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IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation 31

IR2VI: Enhanced Night Environmental Perception by Unsupervis...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Shuo John, Vijay Blasch, Erik Liu, Zheng Huang, Ying Univ British Columbia Vancouver BC Canada Toyota Inst Technol Nagoya Aichi Japan Air Force Res Lab Wright Patterson AFB OH USA Chongqing Univ Posts & Telecommun Chongqing Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights. In this paper, we present the infrared-to-visual (IR2VI) algorithm, a novel unsupervised thermal-to-visible image translation framework based on generative adversarial networks (GANs). IR2VI is able to learn the intrinsic characteristics from VI images and integrate them into IR images. Since the existing unsupervised GAN-based image translation approaches face several challenges, such as incorrect mapping and lack of fine details, we propose a structure connection module and a region-of-interest (ROI) focal loss method to address the current limitations. Experimental results show the superiority of the IR2VI algorithm over baseline methods.

关键词： Training Gallium nitride Image fusion Generators Night vision Task analysis Semantics

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Internet Video Category recognition

Internet Video Category Recognition

引用

IEEE conference on computer vision and pattern recognition

作者： Schindler, Grant Zitnick, Larry Brown, Matthew Georgia Inst Technol Atlanta GA 30332 USA Microsoft Res Atlanta GA USA Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9781424423392

In this paper, we examine the problem of internet video categorization. Specifically, we explore the representation of a video as a "bag of words" using various combinations of spatial and temporal descriptors. The descriptors incorporate both spatial and temporal gradients as well as optical flow information. We achieve state-of-the-art results on a standard human activity recognition database and demonstrate promising category recognition performance on two new databases of approximately 1000 and 1500 online user-submitted videos, which we will be making available to the community.

关键词： pattern recognition

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From Evaluation to Verification: Towards Task-oriented Relevance Metrics for Pedestrian Detection in Safety-critical Domains

From Evaluation to Verification: Towards Task-oriented Relev...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lyssenko, Maria Gladisch, Christoph Heinzemann, Christian Woehrle, Matthias Triebel, Rudolph Robert Bosch GmbH Corp Res Robert Bosch Campus 1 D-71272 Renningen Germany Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany German Aerosp Ctr DLR Munchener Str 20 D-82234 Wessling Germany

ISBN: (纸本)9781665448994

Whenever a visual perception system is employed in safety-critical applications such as automated driving, a thorough, task-oriented experimental evaluation is necessary to guarantee safe system behavior. While most standard evaluation methods in computer vision provide a good comparability on benchmarks, they tend to fall short on assessing the system performance that is actually relevant for the given task. In our work, we consider pedestrian detection as a highly relevant perception task, and we argue that standard measures such as Intersection over Union (IoU) give insufficient results, mainly because they are insensitive to important physical cues including distance, speed, and direction of motion. Therefore, we investigate so-called relevance metrics, where specific domain knowledge is exploited to obtain a task-oriented performance measure focusing on distance in this initial work. Our experimental setup is based on the CARLA simulator and allows a controlled evaluation of the impact of that domain knowledge. Our first results indicate a linear decrease of the IoU related to the pedestrians' distance, leading to the proposal of a first relevance metric that is also conditioned on the distance.

关键词： Measurement Meters computer vision System performance pattern recognition Proposals Motion measurement

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Is synthetic voice detection research going into the right direction?

Is synthetic voice detection research going into the right d...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Borzi, Stefano Giudice, Oliver Stanco, Filippo Allegra, Dario Univ Catania Dept Math & Comp Sci Catania Italy Banca Italia IT Dept Appl Res Team Rome Italy

ISBN: (纸本)9781665487399

Machine Learning, and in general Artificial Intelligence approaches, brought a great advance in each and every field of computer Science increasing accuracy levels of predictors in any known problem. Indeed, this evolution enabled the construction of effective frameworks and solutions able to be used in investigative and forensics scenarios for detection of fakes and, in general, manipulations in multimedia contents. On the other hand, can we trust these systems? Is research activity going in the right direction? Are we just taking the low-hanging fruit without taking into account many real-case-in-the-wild situations? The purpose of this paper is to raise an alert to the research community in the specific context of synthetic voice detection, where data available for training is not big enough to give sufficient trust in the techniques available in the literature. To this aim, an exploratory investigation of the most common voice spoofing dataset was carried out and it was surprisingly easy to build simple classifiers without any Deep Learning techniques. Simple considerations on bitrate were sufficient to achieve an effective detection performance.

关键词： Deep learning Training computer vision Image color analysis Forensics conferences Bit rate

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Group Leakage Overestimates Performance: A Case Study in Keystroke Dynamics

Group Leakage Overestimates Performance: A Case Study in Key...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ayotte, Blaine Banavar, Mahesh K. Hou, Daqing Schuckers, Stephanie Clarkson Univ Dept Elect & Comp Engn 8 Clarkson Ave Potsdam NY 13699 USA

ISBN: (纸本)9781665448994

Keystroke dynamics is a powerful behavioral biometric capable of user authentication based on typing patterns. As larger keystroke datasets become available, machine learning and deep learning algorithms are becoming popular. Knowledge of every possible impostor is not known during training which means that keystroke dynamics is an open set recognition problem. Treating open set recognition problems as closed set (assuming samples from all impostors are present) can cause models to incur data leakage, which can provide unrealistic overestimates of performance. It is a common problem in machine learning and can cause models to report higher accuracies than would be expected in the real world. In this paper, we outline open set recognition and discuss how, if not handled properly, it can lead to data leakage. The performance of common machine learning methods, such as SVM and MLP are investigated with and without leakage to clearly demonstrate the differences in performance. A synthetic dataset and a publicly available keystroke dynamics fixed-text dataset are used for research transparency and reproducibility.

