Using a CNN Model to Assess Visual Artwork's Creativity

Assessing artistic creativity has long challenged researchers, with traditional methods proving time-consuming. Recent studies have applied machine learning to evaluate creativity in drawings, but not paintings. Our research addresses this gap by developing a CNN model to automatically assess the creativity of students' paintings. Using a dataset of 600 paintings by professionals and children, our model achieved 90% accuracy and faster evaluation times than human raters. This approach demonstrates the potential of machine learning in advancing artistic creativity assessment, offering a more efficient alternative to traditional methods.

Distance Guided Channel Weighting for Semantic Segmentation

Recent works have achieved great success in improving the performance of multiple computer vision tasks by capturing features with a high channel number utilizing deep neural networks. However, many channels of extracted features are not discriminative and contain a lot of redundant information. In this paper, we address above issue by introducing the Distance Guided Channel Weighting (DGCW) Module. The DGCW module is constructed in a pixel-wise context extraction manner, which enhances the discriminativeness of features by weighting different channels of each pixel's feature vector when modeling its relationship with other pixels. It can make full use of the high-discriminative information while ignore the low-discriminative information containing in feature maps, as well as capture the long-range dependencies. Furthermore, by incorporating the DGCW module with a baseline segmentation network, we propose the Distance Guided Channel Weighting Network (DGCWNet). We conduct extensive experiments to demonstrate the effectiveness of DGCWNet. In particular, it achieves 81.6% mIoU on Cityscapes with only fine annotated data for training, and also gains satisfactory performance on another two semantic segmentation datasets, i.e. Pascal Context and ADE20K. Code will be available soon at https://github.com/LanyunZhu/DGCWNet.