The deep learning revolution has initiated a surge of remarkable achievements in diverse research areas where large volumes of data that underlie complex processes exist. Despite the successful application of deep models ...

Artificial Intelligence research has progressed tremendously in the last few years. There has been the introduction of several new multi-modal datasets and tasks due to which it is becoming much harder to compare new ...

Despite the increasingly broad perceptual capabilities of neural networks, applying them to new tasks requires significant engineering effort in data collection and model design. Generally, inductive biases can make this ...

This thesis aims to demonstrate how using 3D cues improves semantic labeling and object classification. Specifically, we will consider depth, surface normals, object classification, and pixel-wise semantic labeling in this ...

Deep networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. While these models enable superior performance, their increasing complexity and lack of decomposability into individually intuitive ...

Deep learning (DL) models require large labeled datasets for training. Practitioners often need to adapt an existing DL model to a different domain. For instance, a practitioner in a company developing autonomous vehicles ...

Discrete Optimization algorithms underlie intelligent decision-making in a wide variety of domains. From airline fleet scheduling to data center resource management and matching in ride-sharing services, decisions are often ...

The widespread use of internet-connected mobile devices, internet of things(IoT) and cloud computing has enabled a large scale collection of personal data, including user profiles, daily activities, locations, photos and ...

In this dissertation, I propose and study a multi-modal Artificial Intelligence (AI) task called Visual Question Answering (VQA) -- given an image and a natural language question about the image (e.g., "What kind of store ...

In this work we propose a new approach to the object detection problem using Deep Neural Network, in the context of traffic sign detection. Our approach simplifies the detection head complexity by making the requirement ...