关键词： Training Support vector machines Deep learning computer vision Machine learning algorithms Heuristic algorithms conferences

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Ex-Model: Continual Learning from a Stream of Trained Models

Ex-Model: Continual Learning from a Stream of Trained Models

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Carta, Antonio Cossu, Andrea Lomonaco, Vincenzo Bacciu, Davide Univ Pisa Pisa Italy Scuola Normale Super Pisa Pisa Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize continually in an efficient, effective, and scalable way is fundamental for a sustainable development of Artificial Intelligent systems. However, an agent-centric view of continual learning requires learning directly from raw data, which limits the interaction between independent agents, the efficiency, and the privacy of current approaches. Instead, we argue that continual learning systems should exploit the availability of compressed information in the form of trained models. In this paper, we introduce and formalize a new paradigm named "Ex-Model Continual Learning" (ExML), where an agent learns from a sequence of previously trained models instead of raw data. We further contribute with three ex-model continual learning algorithms and an empirical setting comprising three datasets (MNIST, CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are extensively tested. Finally, we highlight the peculiarities of the ex-model paradigm and we point out interesting future research directions.

关键词： Learning systems Data privacy computer vision conferences Computational modeling Data models pattern recognition

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Sea Situational Awareness (SeaSAw) Dataset

Sea Situational Awareness (SeaSAw) Dataset

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kaur, Parneet Aziz, Arslan Jain, Darshan Patel, Harshil Hirokawa, Jonathan Townsend, Lachlan Reimers, Christoph Hua, Fiona

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Vessels move 90% of international cargo by volume, with the marine economy contributing to 5.1% of global GDP. As one of the oldest industries, the marine industry has yet to embrace innovations in modern technology to safeguard the blue economy. Situational awareness from intelligent vessel systems can enable enhanced safety and decision-making for mariners. As the foundation for these intelligent systems, advanced perception technology requires sufficient real-world operational data to leverage recent AI technologies. In this work, we introduce the Sea Situational Awareness (SeaSAw) dataset - a novel dataset that is comprised of 1.9 million images with 14.6 million objects associated with 20.4 million attributes from 12 object classes, making it the largest maritime dataset for object detection, fine-grained classification and tracking. Furthermore, this dataset consists of 9 sources in combination with various RGB cameras, mounted on different moving vessels, operating in different geographic locations globally, having variations in scenario, weather and illumination conditions. This data collection took place across 4 years with rigorous efforts on data selection, annotation, management and analysis to enhance the marine perception technology.

关键词： Industries computer vision Technological innovation Image resolution Lighting Object detection Safety

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Understanding the Nature of First-Person Videos: Characterization and Classification using Low-Level Features

Understanding the Nature of First-Person Videos: Characteriz...

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Tan, Cheston Goh, Hanlin Chandrasekhar, Vijay Li, Liyuan Lim, Joo-Hwee Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781479943098

First-person view (FPV) video data is set to proliferate rapidly, due to many consumer wearable-camera devices coming onto the market. Research into FPV (or "egocentric") vision is also becoming more common in the computer vision community. However, it is still unclear what the fundamental characteristics of such data are. How is it really different from third-person view (TPV) data? Can all FPV data be treated the same? In this first attempt to approach these questions in a quantitative and empirical manner, we analyzed a meta-collection of 21 FPV and TPV datasets totaling more than 165 hours of video. We performed the first quantitative characterization of FPV videos over multiple datasets, encompassing virtually all available FPV datasets. Validating this characterization, linear classifiers trained on low-level features to perform FPVversus-TPV classification achieved good baseline performance. Accuracy peaked at 81% for 2-minute clips, but 67% accuracy was achieved even with 1-second clips. Our low-level features are fast to compute and do not require annotation. Overall, our work uncovered insights regarding the basic nature and characteristics of FPV data.

关键词： LOW LEVEL Videotapes Pepper golden mosaic virus Dataset images computer vision clipping Clip Camera device

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Less is More: Proxy Datasets in NAS approaches

Less is More: Proxy Datasets in NAS approaches

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Moser, Brian Raue, Federico Hees, Joern Dengel, Andreas German Res Ctr Artificial Intelligence DFKI Darmstadt Germany TU Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) defines the design of Neural Networks as a search problem. Unfortunately, NAS is computationally intensive because of various possibilities depending on the number of elements in the design and the possible connections between them. In this work, we extensively analyze the role of the dataset size based on several sampling approaches for reducing the dataset size (unsupervised and supervised cases) as an agnostic approach to reduce search time. We compared these techniques with four common NAS approaches in NAS-Bench-201 in roughly 1,400 experiments on CIFAR-100. One of our surprising findings is that in most cases we can reduce the amount of training data to 25%, consequently also reducing search time to 25%, while at the same time maintaining the same accuracy as if training on the full dataset. In addition, some designs derived from subsets out-perform designs derived from the full dataset by up to 22 p.p. accuracy.

关键词： Training computer vision conferences Neural networks Training data computer architecture Search problems

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It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation 30

It's Written All Over Your Face: Full-Face Appearance-Based ...

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30th IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhang, Xucong Sugano, Yusuke Fritz, Mario Bulling, Andreas Max Planck Inst Informat Perceptual User Interfaces Grp Saarland Informat Campus Saarbrucken Germany Max Planck Inst Informat Scalable Learning & Percept Grp Saarland Informat Campus Saarbrucken Germany Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka Japan Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9781538607336

Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses.

关键词： computer vision

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