8 accepted papers
Visual Emotion Analysis (VEA) seeks to anticipate individuals’ emotional reactions to visual stimuli. The subjective perception of visual emotion is an integrated impact of the appearance, scene, and objects presented in an image. It is thus significance to analysis visual emotion by incorporating d…
Although deep neural networks have achieved remarkable success in various computer vision tasks, they face significant challenges in degraded image understanding due to domain shifts caused by quality variations. Drawing biological inspiration from the human visual system (HVS), which dynamically ad…
Advanced Deep Neural Networks (DNNs) perform well for high-quality images, but their performance dramatically decreases for degraded images. Data augmentation is commonly used to alleviate this problem, but using too much perturbed data might seriously decrease the performance on pristine images. To…
In this paper we explore a novel challenging generation task i.e. Handwritten Mathematical Expression Generation (HMEG) from symbolic sequences. Since symbolic sequences are naturally graph-structured data we formulate HMEG as a graph-to-image (G2I) generation problem. Unlike the generation of natur…
In this paper, we present a novel system for Human-Robot Interactive Creation of Artworks (HRICA). Different from previous robot painters, HRICA allows a human user and a robot to alternately draw strokes on a canvas, to collaboratively create a portrait drawing through frequent interactions. The ke…
In this paper, we propose an unsupervised artistic portrait drawing generation method for few-shot datasets based on contrastive learning of style features. Firstly, we construct a discriminative style encoder with contrastive learning, improving the ability of the encoder to separate style features…
Graph Convolutional Neural Networks (GCNs) have been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its nei…
Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification performance. However, current existing methods either neglect image features when expl…