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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Xiangping Zhu Amran Bhuiyan Mohamed Lamine Mekhalfi Vittorio Murino Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Istituto Italiano di Tecnologia Genova Liguria IT

Person re-identification (ReID) stands for the task of determining the co-occurrence of individuals across a network of cameras with disjoint viewfields. The relevant literature documents a plausible number of contributions so far. KISS metric learning is an effective ReID method. However, as reported in the existing works, KISS metric learning is sensitive to the feature dimensionality and can not capture the multi modes in the dataset. To this end, we propose in this paper a Gaussian Mixture Importance Estimation (GMIE) approach for ReID, which exploits the Gaussian Mixture Models (GMMs) to estimate the observed commonalities of similar and dissimilar person pairs in the feature space. Experiments on three benchmark datasets reveal that our method offers the property of maintaining its efficiency on high-dimensional features. Moreover, the proposed GMIE scores plausible ReID rates as compared to other works.

关键词： Measurement Cameras Estimation Robustness Feature extraction Covariance matrices

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Multi-Task learning of social psychology assessments and nonverbal features for automatic leadership identification 17

Multi-Task learning of social psychology assessments and non...

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19th ACM International Conference on Multimodal Interaction, ICMI 2017

作者： Beyan, Cigdem Capozzi, Francesca Becchio, Cristina Murino, Vittorio Istituto Italiano di Tecnologia Pattern Analysis and Computer Vision Genoa Italy Department of Psychology McGill University Montreal Canada Motion and Neuroscience Unit Genoa Istituto Italiano di Tecnologia Cognition Italy Department of Psychology University of Turin Torino Italy Department of Computer Science University of Verona Verona Italy

ISBN: (纸本)9781450355438

In social psychology, the leadership investigation is performed using questionnaires which are either i) self-Administered or ii) applied to group participants to evaluate other members or iii) filled by external observers. While each of these sources is informative, using them individually might not be as effective as using them jointly. This paper is the first attempt which addresses the automatic identification of leaders in small-group meetings, by learning effective models using nonverbal audio-visual features and the results of social psychology questionnaires that reflect assessments regarding leadership. Learning is based on Multi-Task Learning which is performed without using ground-Truth data (GT), but using the results of questionnaires (having substantial agreement with GT), administered to external observers and the participants of the meetings, as tasks. The results show that joint learning results in better performance as compared to single task learning and other baselines. © 2017 Association for Computing Machinery.

关键词： Surveys

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Continual Source-Free Unsupervised Domain Adaptation

arXiv

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arXiv 2023年

作者： Ahmed, Waqar Morerio, Pietro Murino, Vittorio Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Genova Italy Dipartimento di Informatica University of Verona Italy

Existing Source-free Unsupervised Domain Adaptation (SUDA) approaches inherently exhibit catastrophic forgetting. Typically, models trained on a labeled source domain and adapted to unlabeled target data improve performance on the target while dropping performance on the source, which is not available during adaptation. In this study, our goal is to cope with the challenging problem of SUDA in a continual learning setting, i.e., adapting to the target(s) with varying distributional shifts while maintaining performance on the source. The proposed framework consists of two main stages: i) a SUDA model yielding cleaner target labels - favoring good performance on target, and ii) a novel method for synthesizing class-conditioned source-style images by leveraging only the source model and pseudo-labeled target data as a prior. An extensive pool of experiments on major benchmarks, e.g., PACS, Visda-C, and DomainNet demonstrates that the proposed Continual SUDA (C-SUDA) framework enables preserving satisfactory performance on the source domain without exploiting the source data at all. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Identification of emergent leaders in a meeting scenario using multiple kernel learning 2

Identification of emergent leaders in a meeting scenario usi...

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2nd Workshop on Advancements in Social Signal Processing for Multimodal Interaction, ASSP4MI 2016

作者： Beyan, Cigdem Capozzi, Francesca Becchio, Cristina Murino, Vittorio Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Department of Psychology McGill University MontrealQC Canada Department of Psychology University of Turin Italy Robotics Brain and Cognitive Sciences Istituto Italiano di Tecnologia Genova Italy Department of Computer Science University of Verona Verona Italy

ISBN: (纸本)9781450345576

In this paper, an effective framework for detection of emergent leaders in small group is presented. In this scope, the combination of different types of nonverbal visual features;the visual focus of attention, head activity and body activity based features are utilized. Using them together ensued significant results. For the first time, multiple kernel learning (MKL) was applied for the identification of the most and the least emergent leaders. Taking the advantage of MKL's capability to use different kernels which corresponds to different feature subsets having different notions of similarity, significantly improved results compared to the state of the art methods were obtained. Additionally, high correlations between the majority of the features and the social psychology questionnaires which are designed to estimate the leadership or dominance were demonstrated. © 2016 ACM.

