52 citations · 120 across the 14 of their papers we have counts for
34 papers · 1 filter
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…
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…
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis
Andreas Blattmann, Timo Milbich, Michael Dorkenwald +1
How would a static scene react to a local poke? What are the effects on other parts of an object if you could locally push it? There will be distinctive movement, despite evident v…
Understanding Object Dynamics for Interactive Image-to-Video Synthesis
Andreas Blattmann, Timo Milbich, Michael Dorkenwald +1
What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level…
Stochastic Image-to-Video Synthesis using cINNs
Michael Dorkenwald, Timo Milbich, Andreas Blattmann +3
Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a futur…