4 citations · 10 across the 7 of their papers we have counts for
7 papers
Empowering Language Models with Active Inquiry for Deeper Understanding
Jing-Cheng Pang, Heng-Bo Fan, Pengyuan Wang +6
The rise of large language models (LLMs) has revolutionized the way that we interact with artificial intelligence systems through natural language. However, LLMs often misinterpret…
Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery
Yuyan Zhou, Dong Liang, Songcan Chen +3
When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and d…
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning
Ming-Kun Xie, Jia-Hao Xiao, Hao-Zhe Liu +3
Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional…
ALL-E: Aesthetics-guided Low-light Image Enhancement
Ling Li, Dong Liang, Yuanhang Gao +2
Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods…
Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks
Ye Li, Song-Can Chen, Sheng-Jun Huang
Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training fa…
Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting
Chen-Chen Zong, Zheng-Tao Cao, Hong-Tao Guo +4
Deep neural networks trained with standard cross-entropy loss are more prone to memorize noisy labels, which degrades their performance. Negative learning using complementary label…