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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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Learning Network Architectures of Deep CNNs under Resource Constraints 31

Learning Network Architectures of Deep CNNs under Resource C...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chan, Michael Scarafoni, Daniel Duarte, Ronald Thornton, Jason Skelly, Luke MIT Lincoln Lab 244 Wood St Lexington MA 02421 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recent works in deep learning have been driven broadly by the desire to attain high accuracy on certain challenge problems. The network architecture and other hyper-parameters of many published models are typically chosen by trial-and-error experiments with little considerations paid to resource constraints at deployment time. We propose a fully automated model learning approach that (1) treats architecture selection as part of the learning process, (2) uses a blend of broad-based random sampling and adaptive iterative refinement to explore the solution space, (3) performs optimization subject to given memory and computational constraints imposed by target deployment scenarios, and (4) is scalable and can use only a practically small number of GPUs for training. We present results that show graceful model degradation under strict resource constraints for object classification problems using CIFAR-10 in our experiments. We also discuss future work in further extending the approach.

关键词： computer architecture Computational modeling Optimization Adaptation models Network architecture Linear programming Training

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QuadricSLAM: Dual Quadrics as SLAM Landmarks 31

QuadricSLAM: Dual Quadrics as SLAM Landmarks

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Nicholson, Lachlan Milford, Michael Sunderhauf, Niko Queensland Univ Technol ARC Ctr Excellence Robot Vis Brisbane Qld Australia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Research in Simultaneous Localization And Mapping (SLAM) is increasingly moving towards richer world representations involving objects and high level features that enable a semantic model of the world for robots, potentially leading to a more meaningful set of robot-world interactions. Many of these advances are grounded in state-of-the-art computer vision techniques primarily developed in the context of image-based benchmark datasets, leaving several challenges to be addressed in adapting them for use in robotics. In this paper, we derive a SLAM formulation that uses dual quadrics as 3D landmark representations, exploiting their ability to compactly represent the size, position and orientation of an object, and show how 2D bounding boxes (such as those typically obtained from visual object detection systems) can directly constrain the quadric parameters via a novel geometric error formulation. We develop a sensor model for deep-learned object detectors that addresses the challenge of partial object detections often encountered in robotics applications, and demonstrate how to jointly estimate the camera pose and constrained dual quadric parameters in factor graph based SLAM with a general perspective camera.

关键词： Simultaneous localization and mapping Cameras Object detection Trajectory Robot vision systems Three-dimensional displays

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Incorporating Touch Biometrics to Mobile One-Time Passwords: Exploration of Digits 31

Incorporating Touch Biometrics to Mobile One-Time Passwords:...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab E-28049 Madrid Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This work evaluates the advantages and potential of incorporating touch biometrics to mobile one-time passwords (OTP). The new e-BioDigit database, which comprises on-line handwritten numerical digits from 0 to 9, has been acquired using the finger touch as input to a mobile device. This database is used in the experiments reported in this work and it is publicly available to the research community. An analysis of the OTP scenario using handwritten digits is carried out regarding which are the most discriminative handwritten digits and how robust the system is when increasing the number of them in the user password. Additionally, the best features for each handwritten numerical digit are studied in order to enhance our proposed biometric system. Our proposed approach achieves remarkable results with EERs ca. 5.0% when using skilled forgeries, outperforming other traditional biometric verification traits such as the handwritten signature or graphical passwords on similar mobile scenarios.

关键词： Biometrics (access control) Databases Password Mobile handsets Feature extraction Protocols

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Priming Neural Networks 31

Priming Neural Networks

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rosenfeld, Amir Biparva, Mandi Tsotsos, John K. York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Visual priming is known to affect the human visual system to allow detection of scene elements, even those that may have been near unnoticeable before, such as the presence of camouflaged animals. This process has been shown to be an effect of top-down signaling in the visual system triggered by the said cue. In this paper, we propose a mechanism to mimic the process of priming in the context of object detection and segmentation. We view priming as having a modulatory, cue dependent effect on layers of features within a network. Our results show how such a process can be complementary to, and at times more effective than simple post-processing applied to the output of the network, notably so in cases where the object is hard to detect such as in severe noise, small size or atypical appearance. Moreover, we find the effects of priming are sometimes stronger when early visual layers are affected. Overall, our experiments confirm that top-down signals can go a long way in improving object detection and segmentation.

关键词： Visualization Task analysis Object detection Neural networks Semantics computer vision Image segmentation

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ComboGAN: Unrestrained Scalability for Image Domain Translation 31

ComboGAN: Unrestrained Scalability for Image Domain Translat...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Anoosheh, Asha Agustsson, Eirikur Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol D ITET Comp Vis Lab Zurich Switzerland Swiss Fed Inst Technol D ITET Zurich Switzerland Merantix Berlin Germany Katholieke Univ Leuven ESAT Leuven Belgium

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The past year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two domains at a time, and scaling them to more would require an quadratic number of models to be trained. And with two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. In this paper, we propose a multi-component image translation model and training scheme which scales linearly - both in resource consumption and time required - with the number of domains. We demonstrate its capabilities on a dataset of paintings by 14 different artists and on images of the four different seasons in the Alps. Note that 14 data groups would need ( 14 choose 2) = 91 different CycleGAN models: a total of 182 generator/discriminator pairs;whereas our model requires only 14 generator/discriminator pairs.

