Search Results for author: Victor A. Prisacariu

Found 9 papers, 2 papers with code

Semi-weakly-supervised neural network training for medical image registration

no code implementations16 Feb 2024 Yiwen Li, Yunguan Fu, Iani J. M. B. Gayo, Qianye Yang, Zhe Min, Shaheer U. Saeed, Wen Yan, Yipei Wang, J. Alison Noble, Mark Emberton, Matthew J. Clarkson, Dean C. Barratt, Victor A. Prisacariu, Yipeng Hu

For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupervised losses are unavailable or ineffective.

Image Registration Medical Image Registration

ABC Easy as 123: A Blind Counter for Exemplar-Free Multi-Class Class-agnostic Counting

no code implementations9 Sep 2023 Michael A. Hobley, Victor A. Prisacariu

Class-agnostic counting methods enumerate objects of an arbitrary class, providing tremendous utility in many fields.

DMS: Differentiable Mean Shift for Dataset Agnostic Task Specific Clustering Using Side Information

no code implementations29 May 2023 Michael A. Hobley, Victor A. Prisacariu

We present a novel approach, in which we learn to cluster data directly from side information, in the form of a small set of pairwise examples.


Contextualising Implicit Representations for Semantic Tasks

no code implementations22 May 2023 Theo W. Costain, Kejie Li, Victor A. Prisacariu

Prior works have demonstrated that implicit representations trained only for reconstruction tasks typically generate encodings that are not useful for semantic tasks.

Segmentation Semantic Segmentation

GroSS: Group-Size Series Decomposition for Grouped Architecture Search

1 code implementation ECCV 2020 Henry Howard-Jenkins, Yiwen Li, Victor A. Prisacariu

We present a novel approach which is able to explore the configuration of grouped convolutions within neural networks.

Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade

1 code implementation29 Oct 2018 Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Victor A. Prisacariu, Luigi Di Stefano, Philip H. S. Torr

The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time.

Pose Estimation

Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation

no code implementations25 Jan 2018 Stuart Golodetz, Tommaso Cavallari, Nicholas A. Lord, Victor A. Prisacariu, David W. Murray, Philip H. S. Torr

Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases.

3D Reconstruction

Dense Reconstruction Using 3D Object Shape Priors

no code implementations CVPR 2013 Amaury Dame, Victor A. Prisacariu, Carl Y. Ren, Ian Reid

More specifically, we automatically augment our SLAM system with object specific identity, together with 6D pose and additional shape degrees of freedom for the object(s) of known class in the scene, combining image data and depth information for the pose and shape recovery.

3D Reconstruction Object

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