关键词： Surveys

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One-Shot Learning-Based Handwritten Word Recognition 5th

One-Shot Learning-Based Handwritten Word Recognition

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5th Asian Conference on pattern Recognition, ACPR 2019

作者： Chakrapani Gv, Asish Chanda, Sukalpa Pal, Umapada Doermann, David Electronics and Communication Department Manipal University Jaipur Jaipur India Centre for Image Analysis Department of Information Technology Uppsala University Uppsala Sweden Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India University at Buffalo SUNY Buffalo United States

ISBN: (纸本)9783030412982

One-Shot and Few-shot Learning algorithms have emerged as techniques that can imitate a humans ability to learn from very few examples. This is an advantage over traditional deep networks which require a lot of training samples and lack of robustness due to their excessive domain specific discriminators. In this paper, we explore a one-shot learning approach to recognizing handwritten words using Siamese networks to classify the handwritten images at the word level. The Siamese network’s ability to compute similarities between two images is learned using a supervised metric but the fully trained Siamese network can be used to classify new data that has previously not been used to train the network. The model learns to discriminate inputs from a small labelled support set. By using a convolutional architecture we were able to achieve robust results. We also expect that training the system over a larger distributions of data will result in improved general handwritten word classification. Accuracy as high as 92.4% was obtained while performing 5-way one-shot word recognition on a publicly available dataset which is quite high in comparison to the state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Image classification

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Visual coverage using autonomous mobile robots for search and rescue applications

Visual coverage using autonomous mobile robots for search an...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： A. Del Bue M. Tamassia F. Signorini V. Murino A. Farinelli Pattern Analysis and Computer Vision Department (PAVIS) Istituto Italiano di Tecnologia (IIT) Genova Italy Department of Computer Science University of Verona Italy

This paper focuses on visual sensing of 3D large-scale environments. Specifically, we consider a setting where a group of robots equipped with a camera must fully cover a surrounding area. To address this problem we propose a novel descriptor for visual coverage that aims at measuring visual information of an area based on a regular discretization of the environment in voxels. Moreover, we propose an autonomous cooperative exploration approach which controls the robot movements so to maximize information accuracy (defined based on our visual coverage descriptor) and minimizing movement costs. Finally, we define a simulation scenario based on real visual data and on widely used robotic tools (such as ROS and Stage) to empirically evaluate our approach. Experimental results show that the proposed method outperforms a baseline random approach and an uncoordinated one, thus being a valid solution for visual coverage in large scale outdoor scenarios.

关键词： Three-dimensional displays Cameras Visualization Robot vision systems

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Asymmetric distances for binary embeddings

Asymmetric distances for binary embeddings

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Conference on computer vision and pattern Recognition (CVPR)

作者： Albert Gordo Florent Perronnin Computer Vision Center Universitat Autònoma Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Centre Europe France

In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique - PCA Embedding (PCAE) - which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH.

关键词： Databases Kernel Euclidean distance Principal component analysis Approximation methods Table lookup Random access memory

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Fast 6D pose from a single RGB image using Cascaded Forests Templates

Fast 6D pose from a single RGB image using Cascaded Forests ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： E. Munoz Y. Konishi C. Beltran V. Murino A. Del Bue Pattern Analysis and Computer Vision (PAVIS) Department Istituto Italiano di Tecnologia (IIT) Genova Italy OMRON corporation Kyoto Japan

ISBN: (纸本)9781509037636

This paper presents a method for 6D pose estimation from a single RGB image for complex texture-less objects. This class of objects are common in any environment but still challenging to deal with. This is due to the fact that the distribution of surface brightness makes difficult to compute interest points or appearance-based descriptors. Here we propose a novel part-based method using an efficient template matching approach where each template independently encodes the similarity function using a Forest trained over the templates. Moreover, accuracy is even more incremented by using a cascade of the learned forest. These templates forests together with the simplicity of the computed image features allow a quick estimate of the pose achieving real-time performance. Performance are demonstrated both on synthetic and real images with known ground truth.

关键词： jigs(templates) cascades ground truth Forest red-green-blue colour model surface brightness image feature images Imagery (Psychotherapy) Image SYNTHETICS

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Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices

Kernel-based classification for brain connectivity graphs on...

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IEEE International Symposium on Biomedical Imaging

作者： Luca Dodero Hà Quang Minh Marco San Biagio Vittorio Murino Diego Sona Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy NeuroInformatics Laboratory Fondazione Bruno Kessler Trento Italy

An important task in connectomics studies is the classification of connectivity graphs coming from healthy and pathological subjects. In this paper, we propose a mathematical framework based on Riemannian geometry and kernel methods that can be applied to connectivity matrices for the classification task. We tested our approach using different real datasets of functional and structural connectivity, evaluating different metrics to describe the similarity between graphs. The empirical results obtained clearly show the superior performance of our approach compared with baseline methods, demonstrating the advantages of our manifold framework and its potential for other applications.

关键词： Manifolds Kernel Measurement Support vector machines Autism Symmetric matrices Laplace equations

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Fast 6D pose estimation for texture-less objects from a single RGB image

Fast 6D pose estimation for texture-less objects from a sing...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： E. Muñoz Y. Konishi V. Murino A. Del Bue Pattern Analysis and Computer Vision (PAVIS) Department Istituto Italiano di Tecnologia (IIT) Genova Italy OMRON corporation Kyoto Japan

A fundamental step to solve bin-picking and grasping problems is the accurate estimation of an object 3D pose. Such visual task usually rely on profusely textured objects: standard procedures such as detection of interest points or computation of appearance-based descriptors are favoured by using a highly informative surface. However, texture-less objects or their parts (i.e., those whose surface texture is poorly conditioned) are common in any environment but still challenging to deal with. This is due the fact that the distribution of surface brightness makes difficult to compute interest points or appearance-based descriptors. In this paper, we propose a method to estimate the 3D pose for texture-less objects given a coarse initialization: the pose is estimated using using edge correspondences, where the similarity measure is encoded using a pre-computed linear regression matrix. Furthermore, we also propose a method to increase the robustness of the estimated pose against background and object clutter. We validate both methods by using synthetic and real image sequences with objects with known ground truth.

关键词： Image edge detection Solid modeling Three-dimensional displays Computational modeling Robustness Training

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