natural-language-processing-3
Image-to-Text Retrieval
3260 papers • 126 benchmarks • 313 datasets
Image-text retrieval refers to the process of finding relevant images based on textual descriptions or retrieving textual descriptions that are relevant to a given image. It's an interdisciplinary area that blends techniques from computer vision, natural language processing (NLP), and machine learning. The aim is to bridge the semantic gap between the visual information present in images and the textual descriptions that humans use to interpret them.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in image-to-text-retrieval-3
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Flickr30k
Flickr30k
MS COCO
MS COCO
WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images
Libraries
iUse these libraries to find image-to-text-retrieval-3 models and implementations
3 papers 1,329
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Most implemented papers
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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