Omkar2118/datasets huggingface dataset:
RaDelft

The RaDelft dataset is a novel, large-scale, real-life, and multi-sensor dataset that has been recorded using a demonstrator vehicle in different locations in the city of Delft. It contains data from a lidar, an imaging radar board, a camera, and the ego vehicle’s odometry. Check the reference paper for more details about the data collection and the sensors' setup.

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Introducing paper: A Deep Automotive Radar Detector using the RaDelft Dataset
AGGA: A Dataset of Academic Guidelines for Generative AIs

AGGA (Academic Guidelines for Generative AIs) is a dataset of 80 academic guidelines for the usage of generative AIs and large language models in academia, selected systematically and collected from official university websites across six continents. Comprising 181,225 words, the dataset supports natural language processing tasks such as language modeling, sentiment and semantic analysis, model synthesis, classification, and topic labeling. It can also serve as a benchmark for ambiguity detection and requirements categorization. This resource aims to facilitate research on AI governance in educational contexts, promoting a deeper understanding of the integration of AI technologies in academia.
Video-MME

Video-MME stands for **Video Multi-Modal Evaluation**. It is the first-ever comprehensive evaluation benchmark specifically designed for Multi-modal Large Language Models (MLLMs) in video analysis¹. This benchmark is significant because it addresses the need for a high-quality assessment of MLLMs' performance in processing sequential visual data, which has been less explored compared to their capabilities in static image understanding.

The Video-MME benchmark is characterized by its:
1. **Diversity** in video types, covering 6 primary visual domains with 30 subfields for broad scenario generalizability.
2. **Duration** in the temporal dimension, including short-, medium-, and long-term videos ranging from 11 seconds to 1 hour, to assess robust contextual dynamics.
3. **Breadth** in data modalities, integrating multi-modal inputs such as video frames, subtitles, and audios.
4. **Quality** in annotations, with rigorous manual labeling by expert annotators for precise and reliable model assessment¹.

The benchmark includes 900 videos totaling 256 hours, manually selected and annotated, resulting in 2,700 question-answer pairs. It has been used to evaluate various state-of-the-art MLLMs, including the GPT-4 series and Gemini 1.5 Pro, as well as open-source image and video models¹. The findings from Video-MME highlight the need for further improvements in handling longer sequences and multi-modal data, which is crucial for the advancement of MLLMs¹.

(1) [2405.21075] Video-MME: The First-Ever Comprehensive Evaluation .... https://arxiv.org/abs/2405.21075.
(2) Video-MME. https://video-mme.github.io/home_page.html.
(3) Video-MME: Welcome. https://video-mme.github.io/.
(4) undefined. https://doi.org/10.48550/arXiv.2405.21075.

Introducing paper: Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis