Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
We identify label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets, and subsequently study the potential for these label errors to affect benchmark results. Errors in test sets are numerous and widespread: we estimate an average of at least 3.3% errors across the 10 datasets, where for example label errors comprise at least 6% of the Ima
IBM Blog
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コラム - グーグルのクラウドを支えるテクノロジー | 第35回 社内横断データセット検索システム「Goods」（パート1）｜CTC教育サービス 研修/トレーニング
[IT研修]注目キーワード Python UiPath(RPA) 最新技術動向 Microsoft Azure Docker Kubernetes 第35回 社内横断データセット検索システム「Goods」（パート1） (中井悦司) 2018年3月 はじめに 今回からは、2016年に公開された論文「Goods: Organizing Google's Datasets」をもとにして、Bigtable、Spanner、GFSと言った、Google社内のデータストアを横断的に検索可能にするツール「Goods（Google Dataset Search）」を紹介していきます。最近、複数のデータストアにまたがった情報を横断的に検索・活用する手法として、「データレイク」という考え方を耳にするようになりました。これは、260億件という膨大な数のデータセットに対してデータレイクの仕組みを実現した、Goog
GitHub - lemondan/HumanParsing-Dataset
This human parsing dataset includes the detailed pixel-wise annotations for fashion images, which is proposed in our TPAMI paper "Deep Human Parsing with Active Template Regression", and ICCV 2015 paper "Human Parsing with Contextualized Convolutional Neural Network". You can download the dataset from this link. http://pan.baidu.com/s/1qY8bToS passwd：kjgk We will mainly maintain a new LIP benchmar
Lukas Bossard
この [引用先] の件数には Scholar 内の次の論文への引用も含まれています。* が付いた論文は、プロフィール内の論文とは異なる場合があります。
DeepFashion Database
News 2022-06-01 We release the DeepFashion-MultiModal dataset with rich multi-modal annotations, including manually annotated human parsing labels, manually annotated human keypoints, manually annotated fine-grained labels and textual descriptions. 2020-05-04 Parsing mask annotations and dense pose annotations have been added to “In-shop Clothes Retrieval Benchmark”. Fine-grained attribute annotat
Announcing YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research
Posted by Sudheendra Vijayanarasimhan and Paul Natsev, Software Engineers Many recent breakthroughs in machine learning and machine perception have come from the availability of large labeled datasets, such as ImageNet, which has millions of images labeled with thousands of classes. Their availability has significantly accelerated research in image understanding, for example on detecting and class
Introducing the Open Images Dataset
Posted by Ivan Krasin and Tom Duerig, Software Engineers In the last few years, advances in machine learning have enabled Computer Vision to progress rapidly, allowing for systems that can automatically caption images to apps that can create natural language replies in response to shared photos. Much of this progress can be attributed to publicly available image datasets, such as ImageNet and COCO
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GitHub - iai-group/DBpedia-Entity: DBpedia-Entity v2: A Test Collection for Entity Search
Cookpad Parsed Corpus: Linguistic Annotations of Japanese Recipes
Dataset or Not? A study on the veracity of semantic markup for dataset pages
CVPR 2019 Open Access Repository
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
An Analysis of Online Datasets Using Dataset Search (Published, in Part, as a Da
KnowledgeNet: A Benchmark Dataset for Knowledge Base Population
Google Dataset Search by the Numbers
p553-maddox.pdf
Dataset search: a survey
Principles of Dataset Versioning: Exploring the Recreation/Storage Tradeoff
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