Learning to Detect Scene Landmarks for Camera Localization
Source code and data for the CVPR 2022 paper “Learning to Detect Scene Landmarks for Camera Localization”.
Discover an index of datasets, SDKs, APIs and open-source tools developed by Microsoft researchers and shared with the global academic community below. These experimental technologies—available through Azure AI Foundry Labs (opens in new tab)—offer a glimpse into the future of AI innovation.
Source code and data for the CVPR 2022 paper “Learning to Detect Scene Landmarks for Camera Localization”.
Microsoft is working to make data that is relevant to important social problems as open as possible, including by contributing open data ourselves. The Data for Society resource center provides access to Microsoft’s open datasets,…
Here, we provide a plug-in-and-play implementation of Admin, which stabilizes previously-diverged Transformer training and achieves better performance, without introducing additional hyper-parameters. The design of Admin is half-precision friendly and can be reparameterized into the original…
XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale.
Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. This open-source code accompanies our paper “Learning to Extend Molecular Scaffolds with Structural Motifs”, which has been accepted at the ICLR 2022…
Github link to Iris – pretrained summarization models for structured datasets and cardinality estimation.
Ekya is a system which enables continuous learning on resource constrained devices. Given a set of video streams and pre-trained models, Ekya can continuously fine-tune the models to maximize accuracy by intelligently allocating resources between…
Knowledge Infused Decoding (KID) is a decoding algorithm that infuses knowledge (from Wikipedia) into each step decoding of text generation.