What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics

This paper is about enabling robots to improve their perceptual performance through repeated use in their operating environment, creating local expert detectors fitted to the places through which a robot moves. We leverage the concept of 'experiences' in visual perception for robotics, accounting for bias in the data a robot sees by fitting object detector models to a particular place. The key question we seek to answer in this paper is simply: how do we define a place? We build bespoke pedestrian detector models for autonomous driving, highlighting the necessary trade off between generalisation and model capacity as we vary the extent of the place we fit to. We demonstrate a sizeable performance gain over a current state-of-the-art detector when using computationally lightweight bespoke place-fitted detector models.Comment: IROS 201

No Spare Parts: Sharing Part Detectors for Image Categorization

This work aims for image categorization using a representation of distinctive parts. Different from existing part-based work, we argue that parts are naturally shared between image categories and should be modeled as such. We motivate our approach with a quantitative and qualitative analysis by backtracking where selected parts come from. Our analysis shows that in addition to the category parts defining the class, the parts coming from the background context and parts from other image categories improve categorization performance. Part selection should not be done separately for each category, but instead be shared and optimized over all categories. To incorporate part sharing between categories, we present an algorithm based on AdaBoost to jointly optimize part sharing and selection, as well as fusion with the global image representation. We achieve results competitive to the state-of-the-art on object, scene, and action categories, further improving over deep convolutional neural networks

S3^3FD: Single Shot Scale-invariant Face Detector

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchor-based detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects: 1) proposing a scale-equitable face detection framework to handle different scales of faces well. We tile anchors on a wide range of layers to ensure that all scales of faces have enough features for detection. Besides, we design anchor scales based on the effective receptive field and a proposed equal proportion interval principle; 2) improving the recall rate of small faces by a scale compensation anchor matching strategy; 3) reducing the false positive rate of small faces via a max-out background label. As a consequence, our method achieves state-of-the-art detection performance on all the common face detection benchmarks, including the AFW, PASCAL face, FDDB and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images.Comment: Accepted by ICCV 2017 + its supplementary materials; Updated the latest results on WIDER FAC

Search for the Higgs Boson decaying to tau leptons at ATLAS using multi-variate analysis techniques

This thesis presents three differing approaches to the search for the Standard Model Higgs boson decaying to tau leptons using ps = 7 TeV protonproton collision data from the ATLAS experiment at the LHC. Multi-variate analysis techniques involving boosted decision trees are used to extend an existing cut-based analysis procedure. The expected 95% confidence level upper limit on the observed cross-section is compared between the analyses. The upper limit at a Higgs mass of mH = 125 GeV is improved from 2:9+4:3 2:1 to 2:3+3:3 1:7 times the Standard Model prediction, after implementing multivariate techniques. No significant excess is seen in data for any analysis strategy. The most sensitive measurement of the signal strength normalised to the Standard Model prediction was observed to be ˆ m = 1:6 1:1, corresponding to 1:4s upward fluctuation of the background-only model to match the data

Search for the Higgs Boson decaying to tau leptons at ATLAS using multi-variate analysis techniques

This thesis presents three differing approaches to the search for the Standard Model Higgs boson decaying to tau leptons using ps = 7 TeV protonproton collision data from the ATLAS experiment at the LHC. Multi-variate analysis techniques involving boosted decision trees are used to extend an existing cut-based analysis procedure. The expected 95% confidence level upper limit on the observed cross-section is compared between the analyses. The upper limit at a Higgs mass of mH = 125 GeV is improved from 2:9+4:3 2:1 to 2:3+3:3 1:7 times the Standard Model prediction, after implementing multivariate techniques. No significant excess is seen in data for any analysis strategy. The most sensitive measurement of the signal strength normalised to the Standard Model prediction was observed to be ˆ m = 1:6 1:1, corresponding to 1:4s upward fluctuation of the background-only model to match the data

Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012

This report of the BOOST2012 workshop presents the results of four working groups that studied key aspects of jet substructure. We discuss the potential of first-principle QCD calculations to yield a precise description of the substructure of jets and study the accuracy of state-of-the-art Monte Carlo tools. Limitations of the experiments’ ability to resolve substructure are evaluated, with a focus on the impact of additional (pile-up) proton proton collisions on jet substructure performance in future LHC operating scenarios. A final section summarizes the lessons learnt from jet substructure analyses in searches for new physics in the production of boosted top quarks