Omkar2118/datasets huggingface dataset:
SBS Figures

Building a large-scale figure QA dataset requires a considerable amount of work, from gathering and selecting figures to extracting attributes like text, numbers, and colors, and generating QAs. Although recent developments in LLMs have led to efforts to synthesize figures, most of these focus primarily on QA generation. Additionally, creating figures directly using LLMs often encounters issues such as code errors, similar-looking figures, and repetitive content in figures. To address this issue, we present SBSFigures (Stage-by-Stage Synthetic Figures), a dataset for pre-training figure QA. Our proposed pipeline enables the creation of chart figures with complete annotations of the visualized data and dense QA annotations without any manual annotation process. Our stage-by-stage pipeline makes it possible to create diverse topic and appearance figures efficiently while minimizing code errors. Our SBSFigures demonstrate a strong pre-training effect, making it possible to achieve efficient training with a limited amount of real-world chart data starting from our pre-trained weights.

Introducing paper: SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images
Student's EEG Brain Signal

This dataset consists of EEG (Electroencephalogram) recordings collected from students at our college during an educational experiment. The objective of this dataset is to evaluate students' cognitive engagement and learning effectiveness while interacting with educational content. 

For data collection, students were exposed to video lectures across various academic subjects. Each video was designed to cover specific topics in detail, ensuring a structured learning environment. During these sessions, EEG signals were recorded to capture students' brain activity, providing insights into their cognitive states, such as focus, attention, and engagement. 

The primary goal of this dataset is to analyze whether students are actively learning and comprehending the topics presented in the videos. By studying their EEG patterns, we aim to identify neural correlates of learning, such as increased attention span, cognitive processing, and memory retention. This dataset can also serve as a valuable resource for understanding cognitive responses to educational stimuli and exploring potential applications in personalized learning systems.

Introducing paper: Real-time classification of EEG signals using Machine Learning deployment
SegRCDB

Pre-training is a strong strategy for enhancing visual models to efficiently train them with a limited number of labeled images. In semantic segmentation, creating annotation masks requires an intensive amount of labor and time, and therefore, a large-scale pre-training dataset with semantic labels is quite difficult to construct. Moreover, what matters in semantic segmentation pre-training has not been fully investigated. In this paper, we propose the Segmentation Radial Contour DataBase (SegRCDB), which for the first time applies formula-driven supervised learning for semantic segmentation. SegRCDB enables pre-training for semantic segmentation without real images or any manual semantic labels. SegRCDB is based on insights about what is important in pre-training for semantic segmentation and allows efficient pre-training. Pre-training with SegRCDB achieved higher mIoU than the pre-training with COCO-Stuff for fine-tuning on ADE-20k and Cityscapes with the same number of training images. SegRCDB has a high potential to contribute to semantic segmentation pre-training and investigation by enabling the creation of large datasets without manual annotation. The SegRCDB dataset will be released under a license that allows research and commercial use. Code is available at: https://github.com/dahlian00/SegRCDB

Introducing paper: SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning
MMComposition

MMCOMPOSITION is a high-quality benchmark specifically designed to comprehensively evaluate the compositionality of pre-trained Vision-Language Models (VLMs) across three main dimensions—VL compositional perception, reasoning, and probing—which are further divided into 13 distinct categories of questions. While previous benchmarks have mainly focused on text-to-image retrieval, single-choice questions, and open-ended text generation, MMCOMPOSITION introduces a more diverse and challenging set of 4,342 tasks covering both single-image and multi-image scenarios, as well as single-choice and indefinite-choice formats. This expanded range of tasks aims to capture the complex interplay between vision and language more effectively, surpassing earlier benchmarks such as ARO and Winoground by providing a more comprehensive and in-depth assessment of models’ cross-modal compositional capabilities.

Introducing paper: MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models
L+M-24

Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released which are 1) small and scraped from existing databases, 2) large but noisy and constructed by performing entity linking on the scientific literature, and 3) built by converting property prediction datasets to natural language using templates. In this document, we detail the L+M-24 dataset, which has been created for the Language + Molecules Workshop shared task at ACL 2024. In particular, L+M-24 is designed to focus on three key benefits of natural language in molecule design: compositionality, functionality, and abstraction

Introducing paper: L+M-24: Building a Dataset for Language + Molecules @ ACL 2024
Pedestrian yielding

The data collection took place at 18 intersections in Minnesota. Data collection was conducted at 18 sites across Minnesota to capture various road types, and intersection features during the summer months. The TIMs were deployed to each location for between one and two weeks, and video was recorded during daylight hours only. The Minnesota Traffic Observatory (MTO) used the Traffic Information Monitors (TIMs) to collect video data at each site. Raw video data will be available upon request. The collected video data is reduced to numeric data using both video processing techniques and manual data extraction.
Evalmuse-40k

Recently, Text-to-Image (T2I) generation models have achieved significant advancements. Correspondingly, many automated metrics have emerged to evaluate the image-text alignment capabilities of generative models. However, the performance comparison among these automated metrics is limited by existing small datasets. Additionally, these datasets lack the capacity to assess the performance of automated metrics at a fine-grained level. In this study, we contribute an EvalMuse-40K benchmark, gathering 40K image-text pairs with fine-grained human annotations for image-text alignment-related tasks. In the construction process, we employ various strategies such as balanced prompt sampling and data re-annotation to ensure the diversity and reliability of our benchmark.

Introducing paper: EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model Evaluation
Wiki-ImageReview1.0
Full name: https://huggingface.co/datasets/naist-nlp/Wiki-ImageReview1.0

We introduce a novel task for LVLMs, which involves reviewing the good and bad points of a given image. We construct a benchmark dataset containing 207 images selected from Wikipedia. Each image is accompanied by five review texts and a manually annotated ranking of these texts in both English and Japanese.

Introducing paper: IRR: Image Review Ranking Framework for Evaluating Vision-Language Models