Texture understanding: This project investigates various aspects of texture understanding. In the representation side of things we have looked into the connection between classical orderless pooling representations (e.g., bag-of-words models) and deep architectures (e.g., CNNs). This led to a new class of deep architectures (e.g., Fisher vector CNNs, bilinear CNNs, etc.) that generalize better than prior architectures on classification tasks where texture cues are useful such as fine-grained species, material, and scene classification. Here is a recent talk by me on this topic presented at the manifold learning workshop at ICCV 2017. Below are links to individual projects.

3D shape understanding: 3D data is becoming common with the growth of consumer depth scanners and the increasing number of gaming and virtual reality applications. We are developing a number of fundamental techniques to improve the creation and organization of 3D shape data. Below are links to individual projects.

Dark Ecology: This project will leverage large-scale cloud computing and develop novel computer vision, machine learning, and radar analysis methods to measure the densities and velocities of migrating birds across the US. Deep convolutional networks will be trained to discriminate migrating birds from precipitation and other clutter in the radar data. New techniques for domain transfer and weakly supervised training will enable the training of convolutional networks with only modest-sized training sets. Gaussian process (GP) models will be developed to create smooth national maps of migration density and velocity. Novel GP methods and cloud-computing workflows will allow us to scale to massive radar data sets and analyze the more then 200 million archived radar scans. The resulting data and tools will be curated with open access policies, and used by the research team to conduct ecological research about patterns and drivers of continent-scale migration.

Link to the project page.

Other active projects:

Past projects: