Search Results for author: Fabio Cuzzolin

Found 29 papers, 7 papers with code

Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder

no code implementations22 Apr 2021 Dinesh Jackson Samuel, Fabio Cuzzolin

Anomaly detection in Minimally-Invasive Surgery (MIS) traditionally requires a human expert monitoring the procedure from a console.

Unsupervised Anomaly Detection

A geometric approach to conditioning belief functions

no code implementations21 Apr 2021 Fabio Cuzzolin

Conditioning is crucial in applied science when inference involving time series is involved.

Time Series

Spatiotemporal Deformable Models for Long-Term Complex Activity Detection

no code implementations16 Apr 2021 Salman Khan, Fabio Cuzzolin

Long-term complex activity recognition and localisation can be crucial for the decision-making process of several autonomous systems, such as smart cars and surgical robots.

Action Detection Activity Detection +3

Uncertainty measures: The big picture

no code implementations14 Apr 2021 Fabio Cuzzolin

Probability theory is far from being the most general mathematical theory of uncertainty.

Articulated Shape Matching Using Laplacian Eigenfunctions and Unsupervised Point Registration

no code implementations14 Dec 2020 Diana Mateus, Radu Horaud, David Knossow, Fabio Cuzzolin, Edmond Boyer

Matching articulated shapes represented by voxel-sets reduces to maximal sub-graph isomorphism when each set is described by a weighted graph.

Graph Matching

Challenges and Opportunities for Computer Vision in Real-life Soccer Analytics

no code implementations13 Apr 2020 Neha Bhargava, Fabio Cuzzolin

In this paper, we explore some of the applications of computer vision to sports analytics.

Sports Analytics

Two-Stream AMTnet for Action Detection

1 code implementation3 Apr 2020 Suman Saha, Gurkirt Singh, Fabio Cuzzolin

This is achieved by augmenting the previous Action Micro-Tube (AMTnet) action detection framework in three distinct ways: by adding a parallel motion stIn this paper, we propose a new deep neural network architecture for online action detection, termed ream to the original appearance one in AMTnet; (2) in opposition to state-of-the-art action detectors which train appearance and motion streams separately, and use a test time late fusion scheme to fuse RGB and flow cues, by jointly training both streams in an end-to-end fashion and merging RGB and optical flow features at training time; (3) by introducing an online action tube generation algorithm which works at video-level, and in real-time (when exploiting only appearance features).

Action Detection Autonomous Driving +2

Datamorphic Testing: A Methodology for Testing AI Applications

no code implementations10 Dec 2019 Hong Zhu, Dongmei Liu, Ian Bayley, Rachel Harrison, Fabio Cuzzolin

With the rapid growth of the applications of machine learning (ML) and other artificial intelligence (AI) techniques, adequate testing has become a necessity to ensure their quality.

Face Recognition

Spatio-Temporal Action Localization in a Weakly Supervised Setting

no code implementations6 May 2019 Kurt Degiorgio, Fabio Cuzzolin

Enabling computational systems with the ability to localize actions in video-based content has manifold applications.

Action Detection Multiple Instance Learning +2

End-to-End Video Captioning

no code implementations4 Apr 2019 Silvio Olivastri, Gurkirt Singh, Fabio Cuzzolin

The decoder is then optimised on such static features to generate the video's description.

Action Recognition Machine Translation +3

Recurrent Convolutions for Causal 3D CNNs

no code implementations17 Nov 2018 Gurkirt Singh, Fabio Cuzzolin

Recently, three dimensional (3D) convolutional neural networks (CNNs) have emerged as dominant methods to capture spatiotemporal representations in videos, by adding to pre-existing 2D CNNs a third, temporal dimension.

Action Detection

Predicting Action Tubes

no code implementations23 Aug 2018 Gurkirt Singh, Suman Saha, Fabio Cuzzolin

In this work, we present a method to predict an entire `action tube' (a set of temporally linked bounding boxes) in a trimmed video just by observing a smaller subset of it.

Action Classification Action Detection +1

Belief likelihood function for generalised logistic regression

no code implementations7 Aug 2018 Fabio Cuzzolin

The notion of belief likelihood function of repeated trials is introduced, whenever the uncertainty for individual trials is encoded by a belief measure (a finite random set).

