Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
Greatest papers with code
Real-time Scene Text Detection with Differentiable Binarization
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.
Ranked #2 on
Scene Text Detection
on MSRA-TD500
Balanced Binary Neural Networks with Gated Residual
In this paper, we attempt to maintain the information propagated in the forward process and propose a Balanced Binary Neural Networks with Gated Residual (BBG for short).
Ranked #178 on
Image Classification
on ImageNet
Forward and Backward Information Retention for Accurate Binary Neural Networks
Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks.
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
• hpi-xnor/BMXNet
• 
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks.
Hierarchical binary CNNs for landmark localization with limited resources
To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment.
READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents
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
ICCV 2017
• 1adrianb/binary-human-pose-estimation
• 
(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.
Ranked #1 on
Face Alignment
on AFLW-Full
MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?
However, the binarization of weights and activations leads to feature maps of lower quality and lower capacity and thus a drop in accuracy compared to traditional networks.
HashNet: Deep Learning to Hash by Continuation
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.
Neural Network Compression Framework for fast model inference
In this work we present a new framework for neural networks compression with fine-tuning, which we called Neural Network Compression Framework (NNCF).
Ranked #1 on
Binarization
on ImageNet