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We present a novel framework that jointly detects and tracks 3D vehicle bounding boxes. Our approach leverages 3D pose estimation to learn 2D patch association overtime and uses temporal information from tracking to obtain stable 3D estimation.

Monocular Plan View Networks use a monocular plan view image together with a first-person image to learn a deep driving policy. The plan view image is generated from the first-person image using 3D detection and re-projection.

We compose free-form filters and structured Gaussian filters by convolution to define a more general family of semi-structured filters than can be learned by either alone. Our composition makes receptive field scale, aspect, and orientation differentiable in a low-dimensional parameterization for efficient end-to-end learning.

We represent objects by a single point at their bounding box center. Other properties, such as object size, dimension, 3D extent, orientation, and pose are then regressed directly from image features at the center location.

We propose an object-centric perception approach to deep control problems, and focus our experimentation on au- tonomous driving. Existing end-to-end models are holistic in nature; our approach augments policy learning with explicit representations that provide object-level attention.

We present a novel neural network layer that performs differentiable rasterization of arbitrary simplex-mesh-based geometrical signals (e.g., point clouds, line mesh, triangular mesh, tetrahedron mesh, polygon and polyhedron) of arbitrary dimensions. We further provide examples of incorporating the DDSL into neural networks for tasks such as polygonal image segmentation and neural shape optimization (for MNIST digits and airfoils).

Deep layer aggregation unifies semantic and spatial fusion to better capture what and where. Our aggregation architectures encompass and extend densely connected networks and feature pyramid networks with hierarchical and iterative skip connections that deepen the representation and refine resolution.

A subordinate-level label carries with an implied hierarchy of labels, each corresponding to a level in the domain ontology. Following the assumption that domain experts distinguish finer classes with visually distinctive features, hierarchies thus have embedded and latent knowledge. The goal is to explore the rich semantic relationships among these extra labels.

Several social images and the associated noisy labels which may be correct, incorrect or missing. We learn a joint model to simultaneously segment and recognize visual concept in images.