We are working on models and algorithms for visual perception. Our work has produced widely used network architectures and random field models for images. We have also created large-scale datasets and benchmarks for training and evaluating broad-competence visual perception systems. One interest is in robust visual perception: models that work reliably in the real world, across many environmental conditions, and can be rapidly deployed in new environments. Another line of inquiry is whether alternatives to convolutional networks can be even more effective than these standard models for images. Another long-term interest is video and how it can be used to enhance visual perception.