nakagawa, Ren Togo, Takahiro Ogawa +1
Variational Autoencoder (VAE)-based generative models offer flexible representation learning by incorporating meta-priors, general premises considered beneficial for downstream tas…