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TREC Video Retrieval Evaluation, TRECVID 2006

作者： Aksoy, S. Duygulu, P. Akçay, G. Ataer, E. Baştan, M. Can, T. Cavuş, Ö. Doǧgrusöz, E. Gökalp, D. Akaydin, A. Akoǧlu, L. Angin, P. Cinbiş, G. Gür, T. Ünlü, M. RETINA Vision and Learning Group Department of Computer Engineering Bilkent University Bilkent 06800 Ankara Turkey

We describe our third participation, that includes one high-level feature extraction run, and two manual and one interactive search runs, to the TRECVID video retrieval evaluation. All of these runs have used a system trained on the common development collection. Only visual and textual information were used where visual information consisted of color, texture and edge-based low-level features and textual information consisted of the speech transcript provided in the collection.

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Augmented reality meets deep learning for car instance segmentation in urban scenes 28

Augmented reality meets deep learning for car instance segme...

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28th British Machine vision Conference, BMVC 2017

作者： Alhaija, Hassan Abu Mustikovela, Siva Karthik Mescheder, Lars Geiger, Andreas Rother, Carsten Computer Vision Lab. TU Dresden Germany Visual Learning Lab. Heidelberg University Germany Autonomous Vision Group MPI-IS Tübingen Germany Computer Vision and Geometry Group ETH Zürich Switzerland

ISBN: (纸本)190172560X

The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D shapes of the target object category. We demonstrate the utility of the proposed approach for training a state-of-the-art high-capacity deep model for semantic instance segmentation. In particular, we consider the task of segmenting car instances on the KITTI dataset which we have annotated with pixel-accurate ground truth. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amounts of annotated real data. © 2017. The copyright of this document resides with its authors.

关键词： Augmented reality

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Enhancing Material Features Using Dynamic Backward Attention on Cross-Resolution Patches 33

Enhancing Material Features Using Dynamic Backward Attention...

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33rd British Machine vision Conference Proceedings, BMVC 2022

作者： Heng, Yuwen Wu, Yihong Dasmahapatra, Srinandan Kim, Hansung Vision Learning and Control Research Group School of Electronics and Computer Science University of Southampton Southampton United Kingdom

Recent studies in material segmentation crop the image into patches to force the network to learn material features from local visual clues. This design is based on the expectation that the contextually invariant features can generalise the network to unseen images regardless of the object or scene in which the material appears. However, most approaches set a fixed patch resolution for all the images in a dataset, which does not consider the varying areas that materials cover within and across images due to the scene scale. As a consequence, the fixed patch resolution can limit the performance of networks. In consideration of this problem, this paper proposes a Dynamic Backward Attention Transformer (DBAT) to extract features from cross-resolution patches and dynamically aggregate these features based on per-pixel attention masks. Experiments show that DBAT achieves the best performance among state-of-the-art models (86.85% in average pixel accuracy, which is 2.15% higher than the second-best model) that can serve real-time inference. Moreover, we also illustrate the network behaviour through visualisation methods as well as descriptive statistics. The project code is available at https://***/heng-yuwen/Dynamic-Backward-Attention-Transformer. © 2022. The copyright of this document resides with its authors.

关键词： Pixels

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SliceFormer: Deep Dense Depth Estimation from a Single Indoor Omnidirectional Image Using a Slice-Based Transformer

SliceFormer: Deep Dense Depth Estimation from a Single Indoo...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Wu, Yihong Heng, Yuwen Niranjan, Mahesan Kim, Hansung School of Electronics and Computer Science University of Southampton Vision Learning and Control Research Group Southampton United Kingdom

ISBN: (纸本)9798350371888

In this research, we tackle the task of estimating depth from a single indoor omnidirectional image. Acknowledging gravity's critical influence in artificially constructed indoor environments, we process the input from equirectangular projection by dividing it into vertical slices. These slices are then utilized as patch embeddings for the transformer encoder, a strategy designed to recreate an equirectangular depth map. Our architecture is evaluated against leading models using real-world datasets, namely Stanford2D3D and Matterport3D, demonstrating its superior performance. These results underscore the significance of our gravity-aligned approach for depth estimation in omnidirectional images, especially in man-made settings. © 2024 IEEE.

关键词： Gravitation

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On the Implementation of Baselines and Lightweight Conditional Model Extrapolation (LIMES) Under Class-Prior Shift 4th

On the Implementation of Baselines and Lightweight Condit...

