2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.RO2025
COLLAGE: Adaptive Fusion-based Retrieval for Augmented Policy Learning
Sateesh Kumar, Shivin Dass, Georgios Pavlakos +1
In this work, we study the problem of data retrieval for few-shot imitation learning: selecting data from a large dataset to train a performant policy for a specific task, given on…
cs.CV2024★ 1 cited
Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters
Weizhi Wang, Khalil Mrini, Linjie Yang +4
We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g.,…
cs.CV2023★ 2 cited
The Devil is in the Details: A Deep Dive into the Rabbit Hole of Data Filtering
Haichao Yu, Yu Tian, Sateesh Kumar +2
The quality of pre-training data plays a critical role in the performance of foundation models. Popular foundation models often design their own recipe for data filtering, which ma…