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EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task.

EarthNet2021: A large-scale dataset and challenge for Earth ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Requena-Mesa, Christian Benson, Vitus Reichstein, Markus Runge, Jakob Denzler, Joachim Max Planck Inst Biogeochem Biogeochem Integrat Jena Germany German Aerosp Ctr DLR Inst Data Sci Jena Germany Univ Jena Comp Vis Grp Jena Germany Michael Stifel Ctr Jena Data Driven & Simulat Sci Jena Germany Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781665448994

Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather. EarthNet2021 is a large dataset suitable for training deep neural networks on the task. It contains Sentinel 2 satellite imagery at 20 m resolution, matching topography and mesoscale (1.28 km) meteorological variables packaged into 32000 samples. Additionally we frame EarthNet2021 as a challenge allowing for model intercomparison. Resulting forecasts will greatly improve (> x50) over the spatial resolution found in numerical models. This allows localized impacts from extreme weather to be predicted, thus supporting downstream applications such as crop yield prediction, forest health assessments or biodiversity monitoring. Find data, code, and how to participate at ***.

关键词： Earth Training Satellites Weather forecasting Surface topography Numerical models pattern recognition

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Deep Fusion of Appearance and Frame Differencing for Motion Segmentation

Deep Fusion of Appearance and Frame Differencing for Motion ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ellenfeld, Marc Moosbauer, Sebastian Cardenes, Ruben Klauck, Ulrich Teutsch, Michael Hensoldt Optron GmbH Oberkochen Germany Hensoldt Analyt GmbH Oberkochen Germany Aalen Univ Appl Sci Aalen Germany Univ Western Cape Cape Town South Africa

ISBN: (纸本)9781665448994

Motion segmentation is a technique to detect and localize class-agnostic motion in videos. This motion is assumed to be relative to a stationary background and usually originates from objects such as vehicles or humans. When the camera moves, too, frame differencing approaches that do not have to model the stationary background over minutes, hours, or even days are more promising compared to background subtraction methods. In this paper, we propose a Deep Convolutional Neural Network (DCNN) for multi-modal motion segmentation: the current image contributes with appearance information to distinguish between relevant and irrelevant motion and frame differencing captures the temporal information, which is the scene's motion independent of the camera motion. We fuse this information to receive an effective and efficient approach for robust motion segmentation. The effectiveness is demonstrated using the multi-spectral CDNet-2014 dataset that we re-labeled for motion segmentation. We specifically show that we can detect tiny moving objects significantly better compared to methods based on optical flow.

关键词： computer vision Image segmentation Three-dimensional displays Fuses Motion segmentation Cameras pattern recognition

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DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring Dataset for Real-World Outdoor Scenarios

DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Bolten, Tobias Pohle-Froehlich, Regina Toennies, Klaus D. Hsch Niederrhein Inst Pattern Recognit Krefeld Germany Univ Magdeburg Dept Simulat & Graph Magdeburg Germany

ISBN: (纸本)9781665448994

Neuromorphic vision sensors are biologically inspired devices which differ fundamentally from well known frame-based sensors. Even though developments in this research area are increasing, applications that rely entirely on event cameras are still relatively rare. This becomes particularly clear when considering real outdoor scenarios apart from laboratory conditions. One obstacle to the development of event-based vision applications in this context may be the lack of labeled datasets for algorithm development and evaluation. Therefore we describe a recording setting of a DVS-based long time monitoring of an urban public area and provide labeled DVS data that also contain effects of environmental outdoor influences recorded in this process. We also describe the processing chain used for label generation, as well as results from a performed denoising benchmark utilizing various spatio-temporal event stream filters. The dataset contains almost 7 hours of real world outdoor event-data with approximate to 47k labeled regions of interest and can be downloaded at http://***/DVS-OUTLAB/

关键词： Privacy Rain Power demand Neuromorphics Noise reduction Pipelines vision sensors

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Insights from the Future for Continual Learning

Insights from the Future for Continual Learning

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Douillard, Arthur Valle, Eduardo Ollion, Charles Robert, Thomas Cord, Matthieu Sorbonne Univ Paris France Heuritech Paris France Univerty Campinas Campinas Brazil CMAP Ecole Polytech Palaiseau France Valeo Ai Paris France

