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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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Improving Viseme recognition using GAN-based Frontal View Mapping 31

Improving Viseme Recognition using GAN-based Frontal View Ma...

引用

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

作者： Borges Oliveira, Dario Augusto Mattos, Andrea Britto Morais, Edmilson da Silva IBM Res Rua Tutoia 1157 Paraiso SP Brazil

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Deep learning methods have become the standard for Visual Speech recognition problems due to their high accuracy results reported in the literature. However, while successful works have been reported for words and sentences, recognizing shorter segments of speech, like phones, has proven to be much more challenging due to the lack of temporal and contextual information. Also, head-pose variation remains a known issue for facial analysis with direct impact in this problem. In this context, we propose a novel methodology to tackle the problem of recognizing visemes the visual equivalent of phonemes - using a GAN to artificially lock the face view into a perfect frontal view, reducing the view angle variability and simplifying the recognition task performed by our classification CNN. The GAN is trained using a large-scale synthetic 2D dataset based on realistic 3D facial models, automatically labelled for different visemes, mapping a slightly random view to a perfect frontal view. We evaluate our method using the GRID corpus, which was processed to extract viseme images and their corresponding synthetic frontal views to be further classified by our CNN model. Our results demonstrate that the additional synthetic frontal view is able to improve accuracy in 5.9% when compared with classification using the original image only.

关键词： Speech recognition Face Mouth Gallium nitride Hidden Markov models Visualization Task analysis

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Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System 31

Scene Understanding Networks for Autonomous Driving based on...

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

作者： Baek, JeongYeol Chelu, Ioana Veronica Iordache, Livia Paunescu, Vlad Ryu, HyunJoo Ghiuta, Alexandru Petreanu, Andrei Soh, YunSung Leica, Andrei Jeon, ByeongMoon LG Elect Convergence Ctr Seoul South Korea Arnia Software Bucharest Romania

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Modern driver assistance systems rely on a wide range of sensors (RADAR, LIDAR, ultrasound and cameras) for scene understanding and prediction. These sensors are typically used for detecting traffic participants and scene elements required for navigation. In this paper we argue that relying on camera based systems, specifically Around View Monitoring (AVM) system has great potential to achieve these goals in both parking and driving modes with decreased costs. The contributions of this paper are as follows: we present a new end-to-end solution for delimiting the safe drivable area for each frame by means of identifying the closest obstacle in each direction from the driving vehicle, we use this approach to calculate the distance to the nearest obstacles and we incorporate it into a unified end-to-end architecture capable of joint object detection, curb detection and safe drivable area detection. Furthermore, we describe the family of networks for both a high accuracy solution and a low complexity solution. We also introduce further augmentation of the base architecture with 3D object detection.

关键词： Object detection computer architecture Three-dimensional displays Cameras Task analysis Complexity theory Two dimensional displays

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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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Human Pose as Calibration pattern;3D Human Pose Estimation with Multiple Unsynchronized and Uncalibrated Cameras 31

Human Pose as Calibration Pattern;3D Human Pose Estimation w...

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

作者： Takahashi, Kosuke Mikami, Dan Isogawa, Mariko Kimata, Hideaki NIPPON TELEGRAPH & TEL COORPORAT NTT Media Intelligence Labs Tokyo Japan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper proposes a novel algorithm of estimating 3D human pose from multi-view videos captured by unsynchronized and uncalibrated cameras. In a such configuration, the conventional vision-based approaches utilize detected 2D features of common 3D points for synchronization and camera pose estimation, however, they sometimes suffer from difficulties of feature correspondences in case of wide baselines. For such cases, the proposed method focuses on that the projections of human joints can be associated each other robustly even in wide baseline videos and utilizes them as the common reference points. To utilize the projections of joint as the corresponding points, they should be detected in the images, however, these 2D joint sometimes include detection errors which make the estimation unstable. For dealing with such errors, the proposed method introduces two ideas. The first idea is to relax the reprojection errors for avoiding optimizing to noised observations. The second idea is to introduce an geometric constraint on the prior knowledge that the reference points consists of human joints. We demonstrate the performance of the proposed algorithm of synchronization and pose estimation with qualitative and quantitative evaluations using synthesized and real data.

