2 citations · 5 across the 3 of their papers we have counts for
3 papers
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…
cs.CV2023★ 2 cited
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
Yiming Cui, Linjie Yang, Haichao Yu
Transformer-based detection and segmentation methods use a list of learned detection queries to retrieve information from the transformer network and learn to predict the location…
cs.CV2023★ 1 cited
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?
Cheng-En Wu, Yu Tian, Haichao Yu +4
Vision-language models such as CLIP learn a generic text-image embedding from large-scale training data. A vision-language model can be adapted to a new classification task through…