92 citations · 199 across the 26 of their papers we have counts for
5 papers · 1 filter
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
Mengke Li, Zhikai Hu, Yang Lu +3
The imbalanced distribution of long-tailed data presents a considerable challenge for deep learning models, as it causes them to prioritize the accurate classification of head clas…
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
Mengke Li, Yiu-ming Cheung, Yang Lu
Long-tailed data is still a big challenge for deep neural networks, even though they have achieved great success on balanced data. We observe that vanilla training on long-tailed d…
Feature-Balanced Loss for Long-Tailed Visual Recognition
Mengke Li, Yiu-ming Cheung, Juyong Jiang
Deep neural networks frequently suffer from performance degradation when the training data is long-tailed because several majority classes dominate the training, resulting in a bia…
Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition
Mengke Li, Yiu-ming Cheung, Yang Lu +3
It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tai…
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation
Yan Jin, Mengke Li, Yang Lu +2
Deep neural networks have made huge progress in the last few decades. However, as the real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be heav…