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computer-vision-7

Generative Visual Question Answering

3260 papers • 126 benchmarks • 313 datasets

Generating answers in free form to questions posed about images.

(Image credit: Papersgraph)

Benchmarks

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Dataset

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PMC-VQA

Libraries

Use these libraries to find generative-visual-question-answering-7 models and implementations

Datasets

PMC-VQA

Subtasks

Video-based Generative Performance Benchmarking

Most implemented papers

Flamingo: a Visual Language Model for Few-Shot Learning

Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, A. Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, O. Vinyals, Andrew Zisserman, K. Simonyan•Thu Apr 28 2022

This work introduces Flamingo, a family of Visual Language Models (VLM) with this ability to bridge powerful pretrained vision-only and language-only models, handle sequences of arbitrarily interleaved visual and textual data, and seamlessly ingest images or videos as inputs.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

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BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

S. Savarese, Junnan Li, Dongxu Li, Steven C. H. Hoi•Sun Jan 29 2023

BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods, and is demonstrated's emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.

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PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering

Yanfeng Wang, Weidi Xie, Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao, Weixiong Lin, Ya Zhang•Tue May 16 2023

This study reframe the problem of MedVQA as a generation task that naturally follows the human-machine interaction and proposes a generative-based model for medical visual understanding by aligning visual information from a pre-trained vision encoder with a large language model.

273 0

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Multimodal Prompt Retrieval for Generative Visual Question Answering

Junjie Hu, Timothy Ossowski•Thu Jun 29 2023

A novel generative model enhanced by multi-modal prompt retrieval (MPR) that integrates retrieved prompts and multimodal features to generate answers in free text that enables rapid zero-shot dataset adaptation to unseen data distributions and open-set answer labels across datasets.

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Generative Visual Question Answering | State-of-the-Art