A long-term goal in AI is to build general-purpose intelligent agents that simultaneously possess the ability to perceive the rich visual environment around us (through vision, audition, or other sensors), reason and infer ...

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 ...

As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but ...

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 ...

Understanding how to model computer vision and natural language jointly is a long-standing challenge in artificial intelligence. In this thesis, I study how modeling vision and language using semantic and pragmatic ...

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 ...

Mosquitoes are directly responsible for the death of more than a million people each year. Yet the ability to mitigate their deadly impact or even monitor them in the wild to better understand their behavior remains ...

In this work, we analyze and apply various recent techniques in visual attribute recognition and labeling on a common benchmark dataset in order to motivate the design of a novel framework for this task. Using the large ...

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 ...

This dissertation presents segmental discriminative analysis techniques for American Sign Language (ASL) recognition and verification. ASL recognition is a sequence classification problem. One of the most successful ...