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Fourth International Workshop on Reproducible Research in Pattern Recognition, RRPR 2022

作者： Tomaszewska, Paulina Lampert, Christoph H. Warsaw University of Technology Faculty of Mathematics and Information Science Warsaw Poland Machine Learning and Computer Vision Group Klosterneuburg Austria

ISBN: (纸本)9783031407727

This paper focuses on the implementation details of the baseline methods and a recent lightweight conditional model extrapolation algorithm LIMES [5] for streaming data under class-prior shift. LIMES achieves superior performance over the baseline methods, especially concerning the minimum-across-day accuracy, which is important for the users of the system. In this work, the key measures to facilitate reproducibility and enhance the credibility of the results are described. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Lime

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On Higher-order Linear Port-Hamiltonian Systems and Their Duals

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IFAC-PapersOnLine 2017年第1期50卷 9236-9241页

作者： Rapisarda P. Maldonado J.C.M. Vision Learning and Control Group School of Electronics and Computer Science University of Southampton United Kingdom School of Engineering and Sciences Tecnologico de Monterrey Mexico

We formulate a behavioral approach to higher-order linear port-Hamiltonian systems. We formalize constitutive laws such as power conservation, storage and (anti-)dissipative relations, and we study several properties ... 详细信息

关键词： behavioral system theory Bilinear- duality port-Hamiltonian systems quadratic differential forms switched linear differential systems

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Sample distillation for object detection and image classification 6

Sample distillation for object detection and image classific...

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6th Asian Conference on Machine learning, ACML 2014

作者： Canévet, Olivier Lefakis, Leonidas Fleuret, François Computer Vision and Learning group Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne Lausanne Switzerland

We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have developed a "distillation" procedure to recursively reduce the size of an initial training set using a criterion that ensures the maximization of the information content of the selected sub-set. We demonstrate the performance of this procedure for two different computer vision problems. First, we show that distillation can be used to improve the traditional bootstrapping approach to object detection. Second, we apply distillation to a classification problem with artificial distortions. We show that in both cases, using the result of a distillation process instead of a random sub-set taken uniformly in the original sample set improves performance significantly. © 2014 O. Canévet, L. Lefakis & F. Fleuret.

关键词： Distillation

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Efficient sample mining for object detection 6

Efficient sample mining for object detection

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6th Asian Conference on Machine learning, ACML 2014

作者： Canévet, Olivier Fleuret, François Computer Vision and Learning group Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne Lausanne Switzerland

Object detectors based on the sliding window technique are usually trained in two successive steps: first, an initial classifier is trained on a population of positive samples (i.e. images of the object to detect) and negative samples randomly extracted from scenes which do not contain the object to detect. Then, the scenes are scanned with that initial classifier to enrich the initial set with negative samples incorrectly classified as positive. This bootstrapping process provides the learning algorithm with "hard" samples, which help to improve the decision boundary. Little work has been done on how to efficiently enrich the training set. While the standard bootstrapping approach densely visits the scenes, we propose to evaluate which regions of scenes can be discarded without any further computation to concentrate the search on promising areas. We apply our method to two standard object detection settings, pedestrian and face detection, and show that it provides a multi-fold speed up. © 2014 O. Canévet & F. Fleuret.

关键词： Object detection

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Data-Driven Dissipativity Analysis: Application of the Matrix S-Lemma

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IEEE Control Systems 2022年第3期42卷 140-149页

作者： Van Waarde, Henk J. Camlibel, M. Kanat Rapisarda, Paolo Trentelman, Harry L. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9747 AG Netherlands Vision Learning and Control Group of the University of Southampton SouthamptonSO17 1BJ United Kingdom

The concept of dissipativity, as introduced by Jan Willems, is one of the cornerstones of systems and control theory. Typically, dissipativity properties are verified by resorting to a mathematical model of the system under consideration. This article aims to assess dissipativity using computing storage functions for linear systems directly from measured data. As our main contributions, we provide conditions under which dissipativity can be ascertained from a finite collection of noisy data samples. Three different noise models are considered that can capture a variety of situations, including the cases where the data samples are noise-free, the energy of the noise is bounded, or the individual noise samples are bounded. All conditions are phrased in terms of data-based linear matrix inequalities, which can be readily solved using existing software packages. © 1991-2012 IEEE.

关键词： Linear matrix inequalities

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Modeling and analysis of energy distribution networks using switched differential systems 1

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Workshop on Mathematical Systems Theory: From Behaviors to Nonlinear Control, 2015

作者： Mayo-Maldonado, Jonathan C. Rapisarda, Paolo Vision Learning and Control Group School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

ISBN: (数字)9783319209883

ISBN: (纸本)9783319209876

It is a pleasure to dedicate this contribution to Prof. Arjan van der Schaft on the occasion of his 60th birthday. We study the dynamics of energy distribution networks consisting of switching power converters and multiple (dis-)connectable modules. We use parsimonious models that deal effectively with the variant complexity of the network and the inherent switching phenomena induced by power converters. We also present the solution to instability problems caused by devices with negative impedance characteristics such as constant power loads. Elements of the behavioral system theory such as linear differential behaviors and quadratic differential forms are crucial in our analysis. © Springer International Publishing Switzerland 2015.

关键词： Power converters

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