736 citations · 1.1k across the 52 of their papers we have counts for
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High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach, Andreas Blattmann, Dominik Lorenz +2
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data…
Improving Deep Metric Learning by Divide and Conquer
Artsiom Sanakoyeu, Pingchuan Ma, Vadim Tschernezki +1
Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically simi…
Unsupervised View-Invariant Human Posture Representation
Faegheh Sardari, Björn Ommer, Majid Mirmehdi
Most recent view-invariant action recognition and performance assessment approaches rely on a large amount of annotated 3D skeleton data to extract view-invariant features. However…
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
Patrick Esser, Robin Rombach, Andreas Blattmann +1
Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis. Nevertheless, the…
Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Voting
Nikolai Ufer, Sabine Lang, Björn Ommer
The search for specific objects or motifs is essential to art history as both assist in decoding the meaning of artworks. Digitization has produced large art collections, but manua…
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
Timo Milbich, Karsten Roth, Samarth Sinha +3
Deep Metric Learning (DML) aims to find representations suitable for zero-shot transfer to a priori unknown test distributions. However, common evaluation protocols only test a sin…