ImageNet-R(endition) contains art, cartoons, deviantart, graffiti, embroidery, graphics, origami, paintings, patterns, plastic objects, plush objects, sculptures, sketches, tattoos, toys, and video game renditions of ImageNet classes.
343 PAPERS • 6 BENCHMARKS
COCO-CN is a bilingual image description dataset enriching MS-COCO with manually written Chinese sentences and tags. The new dataset can be used for multiple tasks including image tagging, captioning and retrieval, all in a cross-lingual setting.
20 PAPERS • 3 BENCHMARKS
Former Flickr30k-CN translates the training and validation sets of Flickr30k using machine translation and manually translates the test set. We check the machine-translated results and find two kinds of problems. (1) Some sentences have language problems and translation errors. (2) Some sentences have poor semantics. In addition, the different translation ways between the training set and test set prevent the model from achieving accurate performance. We gather 6 professional English and Chinese linguists to meticulously re-translate all data of Flickr30k and double-check each sentence.
6 PAPERS • 3 BENCHMARKS
Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online. However, the scarcity of similar data in the medical field, specifically in histopathology, has halted similar progress. To enable similar representation learning for histopathology, we turn to YouTube, an untapped resource of videos, offering 1,087 hours of valuable educational histopathology videos from expert clinicians. From YouTube, we curate Quilt: a large-scale vision-language dataset consisting of 768,826 image and text pairs. Quilt was automatically curated using a mixture of models, including large language models), handcrafted algorithms, human knowledge databases, and automatic speech recognition. In comparison, the most comprehensive datasets curated for histopathology amass only around 200K samples. We combine Quilt with datasets, from other sources, including Twitter, research papers, and the internet in general, to create an even larger dat
4 PAPERS • NO BENCHMARKS YET
XTD10 is a dataset for cross-lingual image retrieval and tagging consisting of the MSCOCO2014 caption test dataset annotated in 7 languages that were collected using a crowdsourcing platform.
4 PAPERS • 1 BENCHMARK
WebLI (Web Language Image) is a web-scale multilingual image-text dataset, designed to support Google’s vision-language research, such as the large-scale pre-training for image understanding, image captioning, visual question answering, object detection etc.
1 PAPER • NO BENCHMARKS YET