Deep neural networks (DNNs) are machine learning algorithms that have revolutionized
computer vision due to their remarkable successes in tasks like object classification and …

Realizing high performance of ordinary robots is one of the core problems in robotic
research. Improving the performance of ordinary robots usually relies on the collaborative …

Biological systems understand the world by simultaneously processing high-dimensional
inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The …

Establishing correspondence between images or scenes is a significant challenge in
computer vision, especially given occlusions, viewpoint changes, and varying object …

The biologically inspired spiking neurons used in neuromorphic computing are nonlinear
filters with dynamic state variables—very different from the stateless neuron models used in …

We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway,
operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating …

Visual object tracking is an important area in computer vision, and many tracking algorithms
have been proposed with promising results. Existing object tracking approaches can be …

We learn to compute optical flow by combining a classical spatial-pyramid formulation with
deep learning. This estimates large motions in a coarse-to-fine approach by war** one …

Spatiotemporal feature learning in videos is a fundamental problem in computer vision. This
paper presents a new architecture, termed as Appearance-and-Relation Network (ARTNet) …

Despite great efforts over several decades, our best models of primary visual cortex (V1) still
predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited …