Omkar2118/datasets huggingface dataset:
Nightrain

Synthetically Generated Night-time Weather Degraded Database

Introducing paper: Frame-Consistent Recurrent Video Deraining With Dual-Level Flow
Beam-Splitter Deblurring (BSD)
Full name: 3ms-24ms

Using the proposed beam-splitter acquisition system, we have collected a new real-world video deblurring dataset (BSD).

We collected blurry/sharp video sequences for three different blur intensity settings (sharp exposure time – blurry exposure time), 1ms–8ms, 2ms–16ms, and 3ms–24ms(most blur), respectively.

[Github](https://github.com/zzh-tech/ESTRNN)

Introducing paper: Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network
VRDS
Full name: Video Raindrop and Rain Streak Removal

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](https://dl.acm.org/doi/abs/10.1145/3581783.3612001)

Website: [Github](https://github.com/TonyHongtaoWu/ViMP-Net)
AutoHallusion

Large vision-language models (LVLMs) are prone to hallucinations, where certain contextual cues in an image can trigger the language module to produce overconfident and incorrect reasoning about abnormal or hypothetical objects. While some benchmarks have been developed to investigate LVLM hallucinations, they often rely on hand-crafted corner cases whose failure patterns may not generalize well. Additionally, fine-tuning on these examples could undermine their validity. To address this, we aim to scale up the number of cases through an automated approach, reducing human bias in crafting such corner cases. This motivates the development of AutoHallusion, the first automated benchmark generation approach that employs several key strategies to create a diverse range of hallucination examples. Our generated visual-question pairs pose significant challenges to LVLMs, requiring them to overcome contextual biases and distractions to arrive at correct answers. AutoHallusion enables us to create new benchmarks at the minimum cost and thus overcomes the fragility of hand-crafted benchmarks. It also reveals common failure patterns and reasons, providing key insights to detect, avoid, or control hallucinations. Comprehensive evaluations of top-tier LVLMs, e.g., GPT-4V(ision), Gemini Pro Vision, Claude 3, and LLaVA-1.5, show a 97.7% and 98.7% success rate of hallucination induction on synthetic and real-world datasets of AutoHallusion, paving the way for a long battle against hallucinations. The codebase and data can be accessed at [this URL](https://github.com/wuxiyang1996/AutoHallusion).

Introducing paper: AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models