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Found 3 papers, 1 papers with code
Boter: Bootstrapping Knowledge Selection and Question Answering for Knowledge-based VQA
Knowledge-based Visual Question Answering (VQA) requires models to incorporate external knowledge to respond to questions about visual content.
Knowledge Condensation and Reasoning for Knowledge-based VQA
We condense the retrieved knowledge passages from two perspectives.
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
Based on the proposed VAST-27M dataset, we train an omni-modality video-text foundational model named VAST, which can perceive and process vision, audio, and subtitle modalities from video, and better support various tasks including vision-text, audio-text, and multi-modal video-text tasks (retrieval, captioning and QA).
Ranked #1 on
Image Captioning
on COCO Captions
(SPICE metric, using extra
training data)
