Obtaining large pre-trained models that can be fine-tuned to new tasks with limited
annotated samples has remained an open challenge for medical imaging data. While pre …

Finding correspondences between 3D shapes is an important and long-standing problem in
computer vision graphics and beyond. A prominent challenge are partial-to-partial shape …

Finding shortest paths on product spaces is a popular approach to tackle numerous variants
of matching problems including the dynamic time war** method for matching signals the …

State-of-the-art medical multi-modal large language models (med-MLLM), like LLaVA-Med
or BioMedGPT, leverage instruction-following data in pre-training. However, those models …

Verifying the neighborhood consensus to remove false correspondence is a popular idea in
feature matching. However, traditional neighborhood consensus only considers spatial …

In biological and medical research, scientists now routinely acquire microscopy images of
hundreds of morphologically heterogeneous organoids and are then faced with the task of …

Graph matching in the setting of federated learning is still an open problem. This paper
proposes an unsupervised federated graph matching algorithm, UFGM, for inferring …

We present a fast, scalable, data-driven approach for solving relaxations of 0-1 integer linear
programs. We use a combination of graph neural networks (GNN) and the Lagrange …

We consider the incomplete multi-graph matching problem, which is a generalization of the
NP-hard quadratic assignment problem for matching multiple finite sets. Multi-graph …

We contribute to the sparsely populated area of unsupervised deep graph matching with
application to keypoint matching in images. Contrary to the standard supervised approach …