2 citations · 5 across the 9 of their papers we have counts for
10 papers
Region Aware Squeeze-and-Excitation Networks for Image Segmentation
Jack Roberts, Praneeth Tummala, Kavi Sarna +1
Squeeze-and-Excitation (SE) modules improve convolutional networks by modeling channel-wise dependencies, but global average pooling collapses spatial structure into a single per-c…
IGLU: The Integrated Gaussian Linear Unit Activation Function
Mingi Kang, Zai Yang, Jeova Farias Sales Rocha Neto
Activation functions are fundamental to deep neural networks, governing gradient flow, optimization stability, and representational capacity. Within historic deep architectures, wh…
Unifying Convolution and Attention via Convolutional Nearest Neighbors
Mingi Kang, Jeová Farias Sales Rocha Neto
Convolutional Neural Networks and Vision Transformers are the two dominant architectural families in computer vision, defined by spatially local convolution and global self-attenti…
Spectral Clustering of Categorical and Mixed-type Data via Extra Graph Nodes
Dylan Soemitro, Jeova Farias Sales Rocha Neto
Clustering data objects into homogeneous groups is one of the most important tasks in data mining. Spectral clustering is arguably one of the most important algorithms for clusteri…
Patch-Based Deep Unsupervised Image Segmentation using Graph Cuts
Isaac Wasserman, Jeova Farias Sales Rocha Neto
Unsupervised image segmentation aims at grouping different semantic patterns in an image without the use of human annotation. Similarly, image clustering searches for groupings of…
Using Neural Networks for Fast SAR Roughness Estimation of High Resolution Images
Li Fan, Jeova Farias Sales Rocha Neto
The analysis of Synthetic Aperture Radar (SAR) imagery is an important step in remote sensing applications, and it is a challenging problem due to its inherent speckle noise. One t…
Improving Log-Cumulant Based Estimation of Roughness Information in SAR imagery
Jeova Farias Sales Rocha Neto, Francisco Alixandre Avila Rodrigues
Synthetic Aperture Radar (SAR) image understanding is crucial in remote sensing applications, but it is hindered by its intrinsic noise contamination, called speckle. Sophisticated…
A Sparse Graph Formulation for Efficient Spectral Image Segmentation
Rahul Palnitkar, Jeova Farias Sales Rocha Neto
Spectral Clustering is one of the most traditional methods to solve segmentation problems. Based on Normalized Cuts, it aims at partitioning an image using an objective function de…
Estimating Appearance Models for Image Segmentation via Tensor Factorization
Jeova Farias Sales Rocha Neto
Image Segmentation is one of the core tasks in Computer Vision and solving it often depends on modeling the image appearance data via the color distributions of each it its constit…
Spectral Image Segmentation with Global Appearance Modeling
Jeova F. S. Rocha Neto, Pedro F. Felzenszwalb
We introduce a new spectral method for image segmentation that incorporates long range relationships for global appearance modeling. The approach combines two different graphs, one…