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Suppressing Model Overfitting for Image Super-Resolution Networks

Suppressing Model Overfitting for Image Super-Resolution Net...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Ruicheng Feng Jinjin Gu Yu Qiao Chao Dong ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences The School of Science and Engineering The Chinese University of Hong Kong

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved. However, in real-world scenarios, due to the limited accessible training pairs, large models exhibit undesirable behaviors such as overfitting and memorization. To suppress model overfitting and further enjoy the merits of large model capacity, we thoroughly investigate generic approaches for supplying additional training data pairs. In particular, we introduce a simple learning principle MixUp to train networks on interpolations of sample pairs, which encourages networks to support linear behavior in-between training samples. In addition, we propose a data synthesis method with learned degradation, enabling models to use extra high-quality images with higher content diversity. This strategy proves to be successful in reducing biases of data. By combining these components - MixUp and synthetic training data, large models can be trained without overfitting under very limited data samples and achieve satisfactory generalization performance. Our method won the second place in NTIRE2019 Real SR Challenge.

关键词： super-resolution Network images Training data Data sampling Imagery (Psychotherapy) Image

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Pedestrian Detection in Thermal Images using Saliency Maps

Pedestrian Detection in Thermal Images using Saliency Maps

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Debasmita Ghose Shasvat M. Desai Sneha Bhattacharya Deep Chakraborty Madalina Fiterau Tauhidur Rahman College of Information and Computer Sciences University of Massachusetts

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Thermal images are mainly used to detect the presence of people at night or in bad lighting conditions, but perform poorly at daytime. To solve this problem, most state-of-the-art techniques employ a fusion network that uses features from paired thermal and color images. Instead, we propose to augment thermal images with their saliency maps, to serve as an attention mechanism for the pedestrian detector especially during daytime. We investigate how such an approach results in improved performance for pedestrian detection using only thermal images, eliminating the need for paired color images. For our experiments, we train the Faster R-CNN for pedestrian detection and report the added effect of saliency maps generated using static and deep methods (PiCA-Net and R~3-Net). Our best performing model results in an absolute reduction of miss rate by 13.4% and 19.4% over the baseline in day and night images respectively. We also annotate and release pixel level masks of pedestrians on a subset of the KAIST Multispec-tral Pedestrian Detection dataset, which is a first publicly available dataset for salient pedestrian detection.

关键词： Pedestrians color image images miss rate Thermal maps Daytime

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Recurrent Segmentation for Variable Computational Budgets 31

Recurrent Segmentation for Variable Computational Budgets

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

作者： McIntosh, Lane Maheswaranathan, Niru Sussillo, David Shlens, Jonathon Stanford Univ Stanford CA 94305 USA Google Brain Mountain View CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

State-of-the-art systems for semantic image segmentation use feed-forward pipelines with fixed computational costs. Building an image segmentation system that works across a range of computational budgets is challenging and time-intensive as new architectures must be designed and trained for every computational setting. To address this problem we develop a recurrent neural network that successively improves prediction quality with each iteration. Importantly, the RNN may be deployed across a range of computational budgets by merely running the model for a variable number of iterations. We find that this architecture is uniquely suited for efficiently segmenting videos. By exploiting the segmentation of past frames, the RNN can perform video segmentation at similar quality but reduced computational cost compared to state-of-the-art image segmentation methods. When applied to static images in the PASCAL VOC 2012 and Cityscapes segmentation datasets, the RNN traces out a speed-accuracy curve that saturates near the performance of state-of-the-art segmentation methods.

关键词： Image segmentation Semantics computer architecture Computational efficiency Videos Computational modeling Network architecture

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Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

Complexer-YOLO: Real-Time 3D Object Detection and Tracking o...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Martin Simon Karl Amende Andrea Kraus Jens Honer Timo Samann Hauke Kaulbersch Stefan Milz Horst Michael Gross Valeo Schalter und Sensoren GmbH Ilmenau University of Technology

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Accurate detection of 3D objects is a fundamental problem in computer vision and has an enormous impact on autonomous cars, augmented/virtual reality and many applications in robotics. In this work we present a novel fusion of neural network based state-of-the-art 3D detector and visual semantic segmentation in the context of autonomous driving. Additionally, we introduce Scale-Rotation-Translation score (SRTs), a fast and highly pa-rameterizable evaluation metric for comparison of object detections, which speeds up our inference time up to 20% and halves training time. On top, we apply state-of-the-art online multi target feature tracking on the object measurements to further increase accuracy and robustness utilizing temporal information. Our experiments on KITTI show that we achieve same results as state-of-the-art in all related categories, while maintaining the performance and accuracy trade-off and still run in real-time. Furthermore, our model is the first one that fuses visual semantic with 3D object detection.

