12 accepted papers
Few-shot font generation has garnered significant attention due to its wide range of applications. The mainstream methods are based on the idea of the style and content disentangled representation learning and can be mainly categorized into two kinds of methods according to the prior used, i.e., the…
Text readability assessment involves categorizing texts based on readers' comprehension levels. Hybrid automatic readability assessment (ARA) models, combining deep and linguistic features, have recently attracted rising attention due to their impressive performance. However, existing hybrid ARA mo…
Accurate skin lesion segmentation is crucial for the quantitative analysis of skin cancer. Despite the significant advancements achieved by the deep-learning methods, the segmentation of skin lesions with irregular shapes and significant size variations is still challenging. To address the problem,…
Font design is a very resource-intensive endeavor, especially for intricate fonts. The task of few-shot font generation (FFG) has attracted great interest recently. This method captures style from a limited set of reference glyphs and then transfers it to other characters to generate diverse style f…
Multimodal Sentiment Analysis (MSA) aims to yield the sentiment polarities of speakers in video streams based on multiple modal features such as textual, acoustic and visual features, and has attracted amounts of attention in recent years. Existing MSA models often yield unimodal embeddings from the…
The hybrid automatic readability assessment (ARA) models that combine deep and linguistic features have recently received rising attention due to their impressive performance. However, the utilization of linguistic features is not fully realized, as ARA models frequently concentrate excessively on n…
Readability assessment aims to automatically classify texts based on readers' reading levels. The hybrid automatic readability assessment (ARA) models using both deep and linguistic features have attracted rising attention in recent years due to their impressive performance. However, deep features a…
The readability assessment task aims to assign a difficulty grade to a text. While neural models have recently demonstrated impressive performance, most do not exploit the ordinal nature of the difficulty grades, and make little effort for model initialization to facilitate fine-tuning. We address t…
The generation of stylish Chinese fonts is an important problem involved in many applications. Most of existing generation methods are based on the deep generative models, particularly, the generative adversarial networks (GAN) based models. However, these deep generative models may suffer from the…
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error. However, compressive networks are desired in many real world applications and direct training of small networks may be trapp…
Deep learning has aroused extensive attention due to its great empirical success. The efficiency of the block coordinate descent (BCD) methods has been recently demonstrated in deep neural network (DNN) training. However, theoretical studies on their convergence properties are limited due to the hig…
There is a recent surge of interest in nonconvex reformulations via low-rank factorization for stochastic convex semidefinite optimization problem in the purpose of efficiency and scalability. Compared with the original convex formulations, the nonconvex ones typically involve much fewer variables,…