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Found 8 papers, 4 papers with code
H-NeXt: The next step towards roto-translation invariant networks
The widespread popularity of equivariant networks underscores the significance of parameter efficient models and effective use of training data.
NeRD: Neural field-based Demosaicking
We introduce NeRD, a new demosaicking method for generating full-color images from Bayer patterns.
Real-Time Wheel Detection and Rim Classification in Automotive Production
This paper proposes a novel approach to real-time automatic rim detection, classification, and inspection by combining traditional computer vision and deep learning techniques.
FMODetect: Robust Detection of Fast Moving Objects
Compared to other methods, such as deblatting, the inference is of several orders of magnitude faster and allows applications such as real-time fast moving object detection and retrieval in large video collections.
Learning-based Tracking of Fast Moving Objects
Tracking fast moving objects, which appear as blurred streaks in video sequences, is a difficult task for standard trackers as the object position does not overlap in consecutive video frames and texture information of the objects is blurred.
Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects
We propose a novel method that tracks fast moving objects, mainly non-uniform spherical, in full 6 degrees of freedom, estimating simultaneously their 3D motion trajectory, 3D pose and object appearance changes with a time step that is a fraction of the video frame exposure time.
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The World of Fast Moving Objects
The notion of a Fast Moving Object (FMO), i. e. an object that moves over a distance exceeding its size within the exposure time, is introduced.
