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Found 5 papers, 2 papers with code
HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction
Concretely, we introduce a hybrid representation called HIQuery to represent all map elements, and propose a point-element interactor to interactively extract and encode the hybrid information of elements, e. g. point position and element shape, into the HIQuery.
Yes, IoU loss is submodular - as a function of the mispredictions
This note is a response to [7] in which it is claimed that [13, Proposition 11] is false.
An Efficient Decomposition Framework for Discriminative Segmentation with Supermodular Losses
These loss functions do not necessarily have the same structure as the one used by the segmentation inference algorithm, and in general, we may have to resort to generic submodular minimization algorithms for loss augmented inference.
A Convex Surrogate Operator for General Non-Modular Loss Functions
This convex surro-gate is based on a submodular-supermodular decomposition for which the existence and uniqueness is proven in this paper.
The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses
The main tools for constructing convex surrogate loss functions for set prediction are margin rescaling and slack rescaling.
