44 citations · 57 across the 13 of their papers we have counts for
6 papers · 1 filter
Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning
Zihua Zhao, Feng Hong, Mengxi Chen +5
The remarkable success of contrastive-learning-based multimodal models has been greatly driven by training on ever-larger datasets with expensive compute consumption. Sample select…
Learning to Instruct for Visual Instruction Tuning
Zhihan Zhou, Feng Hong, Jiaan Luo +5
We propose L2T, an advancement of visual instruction tuning (VIT). While VIT equips Multimodal LLMs (MLLMs) with promising multimodal capabilities, the current design choices for V…
UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification
Tianjie Dai, Ruipeng Zhang, Feng Hong +3
Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition…
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Gregory Holste, Yiliang Zhou, Song Wang +22
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more rela…
Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays
Feng Hong, Tianjie Dai, Jiangchao Yao +2
Clinical classification of chest radiography is particularly challenging for standard machine learning algorithms due to its inherent long-tailed and multi-label nature. However, f…
Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning
Yuhang Zhou, Jiangchao Yao, Feng Hong +2
Class incremental learning (CIL) aims to incrementally update a trained model with the new classes of samples (plasticity) while retaining previously learned ability (stability). T…