most citedDONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning

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cs.CV2026

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…

cs.CV2026

FD: A Dedicated Framework for Fine-Grained Dataset Distillation

Hongxu Ma, Guang Li, Shijie Wang +5

Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong results on general benchmarks. Decoup…

cs.CV2025

-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation

Jucheng Hu, Suorong Yang, Dongzhan Zhou

Visual Instruction Finetuning (VIF) is pivotal for post-training Vision-Language Models (VLMs). Unlike unimodal instruction finetuning in plain-text large language models, which ma…