The embedding spaces of image models have been shown to encode a range o...
Obtaining large pre-trained models that can be fine-tuned to new tasks w...
We propose a graph clustering formulation based on multicut (a.k.a. weig...
Collecting large-scale medical datasets with fully annotated samples for...
The graph matching optimization problem is an essential component for ma...
We present a fast, scalable, data-driven approach for solving linear
rel...
We present a scalable combinatorial algorithm for globally optimizing ov...
Structured prediction problems are one of the fundamental tools in machi...
Multi-Camera Multi-Object Tracking is currently drawing attention in the...
We present a massively parallel Lagrange decomposition method for solvin...
We propose a highly parallel primal-dual algorithm for the multicut (a.k...
We present an efficient approximate message passing solver for the lifte...
We propose an end-to-end trainable architecture for simultaneous semanti...
We present an extension to the disjoint paths problem in which additiona...
We propose a fast approximate solver for the combinatorial problem known...
We consider the MAP-inference problem for graphical models, which is a v...
Building on recent progress at the intersection of combinatorial optimiz...
We consider general discrete Markov Random Fields(MRFs) with additional
...
The matching of multiple objects (e.g. shapes or images) is a fundamenta...
We present a new proximal bundle method for Maximum-A-Posteriori (MAP)
i...
We study the quadratic assignment problem, in computer vision also known...
We propose a general dual ascent framework for Lagrangean decomposition ...
We propose a dual decomposition and linear program relaxation of the NP ...
We present a probabilistic graphical model formulation for the graph
clu...
We consider the NP-hard problem of MAP-inference for undirected discrete...
We consider the energy minimization problem for undirected graphical mod...
We present a novel variational approach to image restoration (e.g.,
deno...