Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Datasets
Latest papers without code
HDIB1M -- Handwritten Document Image Binarization 1 Million Dataset
To solve this problem, we propose HDIB1M - a handwritten document image binarization dataset of 1M images.
Binary Graph Neural Networks
In this paper, we present and evaluate different strategies for the binarization of graph neural networks.
BinaryBERT: Pushing the Limit of BERT Quantization
Therefore, we propose ternary weight splitting, which initializes the binary model by equivalent splitting from a half-sized ternary network.
Deep Hashing for Secure Multimodal Biometrics
The efficacy of the proposed approach is shown using a multimodal database of face and iris and it is observed that the matching performance is improved due to the fusion of multiple biometrics.
Exact Reduction of Huge Action Spaces in General Reinforcement Learning
In this work we show how action-binarization in the non-MDP case can significantly improve Extreme State Aggregation (ESA) bounds.
BINARIZATION GENERAL REINFORCEMENT LEARNING PROTEIN FOLDING STARCRAFT
Unsupervised Neural Domain Adaptation for Document Image Binarization
These techniques take advantage of the knowledge learned in one domain, for which labeled data are available, to apply it to other domains for which there are no labeled data.
Improving Accuracy of Binary Neural Networks using Unbalanced Activation Distribution
We also show that adjusting the threshold values of binary activation functions results in the unbalanced distribution of the binary activation, which increases the accuracy of BNN models.
Saliency-based segmentation of dermoscopic images using color information
Skin lesion segmentation is one of the crucial steps for an efficient non-invasive computer-aided early diagnosis of melanoma.
SoftSeg: Advantages of soft versus binary training for image segmentation
SoftSeg produces consistent soft predictions at tissues' interfaces and shows an increased sensitivity for small objects.
BINARIZATION BRAIN TUMOR SEGMENTATION DATA AUGMENTATION TUMOR SEGMENTATION
Application of sequential processing of computer vision methods for solving the problem of detecting the edges of a honeycomb block
A number of image transformations are considered to increase the efficiency of the Hough algorithm.