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Discrete Reasoning Over Paragraphs

DROP

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Discrete Reasoning Over Paragraphs DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was necessary for prior datasets. The questions consist of passages extracted from Wikipedia articles. The dataset is split into a training set of about 77,000 questions, a development set of around 9,500 questions and a hidden test set similar in size to the development set. Source: https://allennlp.org/drop Image Source: DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

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Raindrop

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Raindrop is a set of image pairs, where each pair contains exactly the same background scene, yet one is degraded by raindrops and the other one is free from raindrops. To obtain this, the images are captured through two pieces of exactly the same glass: one sprayed with water, and the other is left clean. The dataset consists of 1,119 pairs of images, with various background scenes and raindrops. They were captured with a Sony A6000 and a Canon EOS 60. Source: Attentive Generative Adversarial Network for Raindrop Removal from a Single Image

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Video Raindrop and Rain Streak Removal

VRDS

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We generate a synthesized dataset, namely VRDS, with 102 rainy videos from diverse scenarios, and each video frame has the corresponding rain streak map, raindrop mask, and the underlying rain-free clean image (ground truth). This dataset serves as a valuable resource for researchers in this field to develop and test novel methods for the removal of rain streaks and raindrops from video data. To enable our model to cope with various lighting conditions, we considered different weather scenarios, particularly cloudy conditions due to the close correlation between cloudy and rainy conditions. All of the scenarios are present in both the training and test sets, thereby allowing for fair and accurate comparisons between different methods on our dataset. We captured a total of 102 videos, 72 of which were used for training and 30 for testing. The selected video resolution is 1280$\times$720, and each contains 100 frames. Paper: Link Website: Github

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Video Waterdrop Removal Dataset

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Due to the lack of training data for video waterdrop removal, we propose a large-scale synthetic dataset with simulated waterdrops in complex driving scenes on rainy days.

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DropletVideo-10M

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DropletVideo is a project exploring high-order spatio-temporal consistency in image-to-video generation. It is trained on DropletVideo-10M. The model supports multi-resolution inputs, dynamic FPS control for motion intensity, and demonstrates potential for 3D consistency. The model supports multi-resolution inputs, dynamic FPS control for motion intensity, and demonstrates potential for 3D consistency. For further details, you can check our project page as well as the technical report. Features: * Multi-resolution inputs， accommodating pixel values from 512x512x85（default 672x384x85） to 896x896x85（default 1120x640x85, and videos with different aspect ratios. * Dynamic FPS control for motion intensity.

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A Dual-Focused Dataset for Day and Night Raindrop Removal

RaindropClarity

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Existing raindrop removal datasets have two shortcomings. First, they consist of images captured by cameras with a focus on the background, leading to the presence of blurry raindrops. To our knowledge, none of these datasets include images where the focus is specifically on raindrops, which results in a blurry background. Second, these datasets predominantly consist of daytime images, thereby lacking nighttime raindrop scenarios. Consequently, algorithms trained on these datasets may struggle to perform effectively in raindrop-focused or nighttime scenarios. The absence of datasets specifically designed for raindrop-focused and nighttime raindrops constrains research in this area. In this paper, we introduce a large-scale, real-world raindrop removal dataset called Raindrop Clarity. Raindrop Clarity comprises 15,186 high-quality pairs/triplets (raindrops, blur, and background) of images with raindrops and the corresponding clear background images. There are 5,442 daytime raindrop images and 9,744 nighttime raindrop images. Specifically, the 5,442 daytime images include 3,606 raindrop- and 1,836 background-focused images. While the 9,744 nighttime images contain 4,838 raindrop- and 4,906 background-focused images. Our dataset will enable the community to explore background-focused and raindrop-focused images, including challenges unique to daytime and nighttime conditions.

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RaindropsOnWindshield

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RaindropsOnWindshield is a dataset for training and assessing vision algorithms' performance for different tasks of image artifacts detection on either camera lens or windshield. The dataset contains 8190 images, of which 3390 contain raindrops. Images are annotated with the binary mask representing areas with raindrops.

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Stereo Waterdrop

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Steredo Waterdrop is a real-world dataset for research on stereo waterdrop removal. The dataset contains 837 stereo image pairs captured from 129 indoor and outdoor scenes with various waterdrops, disparities, and illumination conditions. We use the ZED 2 stereo camera for data collection.

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100-guidelines-will-expedia-refund-if-price-drops-on-hotel

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