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Found 10 papers, 4 papers with code
Forecasting from LiDAR via Future Object Detection
Object detection and forecasting are fundamental components of embodied perception.
Implicit Neural Deformation for Multi-View Face Reconstruction
In this work, we present a new method for 3D face reconstruction from multi-view RGB images.
FOVEA: Foveated Image Magnification for Autonomous Navigation
Efficient processing of high-res video streams is safety-critical for many robotics applications such as autonomous driving.
Neighborhood-Aware Neural Architecture Search
To facilitate the application to gradient-based algorithms, we also propose a differentiable representation for the neighborhood of architectures.
Towards Streaming Perception
While past work has studied the algorithmic trade-off between latency and accuracy, there has not been a clear metric to compare different methods along the Pareto optimal latency-accuracy curve.
Ranked #2 on
Real-Time Object Detection
on Argoverse-HD (Detection-Only, Val)
(using extra training data)
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.
Photo-Sketching: Inferring Contour Drawings from Images
Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision.
Brute-Force Facial Landmark Analysis With A 140,000-Way Classifier
While most prior work treats this as a regression problem, we instead formulate it as a discrete $K$-way classification task, where a classifier is trained to return one of $K$ discrete alignments.
Guaranteed Parameter Estimation for Discrete Energy Minimization
In this work, we propose a method to overcome this limitation through exploiting the properties of the joint problem of training time inference and learning.
Complexity of Discrete Energy Minimization Problems
Specifically, we show that general energy minimization, even in the 2-label pairwise case, and planar energy minimization with three or more labels are exp-APX-complete.
