Omkar2118/datasets huggingface dataset:
QED

**QED** is a linguistically principled framework for explanations in question answering. Given a question and a passage, QED represents an explanation of the answer as a combination of discrete, human-interpretable steps:
sentence selection := identification of a sentence implying an answer to the question
referential equality := identification of noun phrases in the question and the answer sentence that refer to the same thing
predicate entailment := confirmation that the predicate in the sentence entails the predicate in the question once referential equalities are abstracted away.
The QED dataset is an expert-annotated dataset of QED explanations build upon a subset of the Google Natural Questions dataset.

Source: [https://github.com/google-research-datasets/QED](https://github.com/google-research-datasets/QED)
Image Source: [https://github.com/google-research-datasets/QED](https://github.com/google-research-datasets/QED)

Introducing paper: QED: A Framework and Dataset for Explanations in Question Answering
MEF
Full name: Multi-exposure image fusion

Multi-exposure image fusion (MEF) is considered
an effective quality enhancement technique widely adopted in
consumer electronics, but little work has been dedicated to the
perceptual quality assessment of multi-exposure fused images.
In this paper, we first build an MEF database and carry
out a subjective user study to evaluate the quality of images
generated by different MEF algorithms. There are several useful
findings. First, considerable agreement has been observed among
human subjects on the quality of MEF images. Second, no single
state-of-the-art MEF algorithm produces the best quality for
all test images. Third, the existing objective quality models for
general image fusion are very limited in predicting perceived
quality of MEF images. Motivated by the lack of appropriate
objective models, we propose a novel objective image quality
assessment (IQA) algorithm for MEF images based on the
principle of the structural similarity approach and a novel
measure of patch structural consistency. Our experimental results
on the subjective database show that the proposed model well
correlates with subjective judgments and significantly outperforms the existing IQA models for general image fusion. Finally,
we demonstrate the potential application of the proposed model
by automatically tuning the parameters of MEF algorithms
DICM
Full name: DICM

**DICM** is a dataset for low-light enhancement which consists of 69 images collected with commercial digital cameras.

Source: [Deep Retinex Decomposition for Low-Light Enhancement](https://arxiv.org/abs/1808.04560)
Image Source: [GLADNet: Low-Light Enhancement Network with Global Awareness](https://ieeexplore.ieee.org/document/8373911)

Introducing paper: Contrast enhancement based on layered difference representation