6 papers
Toward Semantic-Agnostic and Shape-Aware Vision-Language Segmentation Models
Corentin Seutin, Mohamed Amine Ettaki, Michaël Clément +2
Vision-language segmentation models have recently achieved strong performance by leveraging high-level semantic object categories expressed in natural language. However, this seman…
H-SPAM: Hierarchical Superpixel Anything Model
Julien Walther, Rémi Giraud, Michaël Clément
Superpixels offer a compact image representation by grouping pixels into coherent regions. Recent methods have reached a plateau in terms of segmentation accuracy by generating noi…
Generalized Shortest Path-based Superpixels for 3D Spherical Image Segmentation
Rémi Giraud, Rodrigo Borba Pinheiro, Yannick Berthoumieu
The growing use of wide angle image capture devices and the need for fast and accurate image analysis in computer visions have enforced the need for dedicated under-representation…
Superpixel Segmentation: A Long-Lasting Ill-Posed Problem
Rémi Giraud, Michaël Clément
For many years, image over-segmentation into superpixels has been essential to computer vision pipelines, by creating homogeneous and identifiable regions of similar sizes. Such co…
Deep Spherical Superpixels
Rémi Giraud, Michaël Clément
Over the years, the use of superpixel segmentation has become very popular in various applications, serving as a preprocessing step to reduce data size by adapting to the content o…
Superpixel Anything: A general object-based framework for accurate yet regular superpixel segmentation
Julien Walther, Rémi Giraud, Michaël Clément
Superpixels are widely used in computer vision to simplify image representation and reduce computational complexity. While traditional methods rely on low-level features, deep lear…