We work on developing artificially intelligent systems that are able to reason
about the visual world. Our research brings together the fields of computer
vision, machine learning, human-computer interaction as well
as fairness, accountability and transparency. We focus on three primary
areas of exploration. The first is developing the fundamental perception
building blocks of visual recognition, such as object detection, image parsing
or human activity recognition. The second is designing human-machine
interaction paradigms to enable computer vision systems to effectively
learn from and collaborate with humans, including but not limited to studying
the interplay between computer vision and natural language. The third is
ensuring the fairness of the vision systems with respect to people of
all backgrounds by improving dataset design, algorithmic methodology, measurement metrics and model
interpretability. |