关键词： Object detection images computer vision Semantics tracking time information robotics Clouds

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A Comparative Study of Real-time Semantic Segmentation for Autonomous Driving 31

A Comparative Study of Real-time Semantic Segmentation for A...

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

作者： Siam, Mennatullah Gamal, Mostafa Abdel-Razek, Moemen Yogamani, Senthil Jagersand, Martin Zhang, Hong Univ Alberta Edmonton AB Canada Cairo Univ Giza Egypt Valeo Vis Syst Tuam Ireland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic segmentation is a critical module in robotics related applications, especially autonomous driving. Most of the research on semantic segmentation is focused on improving the accuracy with less attention paid to computationally efficient solutions. Majority of the efficient semantic segmentation algorithms have customized optimizations without scalability and there is no systematic way to compare them. In this paper, we present a real-time segmentation benchmarking framework and study various segmentation algorithms for autonomous driving. We implemented a generic meta-architecture via a decoupled design where different types of encoders and decoders can be plugged in independently. We provide several example encoders including VGG16, Resnet18, MobileNet, and ShuffleNet and decoders including SkipNet, UNet and Dilation Frontend. The framework is scalable for addition of new encoders and decoders developed in the community for other vision tasks. We performed detailed experimental analysis on cityscapes dataset for various combinations of encoder and decoder. The modular framework enabled rapid prototyping of a custom efficient architecture which provides similar to x143 GFLOPs reduction compared to SegNet and runs real-time at similar to 15 fps on NVIDIA Jetson TX2. The source code of the framework is publicly available(1).

关键词： Convolution Semantics computer architecture Decoding Context modeling Real-time systems Image segmentation

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MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning

MultiNet++: Multi-Stream Feature Aggregation and Geometric L...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Sumanth Chennupati Ganesh Sistu Senthil Yogamani Samir A Rawashdeh Valco North America Valeo Vision Systems University of Michigan-Dearborn

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Multi-task learning is commonly used in autonomous driving for solving various visual perception tasks. It offers significant benefits in terms of both performance and computational complexity. Current work on multi-task learning networks focus on processing a single input image and there is no known implementation of multi-task learning handling a sequence of images. In this work, we propose a multi-stream multi-task network to take advantage of using feature representations from preceding frames in a video sequence for joint learning of segmentation, depth, and motion. The weights of the current and previous encoder are shared so that features computed in the previous frame can be leveraged without additional computation. In addition, we propose to use the geometric mean of task losses as a better alternative to the weighted average of task losses. The proposed loss function facilitates better handling of the difference in convergence rates of different tasks. Experimental results on KITTI, Cityscapes and SYNTHIA datasets demonstrate that the proposed strategies outperform various existing multi-task learning solutions.

关键词： Learning PARALLEL PROCESSING (computerS) Video sequences image sequences Weighted Average urban landscape Geometric mean Frame

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Assessing Shape Bias Property of Convolutional Neural Networks 31

Assessing Shape Bias Property of Convolutional Neural Networ...

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

作者： Hosseini, Hossein Xiao, Baicen Jaiswal, Mayoore Poovendran, Radha Univ Washington Dept Elect Engn NSL Seattle WA 98195 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

It is known that humans display "shape bias" when classifying new items, i.e., they prefer to categorize objects based on their shape rather than color. Convolutional Neural Networks (CNNs) are also designed to take into account the spatial structure of image data. In fact, experiments on image datasets, consisting of triples of a probe image, a shape-match and a color-match, have shown that one-shot learning models display shape bias as well. In this paper, we examine the shape bias property of CNNs. In order to conduct large scale experiments, we propose using the model accuracy on images with reversed brightness as a metric to evaluate the shape bias property. Such images, called negative images, contain objects that have the same shape as original images, but with different colors. Through extensive systematic experiments, we investigate the role of Afferent factors, such as training data, model architecture, initialization and regularization techniques, on the shape bias property of CNNs. We show that it is possible to design different CNNs that achieve similar accuracy on original images, but perform significantly different on negative images, suggesting that CNNs do not intrinsically display shape bias. We then show that CNNs are able to learn and generalize the structures, when the model is properly initialized or data is properly augmented, and if batch normalization is used.

