The current state-of-the-art on VQA v2 test-std is BEiT-3. See a full comparison of 39 papers with code. |
Visual Question Answering (VQA) - Papers With Code paperswithcode.com › task › visual-question-an...
The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language. Image Source: visualqa.org ... |
This paper presents OmniVL, a new foundation model to support both image-language and video-language tasks using one universal architecture. |
1 июн. 2021 г. · We introduce Adversarial VQA, a new large-scale VQA benchmark, collected iteratively via an adversarial human-and-model-in-the-loop procedure. |
We introduce the VizWiz-VQA-Grounding dataset, the first dataset that visually grounds answers to visual questions asked by people with visual impairments. We ... |
VQA-VS proposes a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. |
20 нояб. 2022 г. · Our method generates human-readable explanations while maintaining SOTA VQA accuracy on the GQA-REX (77.49%) and VQA-E (71.48%) datasets. |
Knowledge-based Visual Question Answering (KVQA) requires both image and world knowledge to answer questions. Current methods first retrieve knowledge from ... |
What is Visual Question Answering? - Hugging Face huggingface.co › tasks › visual-question-answer...
Visual Question Answering is the task of answering open-ended questions based on an image. They output natural language responses to natural language questions. |
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