71 citations · 143 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Compact Neural Networks via Stacking Designed Basic Units
Weichao Lan, Yiu-ming Cheung, Juyong Jiang
Unstructured pruning has the limitation of dealing with the sparse and irregular weights. By contrast, structured pruning can help eliminate this drawback but it requires complex c…
FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval
Xin Liu, Xingzhi Wang, Yiu-ming Cheung
Cross-modal hashing, favored for its effectiveness and efficiency, has received wide attention to facilitating efficient retrieval across different modalities. Nevertheless, most e…