关键词： Cameras Two dimensional displays Three-dimensional displays Pose estimation Videos Calibration

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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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Pseudo-labels for Supervised Learning on Dynamic vision Sensor Data, Applied to Object Detection under Ego-motion 31

Pseudo-labels for Supervised Learning on Dynamic Vision Sens...

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

作者： Chen, Nicholas F. Y. DSO Natl Labs 12 Sci Pk Dr Singapore 118225 Singapore

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In recent years, dynamic vision sensors (DVS), also known as event-based cameras or neuromorphic sensors, have seen increased use due to various advantages over conventional frame-based cameras. Using principles inspired by the retina, its high temporal resolution overcomes motion blurring, its high dynamic range overcomes extreme illumination conditions and its low power consumption makes it ideal for embedded systems on platforms such as drones and self-driving cars. However, event-based data sets are scarce and labels are even rarer for tasks such as object detection. We transferred discriminative knowledge from a state-of-the-art frame-based convolutional neural network (CNN) to the event-based modality via intermediate pseudo-labels, which are used as targets for supervised learning. We show, for the first time, event-based car detection under ego-motion in a real environment at 100 frames per second with a test average precision of 40.3% relative to our annotated ground truth. The event-based car detector handles motion blur and poor illumination conditions despite not explicitly trained to do so, and even complements frame-based CNN detectors, suggesting that it has learnt generalized visual representations.

关键词： vision sensors Cameras Vehicle dynamics Object detection Voltage control Supervised learning Automobiles

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Temporal Alignment Improves Feature Quality: an Experiment on Activity recognition with Accelerometer Data 31

Temporal Alignment Improves Feature Quality: an Experiment o...

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

作者： Choi, Hongjun Wang, Qiao Toledo, Meynard Turaga, Pavan Buman, Matthew Srivastava, Anuj Arizona State Univ Geometr Media Lab Sch Arts Media & Engn Tempe AZ 85287 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Arizona State Univ Sch Nutr & Hlth Promot Tempe AZ 85287 USA Florida State Univ Dept Stat Tallahassee FL 32306 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Activity recognition has been receiving significant attention from a variety of research areas such as human performance enhancement, health promotion, and human computer interaction. However, recognizing activities from accelerometer data still remains a challenging problem due to sensitivity to sampling rates, misalignment of data, and increased variability in activities among clinically relevant populations. In order to solve these issues, we adopt methods from functional analysis, which consider non-elastic rate variations in movement. The overall framework factors out temporal variability within activity classes, before leveraging robust machine learning pipelines for a given end-use. The proposed approach has been evaluated on 7 classes of everyday activities with 50 subjects. The results indicate that proposed approach achieves improved performance with the improvements observed in separating similar classes that differ in temporal rates, and also demonstrate higher robustness to change in window lengths. These results suggest that temporal alignment should be considered a core part of activity recognition pipelines.

关键词： Feature extraction Activity recognition Microsoft Windows Machine learning Pipelines Training Indexes

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Hard Example Mining with Auxiliary Embeddings 31

Hard Example Mining with Auxiliary Embeddings

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

作者： Smirnov, Evgeny Melnikov, Aleksandr Oleinik, Andrei Ivanova, Elizaveta Kalinovskiy, Ilya Luckyanets, Eugene Speech Technol Ctr Moscow Russia ITMO Univ St Petersburg Russia

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Hard example mining is an important part of the deep embedding learning. Most methods perform it at the mini-batch level. However, in the large-scale settings there is only a small chance that proper examples will appear in the same mini-batch and will be coupled into the hard example pairs or triplets. Doppelganger mining was previously proposed to increase this chance by means of class-wise similarity. This method ensures that examples of similar classes are sampled into the same mini-batch together. One of the drawbacks of this method is that it operates only at the class level, while there also might be a way to select appropriate examples within class in a more elaborated way than randomly. In this paper, we propose to use auxiliary embeddings for hard example mining. These embeddings are constructed in such way that similar examples have close embeddings in the cosine similarity sense. With the help of these embeddings it is possible to select new examples for the mini-batch based on their similarity with the already selected examples. We propose several ways to create auxiliary embeddings and use them to increase the number of potentially hard positive and negative examples in each minibatch. Our experiments on the challenging Disguised Faces in the Wild (DFW) dataset show that hard example mining with auxiliary embeddings improves the discriminative power of learned representations.

关键词： Training Prototypes Measurement Speech recognition Face recognition Neural networks Image recognition

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