TraMNet - Transition Matrix Network for Efficient Action Tube Proposals

1 code implementation1 Aug 2018 Gurkirt Singh, Suman Saha, Fabio Cuzzolin

At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network.

Action Detection from a Robot-Car Perspective

no code implementations30 Jul 2018 Valentina Fontana, Gurkirt Singh, Stephen Akrigg, Manuele Di Maio, Suman Saha, Fabio Cuzzolin

We present the new Road Event and Activity Detection (READ) dataset, designed and created from an autonomous vehicle perspective to take action detection challenges to autonomous driving.

Action Detection Activity Detection +2

Spatio-temporal Human Action Localisation and Instance Segmentation in Temporally Untrimmed Videos

no code implementations22 Jul 2017 Suman Saha, Gurkirt Singh, Michael Sapienza, Philip H. S. Torr, Fabio Cuzzolin

Current state-of-the-art human action recognition is focused on the classification of temporally trimmed videos in which only one action occurs per frame.

Action Recognition Instance Segmentation +1

AMTnet: Action-Micro-Tube Regression by End-to-end Trainable Deep Architecture

no code implementations ICCV 2017 Suman Saha, Gurkirt Singh, Fabio Cuzzolin

As such, our 3D-RPN net is able to effectively encode the temporal aspect of actions by purely exploiting appearance, as opposed to methods which heavily rely on expensive flow maps.

Action Detection Region Proposal

Incremental Tube Construction for Human Action Detection

1 code implementation5 Apr 2017 Harkirat Singh Behl, Michael Sapienza, Gurkirt Singh, Suman Saha, Fabio Cuzzolin, Philip H. S. Torr

In this work, we introduce a real-time and online joint-labelling and association algorithm for action detection that can incrementally construct space-time action tubes on the most challenging action videos in which different action categories occur concurrently.

Action Detection Human robot interaction

Online Real-time Multiple Spatiotemporal Action Localisation and Prediction

4 code implementations ICCV 2017 Gurkirt Singh, Suman Saha, Michael Sapienza, Philip Torr, Fabio Cuzzolin

To the best of our knowledge, ours is the first real-time (up to 40fps) system able to perform online S/T action localisation and early action prediction on the untrimmed videos of UCF101-24.

Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos

no code implementations4 Aug 2016 Suman Saha, Gurkirt Singh, Michael Sapienza, Philip H. S. Torr, Fabio Cuzzolin

In stage 2, the appearance network detections are boosted by combining them with the motion detection scores, in proportion to their respective spatial overlap.

Action Detection Motion Detection +1

Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge

1 code implementation7 Jul 2016 Gurkirt Singh, Fabio Cuzzolin

We propose a simple, yet effective, method for the temporal detection of activities in temporally untrimmed videos with the help of untrimmed classification.

Action Detection Activity Detection +4

Active Learning for Online Recognition of Human Activities from Streaming Videos

no code implementations11 Apr 2016 Rocco De Rosa, Ilaria Gori, Fabio Cuzzolin, Barbara Caputo, Nicolò Cesa-Bianchi

Recognising human activities from streaming videos poses unique challenges to learning algorithms: predictive models need to be scalable, incrementally trainable, and must remain bounded in size even when the data stream is arbitrarily long.

Active Learning

Consistent transformations of belief functions

no code implementations30 Jul 2014 Fabio Cuzzolin

Consistent belief functions represent collections of coherent or non-contradictory pieces of evidence, but most of all they are the counterparts of consistent knowledge bases in belief calculus.

Feature sampling and partitioning for visual vocabulary generation on large action classification datasets

no code implementations29 May 2014 Michael Sapienza, Fabio Cuzzolin, Philip H. S. Torr

The recent trend in action recognition is towards larger datasets, an increasing number of action classes and larger visual vocabularies.

Action Classification Action Recognition +1

Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies

no code implementations26 May 2014 Fabio Cuzzolin, Diana Mateus, Radu Horaud

In an unsupervised context, i. e., no prior model of the moving object(s) is available, such a structure has to be learned from the data in a bottom-up fashion.

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