5 citations · 10 across the 6 of their papers we have counts for
6 papers
Cross-Modal Retrieval with Cauchy-Schwarz Divergence
Jiahao Zhang, Wenzhe Yin, Shujian Yu
Effective cross-modal retrieval requires robust alignment of heterogeneous data types. Most existing methods focus on bi-modal retrieval tasks and rely on distributional alignment…
Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network
Jie Liu, Yanqi Bao, Wenzhe Yin +4
Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few annotated samples and has made great progress recently. Most of the existing FSS mod…
Principle of Relevant Information for Graph Sparsification
Shujian Yu, Francesco Alesiani, Wenzhe Yin +2
Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties. In this paper, we propose the first general and effective informatio…
Bilevel Continual Learning
Ammar Shaker, Francesco Alesiani, Shujian Yu +1
Continual learning (CL) studies the problem of learning a sequence of tasks, one at a time, such that the learning of each new task does not lead to the deterioration in performanc…
Learning an Interpretable Graph Structure in Multi-Task Learning
Shujian Yu, Francesco Alesiani, Ammar Shaker +1
We present a novel methodology to jointly perform multi-task learning and infer intrinsic relationship among tasks by an interpretable and sparse graph. Unlike existing multi-task…
Towards Interpretable Multi-Task Learning Using Bilevel Programming
Francesco Alesiani, Shujian Yu, Ammar Shaker +1
Interpretable Multi-Task Learning can be expressed as learning a sparse graph of the task relationship based on the prediction performance of the learned models. Since many natural…