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On the Robustness of Monte Carlo Dropout Trained with Noisy Labels

On the Robustness of Monte Carlo Dropout Trained with Noisy ...

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

作者： Goel, Purvi Li Chen Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665448994

The memorization effect of deep learning hinders its performance to effectively generalize on test set when learning with noisy labels. Prior study has discovered that epistemic uncertainty techniques are robust when trained with noisy labels compared with neural networks without uncertainty estimation. They obtain prolonged memorization effect and better generalization performance under the adversarial setting of noisy labels. Due to its superior performance amongst other selected epistemic uncertainty methods under noisy labels, we focus on Monte Carlo Dropout (MCDropout) and investigate why it is robust when trained with noisy labels. Through empirical studies on datasets MNIST, CIFAR-10, Animal-10n, we deep dive into three aspects of MCDropout under noisy label setting: 1. efficacy: understanding the learning behavior and test accuracy of MCDropout when training set contains artificially generated or naturally embedded label noise;2. representation volatility: studying the responsiveness of neurons by examining the mean and standard deviation on each neuron's activation;3. network sparsity: investigating the network support of MCDropout in comparison with deterministic neural networks. Our findings suggest that MCDropout further sparsifies and regularizes the deterministic neural networks and thus provides higher robustness against noisy labels.

关键词： Training Uncertainty Monte Carlo methods Neurons Estimation Robustness pattern recognition

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Learned Video Compression with Intra-Guided Enhancement and Implicit Motion Information

Learned Video Compression with Intra-Guided Enhancement and ...

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

作者： Zou, Nannan Zhang, Honglei Cricri, Francesco Tavakoli, Hamed R. Lainema, Jani Aksu, Emre Hannuksela, Miska Rahtu, Esa Nokia Technol Tampere Finland Tampere Univ Tampere Finland

ISBN: (纸本)9781665448994

Although learned approaches to video compression have been proposed with promising results, hand-engineered video codecs are still unbeaten. On the other hand, learned image compression has already surpassed traditional image codecs. In this paper, we propose a learned video compression system that mimics part of the pipeline of traditional codecs while leveraging learned image compression. It comprises two main modules: a learned intra-frame compression module, and a learned inter-frame compression module that is conditioned on intra-coded frames. These modules use separate learned probability models for entropy coding. The intra-frame codec uses a variant of nonlocal attention layers. Regarding the inter-frame codec, we propose an implicit motion information mechanism, and an enhancement of the inter-frame predictions by leveraging the high quality information of intra-coded frames. On the learned probability model side, we propose to use the reference frames as additional conditioning information. We used this system as our submitted entry for the 2021 Challenge on Learned Image Compression (CLIC). In our experiments, we show the effectiveness of our system and its components via a set of ablation studies.

关键词： computer vision Image coding conferences Computational modeling Pipelines MIMICs Video compression

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Weighted Multi-Kernel Prediction Network for Burst Image Super-Resolution

Weighted Multi-Kernel Prediction Network for Burst Image Sup...

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

作者： Cho, Wooyeong Son, Sanghyeok Kim, Dae-Shik Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

Burst image super-resolution is an ill-posed problem tha' aims to restore a high-resolution (HR) image from a sequence of low-resolution (LR) burst images. To restore a photo-realistic HR image using their abundant information, it is essential to align each burst of frames containing random hand-held motion. Some kernel prediction networks (KPNs) that are operated without external motion compensation such as optical flow estimation have been applied to burst image processing as implicit image alignment modules. However, the existing methods do not consider the interdependencies among the kernels of different sizes that have a significant effect on each pixel. In this paper, we propose a novel weighted multi-kernel prediction network (WMKPN) that can learn the discriminative features on each pixel for burst image super-resolution. Our experimental results demonstrate that WMKPN improves the visual quality of super-resolved images. To the best of our knowledge, it outperforms the state-of-the-art within kernel prediction methods and multiple frame super-resolution (MFSR) on both the Zurich RAW to RGB and BurstSR datasets.

关键词： Visualization Computational modeling Superresolution Prediction methods Motion compensation Image restoration pattern recognition

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AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks

AsymmNet: Towards ultralight convolution neural networks usi...

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

作者： Yang, Haojin Shen, Zhen Zhao, Yucheng Hasso Plattner Inst Potsdam Germany Alibaba Cloud Hangzhou Peoples R China ByteDance Beijing Peoples R China

ISBN: (纸本)9781665448994

Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications. However, using these models on mobile or embedded devices is difficult due to the limited memory and computation resources. Recently, the inverted residual block becomes the dominating solution for the architecture design of compact CNNs. In this work, we comprehensively investigated the existing design concepts, rethink the functional characteristics of two pointwise convolutions in the inverted residuals. We propose a novel design, called asymmetrical bottlenecks. Precisely, we adjust the first pointwise convolution dimension, enrich the information flow by feature reuse, and migrate saved computations to the second pointwise convolution. Doing so we can further improve the accuracy without increasing the computation overhead. The asymmetrical bottlenecks can be adopted as a dropin replacement for the existing CNN blocks. We can thus create AsymmNet by easily stack those blocks according to proper depth and width conditions. Extensive experiments demonstrate that our proposed block design is more beneficial than the original inverted residual bottlenecks for mobile networks, especially useful for those ultralight CNNs within the regime of <220M MAdds. Code is available at https://***/Spark001/AsymmNet

关键词： Convolutional codes computer vision conferences Computational modeling Neural networks computer architecture pattern recognition

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Instance Segmentation-based Identification of Pelagic Species in Acoustic Backscatter Data

Instance Segmentation-based Identification of Pelagic Specie...

