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
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
FashionGraph: understanding fashion data using scene graph generation | HCU Psycho-Informatics Group
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https://datacentricai.org/neurips21/papers/118_CameraReady_DataCentricWorkshop2021_CR_Final.pdf
https://ceur-ws.org/Vol-2980/paper362.pdf
Visual Information Oriented Knowledge Graph
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.
GitHub - NVIDIA/ContrastiveLosses4VRD: Implementation for the CVPR2019 paper "Graphical Contrastive Losses for Scene Graph Generation"
