13 datasets found

Groups: Multimodal Learning Organizations: No Organization

Filter Results

  • LLaVA 158k

    The LLaVA 158k dataset is a large-scale multimodal learning dataset, which is used for training and testing multimodal large language models.

  • Multimodal Robustness Benchmark

    The MMR benchmark is designed to evaluate MLLMs' comprehension of visual content and robustness against misleading questions, ensuring models truly leverage multimodal inputs...

  • Multimodal Visual Patterns (MMVP) Benchmark

    The Multimodal Visual Patterns (MMVP) benchmark is a dataset used to evaluate the visual question answering capabilities of multimodal large language models (MLLMs).

  • LLaMA-7B

    A benchmark for evaluating the perception ability of Large Vision-Language Models (LVLMs) via various subtasks and scenarios.

  • Dysca: A Dynamic and Scalable Benchmark for Evaluating Perception Ability of ...

    Dysca is a dynamic and scalable benchmark for evaluating the perception ability of Large Vision-Language Models (LVLMs) via various subtasks and scenarios.

  • MSRVTT-QA

    Video question answering (VideoQA) requires systems to understand the visual information and infer an answer for a natural language question from it.

  • Youtube2Text-QA

    Video question answering task, which requires machines to answer questions about videos in a natural language form.

  • MSVD-QA

    The MSVD-QA dataset is a benchmark for video question answering, containing 1,970 videos with multiple-choice questions.

  • TGIF-QA

    The TGIF-QA dataset consists of 165165 QA pairs chosen from 71741 animated GIFs. To evaluate the spatiotemporal reasoning ability at the video level, TGIF-QA dataset designs...

  • AVQA

    The AVQA dataset contains 57,015 videos and 57,335 question-and-answer pairs.

  • Music-AVQA

    The Music-AVQA dataset contains multiple question-and-answer pairs, with 9,288 videos and 45,867 question-and-answer pairs.

  • Audio-Visual Question Answering

    Audio-visual question answering (AVQA) requires reference to video content and auditory information, followed by correlating the question to predict the most precise answer.

  • Conceptual Captions

    The dataset used in the paper "Scaling Laws of Synthetic Images for Model Training". The dataset is used for supervised image classification and zero-shot classification tasks.