742 citations · 811 across the 30 of their papers we have counts for
12 papers · 1 filter
Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics
Souradeep Chakraborty, Shujon Naha, Muhammet Bastan +2
In this paper, we address the detection of co-occurring salient objects (CoSOD) in an image group using frequency statistics in an unsupervised manner, which further enable us to d…
Zero-Shot Object Counting with Language-Vision Models
Jingyi Xu, Hieu Le, Dimitris Samaras
Class-agnostic object counting aims to count object instances of an arbitrary class at test time. It is challenging but also enables many potential applications. Current methods re…
Controllable Dynamic Appearance for Neural 3D Portraits
ShahRukh Athar, Zhixin Shu, Zexiang Xu +4
Recent advances in Neural Radiance Fields (NeRFs) have made it possible to reconstruct and reanimate dynamic portrait scenes with control over head-pose, facial expressions and vie…
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
Saarthak Kapse, Srijan Das, Jingwei Zhang +4
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning techniques, such as contrastive and non-contrastive app…
Learning from Pseudo-labeled Segmentation for Multi-Class Object Counting
Jingyi Xu, Hieu Le, Dimitris Samaras
Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing, based on only a few…
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology
Jingwei Zhang, Ke Ma, Saarthak Kapse +4
Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have…