Omkar2118/datasets huggingface dataset:
CameraFusion

We present a novel approach to reference-based super-resolution (RefSR) with the focus on real-world dual-camera super-resolution (DCSR).
This dataset currently consists of 143 pairs of telephoto and wide-angle images in 4K resolution captured by smartphone dual-cameras.
See our paper for more details: Dual-Camera Super-Resolution with Aligned Attention Modules.

Introducing paper: Dual-Camera Super-Resolution with Aligned Attention Modules
StoryCloze

Representation and learning of commonsense knowledge is one of the foundational problems in the quest to enable deep language understanding. This issue is particularly challenging for understanding casual and correlational relationships between events. While this topic has received a lot of interest in the NLP community, research has been hindered by the lack of a proper evaluation framework. This paper attempts to address this problem with a new framework for evaluating story understanding and script learning: the 'Story Cloze Test'. This test requires a system to choose the correct ending to a four-sentence story. We created a new corpus of ~50k five-sentence commonsense stories, ROCStories, to enable this evaluation. This corpus is unique in two ways: (1) it captures a rich set of causal and temporal commonsense relations between daily events, and (2) it is a high quality collection of everyday life stories that can also be used for story generation. Experimental evaluation shows that a host of baselines and state-of-the-art models based on shallow language understanding struggle to achieve a high score on the Story Cloze Test. We discuss these implications for script and story learning, and offer suggestions for deeper language understanding.

Introducing paper: A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories
MSU Shot Boundary Detection Benchmark

This is a dataset for a shot boundary detection task. The dataset contains 2 existing datasets and 19 manually marked up open source videos with a total length of more than 1200 minutes and 10000 scene transitions. The dataset includes different types of videos with different resolutions from 360×288 to 1920×1080 in MP4 and MKV formats. Videos include samples in RGB scale or in grayscale with FPS from 23 to 60.

Introducing paper: Shot boundary detection method based on a new extensive dataset and mixed features
Real-world graphs for betweenness-centrality ranking estimation

The ground truth betweenness-centralities for the real-world graphs are provided by AlGhamdi et al. (2017), which are computed by the parallel implementation of Brandes algorithm on a 96000-core supercomputer. The ground truth scores for the synthetic networks are provided by Fan et al. (2019) and are computed using the graph-tool (Peixoto, 2014) library.

The presented approach is compared to several baseline models. The performance of those models are adopted from the benchmark provided by Fan et al. (2019):

ABRA (Riondato & Upfal, 2018): Samples pairs of nodes until the desired accuracy is reached. Where the error tolerance λ was set to 0.01 and the probability δ was set to 0.1.

RK (Riondato & Kornaropoulos, 2014): The number of pairs of nodes is determined by the diameter of the network. Where the error tolerance and the probability were set similar to ABRA.

k-BC (Pfeffer & Carley, 2012): Does only k steps of Brandes algorithm (Brandes, 2001) which was set to 20% of the diameter of the network.

KADABRA (Borassi & Natale, 2019): Uses bidirectional BFS to sample the shortest paths. The variant where it computest the top-k% nodes with the highest betweenness-centrality was used. The error tolerance and probability were set to be the same as ABRA and RK.

Node2Vec (Grover & Leskovec, 2016): Uses a biased random walk to aggregate information from the neighbors. The vector representations of each node were then mapped with a trained MLP to ranking scores.

DrBC (Fan et al., 2019): Shallow graph convolutional network that outputs a ranking score for each node by propagating through the neighbors with a walk length of 5.