Image and text matching plays a crucial role in bridging the cross-modal gap between vision
and language, and has achieved great progress due to the deep learning. However, the …

Abstract Convolutional Neural Networks (CNNs) have achieved great success in computer
vision applications. However, due to the high requirements for computation power and …

Road segmentation plays an important role in navigation systems and autonomous driving.
However, many methods in road segmentation are based on supervised learning and suffer …

Haze is a ubiquitous atmospheric phenomenon that seriously influences the visibility of
images. To this end, numerous image dehazing models have been proposed to improve the …

Abstract Machine attention mechanisms are widely used in the task of image captioning.
Such mechanisms dynamically focus on different regions to guide the word generation …

Fine-grained image captioning is a focal point in the vision-to-language task and has
attracted considerable attention for generating accurate and contextually relevant image …

The robustness of deep neural networks is an increasingly essential issue as they become
more and more prevalent in several real-world applications like autonomous vehicles. If …

Image inpainting is widely used to fill the damaged or masked area in an image with realistic
visual contents. However, most existing inpainting methods have limitations in …

Image captioning is a multi-modal task to describe an image into natural language. Many
state-of-the-art methods generally take the encoder–decoder architecture, encode an image …

Visual question answering (VQA), along with multiple types of image and textual questions,
makes it a challenging task to infer the correct answer. Consequently, traditional methods …