In recent years a vast amount of visual content has been generated and shared from many
fields, such as social media platforms, medical imaging, and robotics. This abundance of …

Image dehazing is an emblematical low-level vision task that aims at restoring haze-free
images from haze images. Recently, some methods adopts deep learning techniques to …

Temporal sentence grounding aims to localize a target segment in an untrimmed video
semantically according to a given sentence query. Most previous works focus on learning …

For a long time, the local descriptors learning benefited from the use of L2 normalization,
which projects the descriptor space onto the hypersphere. However, there is no free lunch in …

With the rapid development of deep neural networks, cross-modal hashing has made great
progress. However, the information of different types of data is asymmetrical, that is to say, if …

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

Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural
networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate …

The Scene Graph Generation (SGG) task aims to detect all the objects and their pairwise
visual relationships in a given image. Although SGG has achieved remarkable progress …

Lipreading refers to understanding and further translating the speech of a video speaker into
textual outputs. State-of-the-art lipreading methods excel in interpreting overlap speakers, ie …

Dropout is a mechanism to prevent deep neural networks from overfitting and improving
their generalization. Random dropout is the simplest method, where nodes are randomly …