Selected Videos

Semantic Localisation via Globally Unique Instance Segmentation - 360 Video

A supplementary video demonstrating four key stages: video collection, hand labeling and label propagation and localisation of our semantic localisation approach described in Semantic Localisation via Globally Unique Instance Segmentation paper at BMVC 2018 (Newcastle). See [15] for more detail.

Semantic Localisation via Globally Unique Instance Segmentation - Detailed Analysis

This video illustrates simultaneous localisation and surrounding environment recognition results on CamVid-360 dataset. See more detail in Semantic Localisation via Globally Unique Instance Segmentation paper [15] at BMVC 2018 (Newcastle).

Semantic Localisation in an Artificial City under Changes of Environment - Missing Buildings

This video illustrates simultaneous localisation and surrounding environment recognition results on SceneCity (artificial city) dataset. See details in Semantic Localisation via Globally Unique Instance Segmentation paper [15] at BMVC 2018 (Newcastle).

Semantic Localisation in a Highly Repetitive Large Artificial City - more than 800 Buildings

This video illustrates simultaneous localisation and surrounding environment recognition results on SceneCity (artificial city) dataset. See details in Semantic Localisation via Globally Unique Instance Segmentation paper [15] at BMVC 2018 (Newcastle).

Indirect Deep Structured Learning

A supplementary video demonstrating results of Indirect Deep Structured Learning for 3D Human Body Shape and Pose Prediction paper at BMVC 2017. See [13].

Large Scale Augmentation of Semantic Labels in Videos - CamVid

Example video of semantic label propagation from Large Scale Labelled Video Data Augmentation for Semantic Segmentation in Driving Scenarios at the 5th Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving, ICCV 2017. See [14].

Large Scale Augmentation of Semantic Labels in Videos - CityScapes

Example video of instance label propagation from Large Scale Labelled Video Data Augmentation for Semantic Segmentation in Driving Scenarios at the 5th Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving, ICCV 2017. See [14].

Large Scale Augmentation of Instance Labels in Videos - CamVid-Instance

Example video of instance label propagation from Large Scale Labelled Video Data Augmentation for Semantic Segmentation in Driving Scenarios at the 5th Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving, ICCV 2017. See [14].

3D Human Body Shape and Pose Prediction

An early attempt to predict 3D shape and pose in videos using indirect deep structured learning. See [13].

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All publications



[15] Budvytis, I., Sauer, P., Cipolla, R.,
Semantic localisation via globally unique instance segmentation. [pdf][dataset-available-via-email]
In Proc. British Machine Vision Conference (BMVC), Newcastle, September 2018

[14] Budvytis, I., Sauer, P., Roddick, T., Breen, K., Cipolla, R.,
Large scale labelled video data augmentation for semantic segmentation in driving scenarios. [pdf][dataset-available-via-email]
In 5th Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving in IEEE International Conference on Computer Vision (ICCV), Venice, October 2017

[13] Tan, J., Budvytis, I., Cipolla, R.,
Indirect deep structured learning for 3D human body shape and pose prediction. [pdf] [oral presentation] [slides-pdf]
In Proc. British Machine Vision Conference (BMVC), London, September 2017

[12] Charles, J., Budvytis, I., Cipolla, R.,
Real-time Factored ConvNets: Extracting the X Factor in Human Parsing. [pdf]
In Proc. British Machine Vision Conference (BMVC), London, September 2017

[11] Badrinarayanan, V., Budvytis, I., Cipolla,R.,
Mixture of Trees Probabilistic Graphical Model for Video Segmentation. [pdf]
International Journal of Computer Vision (IJCV), BMVC12 special issue, December 2013

[10] Budvytis, I., advised by Badrinarayanan V., supervised by Cipolla, R.,
Novel Probabilistic Graphical Models for Semi-Supervised Video Segmentation. [pdf]
PhD Thesis, 2013.

[9] Badrinarayanan V., Budvytis, I., Cipolla, R.,
Semi-Supervised Video Segmentation using Tree Structured Graphical Models. [pdf]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013.

[8] Badrinarayanan V., Budvytis I., Cipolla, R.,
Semi-Supervised Video Segmentation using Random Forests. [to buy]
Book Chapter in Decision Forests: for Computer Vision and Medical Image Analysis, Edited by Criminisi A., Shotton J., Springer 2013.

[7] Budvytis, I., Badrinarayanan, V., Cipolla, R.,
MoT - Mixture of Trees Probabilistic Graphical Model for Video Segmentation. [pdf] [oral presentation]
In Proc. British Machine Vision Conference, Surrey, September 2012

[6] Kim, T-K., Budvytis, I., Cipolla, R.,
Making a Shallow Network Deep: Conversion of a Boosting Classifier into a Decision Tree by Boolean Optimisation. [pdf]
International Journal of Computer Vision (IJCV), BMVC10 special issue, June 2011.

[5] Budvytis, I., Badrinarayanan, V., Cipolla, R.,
Semi-Supervised Video Segmentation using Tree Structured Graphical Models. [pdf]
In Proc. IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, June 2011.

[4] Budvytis I., Badrinarayanan V., Cipolla R.,
Label Propagation in Complex Video Sequences using Semi-Supervised Learning. [pdf]
In Proc. British Machine Vision Conference, Aberystwyth, September 2010.

[3] Budvytis* I., Kim*, T-K., Cipolla R.,
Making a Shallow Network Deep: Growing a Tree from Decision Regions of a Boosting Classifier. [pdf] [oral presentation]
In Proc. British Machine Vision Conference, Aberystwyth, September 2010.
* indicates equal contribution

[2] Budvytis, I., Scott, J., Butler, A.,
Compass-Based Automatic Picture Taking using SenseCam. [pdf]
In Adj. Proc. of Pervasive 2008, Sydney, May 2008.

[1] Blackwell, A.F., Bailey, G., Budvytis, I., Chen, V., Church, L., Dubuc, L., Edge, D., Linnap, M., Naudziunas, V. and Warrington, H.
Tangible interaction in a mobile context. [pdf]
Workshop on Tangible user interfaces in context and theory in CHI 2007, San Jose, California, May 2007.

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