128 papers with code • 16 benchmarks • 17 datasets

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Use these libraries to find Binarization models and implementations

Most implemented papers

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

hpi-xnor/BMXNet 16 Mar 2016

We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks.

Real-time Scene Text Detection with Differentiable Binarization

MhLiao/DB 20 Nov 2019

Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text.

Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion

MhLiao/DB 21 Feb 2022

By incorporating the proposed DB and ASF with the segmentation network, our proposed scene text detector consistently achieves state-of-the-art results, in terms of both detection accuracy and speed, on five standard benchmarks.

READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents

Transkribus/TranskribusBaseLineEvaluationScheme 9 May 2017

Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level.

Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources

1adrianb/binary-networks-pytorch ICCV 2017

(d) We present results for experiments on the most challenging datasets for human pose estimation and face alignment, reporting in many cases state-of-the-art performance.

Towards the first adversarially robust neural network model on MNIST

bethgelab/AnalysisBySynthesis ICLR 2019

Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans.

Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction

usstdqq/deep-adaptive-sampling-mask 27 Dec 2018

We propose an XRF image inpainting approach to address the issue of long scanning time, thus speeding up the scanning process while still maintaining the possibility to reconstruct a high quality XRF image.

DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement

dali92002/DE-GAN 17 Oct 2020

Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system.

ReCU: Reviving the Dead Weights in Binary Neural Networks

z-hXu/ReCU ICCV 2021

We prove that reviving the "dead weights" by ReCU can result in a smaller quantization error.

HashNet: Deep Learning to Hash by Continuation

thuml/HashNet ICCV 2017

Learning to hash has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval, due to its computation efficiency and retrieval quality.