ISBN: (纸本)9781665448994

Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting. In this work, we propose prescient continual learning, a novel experimental setting, to incorporate existing information about the classes, prior to any training data. Usually, each task in a traditional continual learning setting evaluates the model on present and past classes, the latter with a limited number of training samples. Our setting adds future classes, with no training samples at all. We introduce Ghost Model, a representation-learning-based model for continual learning using ideas from zero-shot learning. A generative model of the representation space in concert with a careful adjustment of the losses allows us to exploit insights from future classes to constraint the spatial arrangement of the past and current classes. Quantitative results on the AwA2 and aP&Y datasets and detailed visualizations showcase the interest of this new setting and the method we propose to address it.(1)

关键词： Training computer vision Computational modeling conferences Training data pattern recognition Task analysis

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Supervised deep learning of elastic SRV distances on the shape space of curves

Supervised deep learning of elastic SRV distances on the sha...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hartman, Emmanuel Sukurdeep, Yashil Charon, Nicolas Klassen, Eric Bauer, Martin Florida State Univ Dept Math Tallahassee FL 32306 USA Johns Hopkins Univ Dept Appl Math Baltimore MD USA

ISBN: (纸本)9781665448994

Motivated by applications from computer vision to bioinformatics, the field of shape analysis deals with problems where one wants to analyze geometric objects, such as curves, while ignoring actions that preserve their shape, such as translations, rotations, scalings, or reparametrizations. Mathematical tools have been developed to define notions of distances, averages, and optimal deformations for geometric objects. One such framework, which has proven to be successful in many applications, is based on the square root velocity (SRV) transform, which allows one to define a computable distance between spatial curves regardless of how they are parametrized. This paper introduces a supervised deep learning framework for the direct computation of SRV distances between curves, which usually requires an optimization over the group of reparametrizations that act on the curves. The benefits of our approach in terms of computational speed and accuracy are illustrated via several numerical experiments on both synthetic and real data.

关键词： Training Deep learning computer vision Shape conferences Transforms Tools

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BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains

BalaGAN: Cross-Modal Image Translation Between Imbalanced Do...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Patashnik, Or Danon, Dov Zhang, Hao Cohen-Or, Daniel Tel Aviv Univ Tel Aviv Israel Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665448994

State-of-the-art image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity. We introduce a new unsupervised translation network, BalaGAN, specifically designed to tackle the domain imbalance problem. We leverage the latent modalities of the richer domain to turn the image-to-image translation problem, between two imbalanced domains, into a multi-class translation problem, more resembling the style transfer setting. Specifically, we analyze the source domain and learn a decomposition of it into a set of latent modes or classes, without any supervision. This leaves us with a multitude of balanced crossdomain translation tasks, between all pairs of classes, including the target domain. During inference, the trained network takes as input a source image, as well as a reference style image from one of the modes as a condition, and produces an image which resembles the source on the pixel-wise level, but shares the same mode as the reference. We show that employing modalities within the dataset improves the quality of the translated images, and that BalaGAN outperforms strong baselines of both unconditioned and style-transfer-based image-to-image translation methods, in terms of image quality and diversity.

关键词： Training Image quality computer vision conferences pattern recognition Task analysis

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Adversarial Robust Model Compression using In-Train Pruning

Adversarial Robust Model Compression using In-Train Pruning

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Vemparala, Manoj-Rohit Fasfous, Nael Frickenstein, Alexander Sarkar, Sreetama Zhao, Qi Kuhn, Sabine Frickenstein, Lukas Singh, Anmol Unger, Christian Nagaraja, Naveen-Shankar Wressnegger, Christian Stechele, Walter BMW Autonomous Driving Munich Germany Tech Univ Munich Munich Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665448994

Efficiently deploying learning-based systems on embedded hardware is challenging for various reasons, two of which are considered in this paper: The model's size and its robustness against attacks. Both need to be addressed even-handedly. We combine adversarial training and model pruning in a joint formulation of the fundamental learning objective during training. Unlike existing post-train pruning approaches, our method does not use heuristics and eliminates the need for a pre-trained model. This allows for a classifier which is robust against attacks and enables better compression of the model, reducing its computational effort. In comparison to prior work, our approach yields 6.21 pp higher accuracy for an 85 % reduction in parameters for ResNet20 on the CIFAR-10 dataset.

