Visual Relationship Detection with Language Priors
Visual relationships capture a wide variety of interactions between pairs of objects in images (e.g. "man riding bicycle" and "man pushing bicycle"). Consequently, the set of possible relationships is extremely large and it is difficult to obtain sufficient training examples for all possible relationships. Because of this limitation, previous work on visual relationship detection has concentrated
Diffbot、AIによるナレッジグラフをローンチ——人、場所、モノに関する1兆件の情報を網羅 - BRIDGE（ブリッジ）
Image Credit: Diffbot Google サーチでセレブや有名ランドマーク、あるいは製品についてサーチしたとき、結果ページの右側に表示されるインフォボックスを目にしたことがある人は多いだろう。そこに表示される情報は、Google の Knowledge Graph から引用された情報に基づいている。Knowledge Graph とは、ウェブ検索や Google Home をはじめとするスマートスピーカーの検索結果を向上させるために使用されるエンティティ・データベースのことだ。Knowledge Graph には、16億件以上もの情報が記録されている。その大半は、人、場所、モノについてのよくある質問への回答のために、人間の作業チームが数百万単位のウェブサイトを定期的にチェックし、クラウド上で集めたものである。 しかしながら、Mike Tung 氏に言わせれば、それを行うもっ
Labeling and Crowdsourcing - Data-centric AI Resource Hub
Michael Bernstein is an Associate Professor of Computer Science and STMicroelectronics Faculty Scholar at Stanford University, where he is a member of the Human-Computer Interaction Group. His research focuses on the design of social computing systems. This research has won best paper awards at top conferences in human-computer interaction, including CHI, CSCW, and UIST. Michael has been recognize
Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images - MIT
♰ Universitat Politecnica de Catalunya ✦ Massachusetts Institute of Technology ✥ Qatar Computing Research Institute Abstract In this work we train a neural network to learn a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Moreover, we demonstrate that regularization via the addition of a high-level classification objective both improves retr
An Introduction to Knowledge Graphs
Knowledge Graphs (KGs) have emerged as a compelling abstraction for organizing the world’s structured knowledge, and as a way to integrate information extracted from multiple data sources. Knowledge graphs have started to play a central role in representing the information extracted using natural language processing and computer vision. Domain knowledge expressed in KGs is being input into machine
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
We consider the problem of zero-shot recognition: learning a visual classifier for a category with zero training examples, just using the word embedding of the category and its relationship to other categories, which visual data are provided. The key to dealing with the unfamiliar or novel category is to transfer knowledge obtained from familiar classes to describe the unfamiliar class. In this pa
FashionGraph: understanding fashion data using scene graph generation | HCU Psycho-Informatics Group
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GitHub - CharlieDDDD/AISurveyPapers: Large Visual Language Model(LVLM), Large Language Model(LLM), Multimodal Large Language Model(MLLM), Alignment, Agent, AI System, Survey
Data Labeling Best Practices - Video | MLOps Community
https://datacentricai.org/neurips21/papers/118_CameraReady_DataCentricWorkshop2021_CR_Final.pdf
Visual Information Oriented Knowledge Graph
https://ceur-ws.org/Vol-2980/paper362.pdf
Visual Information Oriented Knowledge Graph
http://jiayoutiantian.github.io/publications/
Google AI Open Images - Visual Relationship Track
GitHub - shabnamsadegh/FashionGraph: FashionGraph: understanding fashion data using scene graph generation [ICPR2020]
GitHub - bknyaz/sgg: Train Scene Graph Generation for Visual Genome and GQA in PyTorch >= 1.2 with improved zero and few-shot generalization.
CVPR 2019 Open Access Repository
Compositionality in Computer Vision
GitHub - NVIDIA/ContrastiveLosses4VRD: Implementation for the CVPR2019 paper "Graphical Contrastive Losses for Scene Graph Generation"