91 citations · 270 across the 62 of their papers we have counts for
8 papers · 1 filter
Distilling Visual Reasoning into Text Space
Wenhan Yang, Nilay Naharas, Ali Payani +1
Large Vision-Language Models (LVLMs) have shown strong promise for multimodal reasoning, yet often struggle with tasks requiring concepts beyond what is directly observable in the…
Data Selection for Fine-tuning Vision Language Models via Cross Modal Alignment Trajectories
Nilay Naharas, Dang Nguyen, Nesihan Bulut +3
Data-efficient learning aims to eliminate redundancy in large training datasets by training models on smaller subsets of the most informative examples. While data selection has bee…
Do We Need All the Synthetic Data? Targeted Image Augmentation via Diffusion Models
Dang Nguyen, Jiping Li, Jinghao Zheng +1
Synthetically augmenting training datasets with diffusion models has become an effective strategy for improving the generalization of image classifiers. However, existing approache…
DD-Ranking: Rethinking the Evaluation of Dataset Distillation
Zekai Li, Xinhao Zhong, Samir Khaki +49
In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance co…
MM-GEN: Enhancing Task Performance Through Targeted Multimodal Data Curation
Siddharth Joshi, Besmira Nushi, Vidhisha Balachandran +4
Vision-language models (VLMs) are highly effective but often underperform on specialized tasks; for example, Llava-1.5 struggles with chart and diagram understanding due to scarce…
Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity
Siddharth Joshi, Arnav Jain, Ali Payani +1
Contrastive Language-Image Pre-training (CLIP) on large-scale image-caption datasets learns representations that can achieve remarkable zero-shot generalization. However, such mode…
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
Xuxi Chen, Yu Yang, Zhangyang Wang +1
Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar genera…
Robust Contrastive Language-Image Pre-training against Data Poisoning and Backdoor Attacks
Wenhan Yang, Jingdong Gao, Baharan Mirzasoleiman
Contrastive vision-language representation learning has achieved state-of-the-art performance for zero-shot classification, by learning from millions of image-caption pairs crawled…