A Visual Enhancement Network with Feature Fusion for Image Aesthetic Assessment

Abstract

Image aesthetic assessment (IAA) with neural attention has made significant progress due to its effectiveness in object recognition. Current studies have shown that the features learned by convolutional neural networks (CNN) at different learning stages indicate meaningful information. The shallow feature contains the low-level information of images, and the deep feature perceives the image semantics and themes. Inspired by this, we propose a visual enhancement network with feature fusion (FF-VEN). It consists of two sub-modules, the visual enhancement module (VE module) and the shallow and deep feature fusion module (SDFF module). The former uses an adaptive filter in the spatial domain to simulate human eyes according to the region of interest (ROI) extracted by neural feedback. The latter not only extracts the shallow feature and the deep feature via transverse connection, but also uses a feature fusion unit (FFU) to fuse the pooled features together with the aim of information contribution maximization. Experiments on standard AVA dataset and Photo.net dataset show the effectiveness of FF-VEN.

A Short Video Classification Framework Based on Cross-Modal Fusion

“…It is difficult to match accurate hashtags to meet users' content consumption needs due to insufficient video text information in the method of searching for tags with the same text in videos [15,16]. Moreover, some videos usually do not contain classification information, and video feature analysis is mainly based on understanding visual image information, but lacks text semantic mining, resulting in an underutilization of semantic information [17,18].…”