关键词： Training Generators Gallium nitride Decoding computer vision Task analysis Data models

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Image Caption Generation with Hierarchical Contextual Visual Spatial Attention 31

Image Caption Generation with Hierarchical Contextual Visual...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Khademi, Mahmoud Schulte, Oliver Simon Fraser Univ Burnaby BC Canada

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present a novel context-aware attention-based deep architecture for image caption generation. Our architecture employs a Bidirectional Grid LSTM, which takes visual features of an image as input and learns complex spatial patterns based on two-dimensional context, by selecting or ignoring its input. The Grid LSTM has not been applied to image caption generation task before. Another novel aspect is that we leverage a set of local region-grounded texts obtained by transfer learning. The region-grounded texts often describe the properties of the objects and their relationships in an image. To generate a global caption for the image, we integrate the spatial features from the Grid LSTM with the local region-grounded texts, using a two-layer Bidirectional LSTM. The first layer models the global scene context such as object presence. The second layer utilizes a novel dynamic spatial attention mechanism, based on another Grid LSTM, to generate the global caption word-by-word, while considering the caption context around a word in both directions. Unlike recent models that use a soft attention mechanism, our dynamic spatial attention mechanism considers the spatial context of the image regions. Experimental results on MS-COCO dataset show that our architecture outperforms the state-of-the-art.

关键词： Feature extraction Visualization Logic gates computer architecture Task analysis Context modeling Computational modeling

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Efficient Semantic Segmentation using Gradual Grouping 31

Efficient Semantic Segmentation using Gradual Grouping

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Vallurupalli, Nikitha Annamaneni, Sriharsha Varma, Girish Jawahar, C., V Mathew, Manu Nagori, Soyeb IIIT Hyderabad Kohli Ctr Intelligent Syst Ctr Visual Informat Technol Hyderabad Telangana India Texas Instruments Inc Bangalore Karnataka India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Deep CNNs for semantic segmentation have high memory and run time requirements. Various approaches have been proposed to make CNNs efficient like grouped, shuffled, depth-wise separable convolutions. We study the effectiveness of these techniques on a real-time semantic segmentation architecture like ERFNet for improving run-time by over 5X. We apply these techniques to CNN layers partially or fully and evaluate the testing accuracies on Cityscapes dataset. We obtain accuracy vs parameters/FLOPs trade offs, giving accuracy scores for models that can run under specified runtime budgets. We further propose a novel training procedure which starts out with a dense convolution but gradually evolves towards a grouped convolution. We show that our proposed training method and efficient architecture design can improve accuracies by over 8% with depthwise separable convolutions applied on the encoder of ERFNet and attaching a light weight decoder. This results in a model which has a 5X improvement in FLOPs while only suffering a 4% degradation in accuracy with respect to ERFNet.

关键词： Training Semantics computer architecture Computational modeling Sparse matrices Predictive models Decoding

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A Fully Progressive Approach to Single-Image Super-Resolution 31

A Fully Progressive Approach to Single-Image Super-Resolutio...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Yifan Perazzi, Federico McWilliams, Brian Sorkine-Hornung, Alexander Sorkine-Hornung, Olga Schroers, Christopher Swiss Fed Inst Technol Zurich Switzerland Disney Res Zurich Switzerland Oculus Menlo Pk CA USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. This not only allows to scale well to high upsampling factors (e.g., 8x) but constitutes a principled multi-scale approach that increases the reconstruction quality for all upsampling factors simultaneously. In particular ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge [35]. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster.

关键词： Training Gallium nitride Image resolution Image reconstruction Generative adversarial networks computer architecture Generators

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Scene Grammar in Human and Machine recognition of Objects and Scenes 31

Scene Grammar in Human and Machine Recognition of Objects an...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bayat, Akram Koh, Do Hyong Nand, Anubhaw Kumar Pereira, Marta Pomplun, Marc Univ Massachusetts Boston Boston MA 02125 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, we study the effects of violating the high level scene syntactic and semantic rules on human eye-movement behavior and deep neural scene and object recognition networks. An eye-movement experimental study was conducted with twenty human subjects to view scenes from the SCEGRAM image database and determine whether there is an inconsistent object or not. We examine the contribution of multiple types of features that influence eye movements while searching for an inconsistent object in a scene (e.g., size and location of an object) by evaluating the consistency prediction power of the trained classifiers on fixation features. The results of the eye movement analysis and inconsistency prediction reveal that: 1) inconsistent objects are fixated significantly more than consistent objects in a scene, 2) the distribution of fixations is the main factor that is influenced by the inconsistency condition of a scene which is reflected in the ground truth fixation maps. It is also observed that the performance of deep object and scene recognition networks drops due to the violations of scene grammar. The class-specific visual saliency maps are created from the high-level representation of the convolutional layers of a deep network during the scene and object recognition process. We discuss whether the scene inconsistencies are represented in those saliency maps by evaluating their prediction powers using multiple well-known metrics including AUC, SIM, and KL. The results suggest that an inconsistent object in a scene causes significant variations in the prediction power of saliency maps.

关键词： Semantics Grammar Syntactics Object recognition Image databases Task analysis

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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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