Search Results for author:
Found 8 papers, 3 papers with code
NetTrack: Tracking Highly Dynamic Objects with a Net
Most methods that solely depend on coarse-grained object cues, such as boxes and the overall appearance of the object, are susceptible to degradation due to distorted internal relationships of dynamic objects.
NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning
Navigating a nonholonomic robot in a cluttered environment requires extremely accurate perception and locomotion for collision avoidance.
Neuromorphic Synergy for Video Binarization
We also develop an efficient integration method to propagate this binary image to high frame rate binary video.
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
Deep learning models are able to approximate one specific dynamical system but struggle at learning generalisable dynamics, where dynamical systems obey the same laws of physics but contain different numbers of elements (e. g., double- and triple-pendulum systems).
Autofocus for Event Cameras
Focus control (FC) is crucial for cameras to capture sharp images in challenging real-world scenarios.
Synthetic Aperture Imaging With Events and Frames
This paper addresses this problem by leveraging the merits of both events and frames, leading to a fusion-based SAI (EF-SAI) that performs consistently under the different densities of occlusions.
Modular Lagrangian Neural Networks: Designing Structures of Networks with Physical Inductive Biases
At the same time, we directly applied our trained models to predict the motion of multi-pendulum and multi-body systems, demonstrating the intriguing performance in the extrapolation of our method.
Matching Neuromorphic Events and Color Images via Adversarial Learning
The event camera has appealing properties: high dynamic range, low latency, low power consumption and low memory usage, and thus provides complementariness to conventional frame-based cameras.