关键词： computer vision pattern recognition conferences

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Empirically Analyzing the Effect of Dataset Biases on Deep Face recognition Systems 31

Empirically Analyzing the Effect of Dataset Biases on Deep F...

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

作者： Kortylewski, Adam Egger, Bernhard Schneider, Andreas Gerig, Thomas Morel-Forster, Andreas Vetter, Thomas Univ Basel Dept Math & Comp Sci Basel Switzerland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only provide limited insight about the generalization ability of a Deep Convolutional Neural Networks (DCNNs). We propose to empirically study the effect of different types of dataset biases on the generalization ability of DCNNs. Using synthetically generated face images, we study the face recognition rate as a function of interpretable parameters such as face pose and light. The proposed method allows valuable details about the generalization performance of different DCNN architectures to be observed and compared. In our experiments, we find that: 1) Indeed, dataset bias has a significant influence on the generalization performance of DCNNs. 2) DCNNs can generalize surprisingly well to unseen illumination conditions and large sampling gaps in the pose variation. 3) Using the presented methodology we reveal that the VGG-16 architecture outperforms the AlexNet architecture at face recognition tasks because it can much better generalize to unseen face poses, although it has significantly more parameters. 4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation. 5) We demonstrate that our findings on synthetic data also apply when learning from real-world data. Our face image generator is publicly available to enable the community to benchmark other DCNN architectures.

关键词： Face Face recognition Lighting Generators Training data computer architecture Task analysis

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SCAN: Spatial Color Attention Networks for Real Single Image Super-Resolution

SCAN: Spatial Color Attention Networks for Real Single Image...

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ieee/cvf conference on computer vision and pattern recognition workshops

作者： Xuan Xu Xin Li Lane Department of Computer Science and Electrical Engineering West Virginia University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Conceptually similar to adaptation in model-based approaches, attention has received increasing more attention in deep learning recently. As a tool to reallocate limited computational resources based on the importance of informative components, attention mechanism has found successful applications in both high-level and low-level vision tasks which includes channel attention, spatial attention, non-local attention and etc. However, to the best of our knowledge, attention mechanism has not been studied for the R,G,B channels of color images in the open literature. In this paper, we propose a spatial color attention networks (SCAN) designed to jointly exploit the spatial and spectral dependency within color images. More specifically, we present a spatial color attention module that calibrates important color information for individual color components from output feature maps of residual groups. When compared against previous state-of-the-art method Residual Channel Attention Networks (RCAN), SCAN has achieved superior performance in terms of both subjective and objective qualities on the dataset provided by NTIRE2019 real single image super-resolution challenge.

关键词： color image super-resolution low-level vision Color chromatic information red-green-blue colour model

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Design of a Reconfigurable 3D Pixel-Parallel Neuromorphic Architecture for Smart Image Sensor 31

Design of a Reconfigurable 3D Pixel-Parallel Neuromorphic Ar...

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

作者： Bhowmik, Pankaj Pantho, Jubaer Hossain Asadinia, Marjan Bobda, Christophe Univ Arkansas Fayetteville AR 72701 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Power reduction and speed-up of image processing algorithms remain of high interest as image resolutions continue to increase. Neuromorphic-circuits are inspired by the nervous system aiming to reduce power consumption and speed-up. This paper presents a neuromorphic smart image sensor designed by the pixel-parallel 3D hierarchical architecture with an on-chip attention module. The module dynamically detects regions with relevant information and produces a feedback path to sample those regions at high speed. On the other hand, by sampling non-relevant regions with a low-speed, the sensor can reduce redundancy and enable high-performance computing by ensuring low-power operation. The image sensor is comprised of several hierarchical planes and each plane has small and independent reconfigurable computational units (XPU). In each plane, all XPUs operate in parallel with a different operating speed which gives a pixel-parallel architecture. When the raw image passes through the hierarchical planes, necessary image processing algorithms are performed in parallel on different planes at a variable clock rate for saving power and reducing redundancy. The goal of this work is to prototype the focal plane image sensor which emulates the brain features. The results show that the prototype achieves remarkable power saving and speed-up at different stages.

关键词： computer architecture Image sensors Visualization Program processors Clocks Three-dimensional displays Image processing

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