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Found 5 papers, 0 papers with code
Learning to dehaze with polarization
Haze, a common kind of bad weather caused by atmospheric scattering, decreases the visibility of scenes and degenerates the performance of computer vision algorithms.
EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution
To reconstruct high-resolution intensity images from event data, we propose EvIntSR-Net that converts event data to multiple latent intensity frames to achieve super-resolution on intensity images in this paper.
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging
A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR).
Pushing the limits of RNN Compression
This paper introduces a method to compress RNNs for resource constrained environments using Kronecker product (KP).
Compressing RNNs for IoT devices by 15-38x using Kronecker Products
Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size. As a result, there is a need for compression techniques that can significantly compress RNNs without negatively impacting task accuracy.
