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Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via do-calculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation.

Image restoration and image enhancement are critical image processing tasks since good image quality is mandatory for many image applications. We are particularly interested in the restoration of ill-exposed images. These effects are caused by sensor limitation or optical arrangement. They prevent the details of the scene from being adequately represented in the captured image. We proposed a deep neural network model due to the number of uncontrolled variables that impact the acquisition.

Humans have an incredible ability to process and understand
information from multiple sources such as images,
video, text, and speech. Recent success of deep neural
networks has enabled us to develop algorithms which give
machines the ability to understand and interpret this information.
There is a need to both broaden their applicability and
develop methods which correlate visual information along
with semantic content. We propose a unified model which
jointly trains on images and captions, and learns to generate

A promising way to deploy Artificial Intelligence (AI)-based services on mobile devices is to run a part of the AI model (a deep neural network) on the mobile itself, and the rest in the cloud. This is sometimes referred to as collaborative intelligence. In this framework, intermediate features from the deep network need to be transmitted to the cloud for further processing. We study the case where such features are used for multiple purposes in the cloud (multi-tasking) and where they need to be compressible in order to allow efficient transmission to the cloud.

In recent years, it has become a trend for people to manipulate their own portraits before posting them on a social networking service. However, it is difficult to get a desired portrait after manipulation without sufficient experience or skill. To obtain a simpler and more effective portrait manipulation technique, we consider an automated portrait manipulation method based on five impression words: clear, sweet, elegant, modern, and dynamic.