3 accepted papers
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 classes but largely disregard tail classes. The biased decision boundary caused by inadequate semantic information in tail c…
Supervised cross-modal hashing methods usually construct a massive undirected weighted graph based on labels for training data, with the aim of learning more structured hash codes by preserving relationships within this graph. However, as the volume of data increases, such an approach demands substa…
Deep Convolutional Neural Networks (CNN) have been successfully applied to many real-life problems. However, the huge memory cost of deep CNN models poses a great challenge of deploying them on memory-constrained devices (e.g., mobile phones). One popular way to reduce the memory cost of deep CNN mo…