关键词： Training computer vision Computational modeling conferences Robustness Hardware pattern recognition

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A Deep Adversarial Framework for Visually Explainable Periocular recognition

A Deep Adversarial Framework for Visually Explainable Perioc...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Brito, Joao Proenca, Hugo Univ Beira Interior IT Inst Telecomunicacoes P-6200001 Covilha Portugal

ISBN: (纸本)9781665448994

In the biometrics context, the ability to provide the reasoning behind a decision has been at the core of major research efforts. Explanations serve not only to increase the trust amongst the users of a system, but also to augment the system's overall accountability and transparency. In this work, we describe a periocular recognition framework that not only performs biometric recognition, but also provides visual representations of the features/regions that supported a decision. Being particularly designed to explain non-match ("impostors") decisions, our solution uses adversarial generative techniques to synthesise a large set of "genuine" image pairs, from where the most similar elements with respect to a query are retrieved. Then, assuming the alignment between the query/retrieved pairs, the element-wise differences between the query and a weighted average of the retrieved elements yields a visual explanation of the regions in the query pair that would have to be different to transform it into a "genuine" pair. Our quantitative and qualitative experiments validate the proposed solution, yielding recognition rates that are similar to the state-of-the-art, but - most importantly - also providing the visual explanations for every decision.

关键词： Visualization computer vision Biometrics (access control) conferences Transforms Cognition pattern recognition

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GOO: A Dataset for Gaze Object Prediction in Retail Environments

GOO: A Dataset for Gaze Object Prediction in Retail Environm...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Tomas, Henri Reyes, Marcus Dionido, Raimarc Ty, Mark Mirando, Jonric Casimiro, Joel Atienza, Rowel Guinto, Richard Univ Philippines Quezon City Philippines Samsung R&D Inst Philippines Taguig Philippines

ISBN: (纸本)9781665448994

One of the most fundamental and information-laden actions humans do is to look at objects. However, a survey of current works reveals that existing gaze-related datasets annotate only the pixel being looked at, and not the boundaries of a specific object of interest. This lack of object annotation presents an opportunity for further advancing gaze estimation research. To this end, we present a challenging new task called gaze object prediction, where the goal is to predict a bounding box for a person's gazed-at object. To train and evaluate gaze networks on this task, we present the Gaze On Objects (GOO) dataset. GOO is composed of a large set of synthetic images (GOO-Synth) supplemented by a smaller subset of real images (GOO-Real) of people looking at objects in a retail environment. Our work establishes extensive baselines on GOO by re-implementing and evaluating selected state-of-the-art models on the task of gaze following and domain adaptation. Code is available(1) on github.

关键词： Training computer vision Adaptation models conferences Estimation computer architecture Benchmark testing

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Learning to Detect Phone-related Pedestrian Distracted Behaviors with Synthetic Data

Learning to Detect Phone-related Pedestrian Distracted Behav...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hatay, Emre Ma, Jin Sun, Huiming Fang, Jianwu Gao, Zhiqiang Yu, Hongkai Cleveland State Univ Cleveland OH 44115 USA Changan Univ Xian Peoples R China

ISBN: (纸本)9781665448994

Due to the popularity and mobility of smart phones, phone-related pedestrian distracted behaviors, e.g., Texting, Game Playing, and Phone calls, have caused many traffic fatalities and accidents. As an advanced driver-assistance or autonomous-driving system, computer vision could be used to automatically detect distractions from cameras installed on the vehicle for useful safety intervention. The state-of-the-art method models this problem as a standard supervised learning method with a two-branch Convolutional Neural Network (CNN) followed by a voting on all image frames. In contrast, this paper proposes a new synthetic dataset named SYN-PPDB (448 synchronized video pairs of 53,760 computer game images) for this research problem and models it as a transfer learning problem from synthetic data to real data. A new deep learning model embedded with spatial-temporal feature learning and pose-aware transfer learning is proposed. Experimental results show that we could improve the state-of-the-art overall recognition accuracy from 84.27% to 96.67%.

关键词： computer vision Computational modeling Transfer learning Supervised learning Games pattern recognition Synchronization

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