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

作者： Marques, Tunai Porto Cote, Melissa Rezvanifar, Alireza Albu, Alexandra Branzan Ersahin, Kaan Mudge, Todd Gauthier, Stephane Univ Victoria Elect & Comp Engn Victoria BC Canada ASL Environm Sci Victoria BC Canada Fisheries & Oceans Canada Sidney BC Canada

ISBN: (纸本)9781665448994

This paper addresses the automatic identification of pelagic species in acoustic backscatter data. Large quantities of data acquired during underwater acoustic surveys for environmental monitoring and resources management, visualized as echograms, are typically analyzed manually or semi-automatically by marine biologists, which is time-consuming and prone to errors and inter-expert disagreements. In this paper, we propose to detect pelagic species (schools of herring and of juvenile salmon) from echograms with a deep learning (DL) framework based on instance segmentation, allowing us to carefully study the acoustic properties of the targets and to address specific challenges such as close proximity between schools and varying size. Experimental results demonstrate our system's ability to correctly detect pelagic species from echograms and to outperform an existing object detection framework designed for schools of herring in terms of detection performance and computational resources utilization. Our pixel-level detection method has the advantage of generating a precise identification of the pixel groups forming each detection, opening up many possibilities for automatic biological analyses.

关键词： Training Image segmentation Semantics Object detection Standardization Biology pattern recognition

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Wide Receptive Field and Channel Attention Network for JPEG Compressed Image Deblurring

Wide Receptive Field and Channel Attention Network for JPEG ...

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

作者： Lee, Donghyeon Lee, Chulhee Kim, Taesung Samsung Elect Hwaseong South Korea Sunmoon Univ Asan South Korea

ISBN: (纸本)9781665448994

A motion blurred image stored in the joint photographic experts group (JPEG) image compression format contains both motion blur and JPEG artifacts. Therefore, it is very difficult to restore the original image from a blurred and JPEG-compressed image. To address this problem, this paper proposes two methods: a wide receptive field and channel attention network (WRCAN), and JPEG autoencoder loss. First, the WRCAN utilizes a large receptive field and considers the interdependencies among channels of a feature map. Second, the proposed JPEG auto-encoder loss enables the WRCAN to learn prior knowledge of JPFG compression artifacts such that the proposed WRCAN can effectively restore the original image from JPEG-compressed images. The proposed methods are evaluated on the JPEG-compressed REDS dataset by participating in the NTIRE 2021 workshop challenges on Image Deblurring Track 2 JPEG artifacts. The WRCAN trained with the proposed loss ranked third with an output of 29.60dB on the REDS test set, indicating that the proposed methods provide state-of-the-art results. The source codes, model, and data are available at https://***/dhyeonlee/WRCAN-PyTorch.

关键词： computer vision Image coding Convolution conferences Transform coding computer architecture Data models

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Less is More: Pursuing the Visual Turing Test with the Kuleshov Effect

Less is More: Pursuing the Visual Turing Test with the Kules...

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

作者： Olague, Gustavo Olague, Matthieu Jacobo-Lopez, Angel R. Ibarra-Vazquez, Gerardo CICESE Res Ctr Ensenada Baja California Mexico Anahuac Univ Queretaro Queretaro Mexico Autonomous Univ San Luis Potosi San Luis Potosi San Luis Potosi Mexico

ISBN: (纸本)9781665448994

The Turing test centers on the idea that if a computer could trick a human into believing that it was human, then the machine was deemed to be intelligent or indistinguishable from people. Designing a visual Turing test involves recognizing objects and their relationships on images and creating a method to derive new concepts from the visual information. Until now, the proposed visual tests heavily use natural language processing to conduct the questionnaire or storytelling. We deviate from the mainstream, and we propose to reframe the visual Turing test through the Kuleshov effect to avoid written or spoken language. The idea resides on elucidating a method that creates the concept of montage synthetically. Like the first days of cinema, we would like to convey messages with the interpretation of image shots that a machine could decipher while comparing it with those scored by humans. The first implementation of this new test uses images from a psychology study where the circumplex model is applied to rate each image. We consider five deep learning methodologies and eight optimizers, and through semiotics, we derive an emotional state in the computer. The results are promising since we confirm that this version of the visual Turing test is challenging as a new research avenue.

关键词： Deep learning Visualization computer vision Image recognition conferences Psychology Motion pictures

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A Study on the Relative Importance of Convolutional Neural Networks in Visually-Aware Recommender Systems

A Study on the Relative Importance of Convolutional Neural N...

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

作者： Deldjoo, Yashar Di Noia, Tommaso Malitesta, Daniele Merra, Felice Antonio Politecn Bari Bari Italy

ISBN: (纸本)9781665448994

Visually-aware recommender systems (VRSs) enhance the semantics of user-item interactions with visual features extracted from item images when they are available. Traditionally, VRSs leverage the representational power of pre-trained convolutional neural networks (CNNs) to perform the item recommendation task. The adoption of CNNs is mainly attributed to their outstanding performance in representing visual data for supervised learning tasks, such as image classification. Their main drawback is that the learned representation of these networks is not entirely in line with the RS tasks - learning users' preferences. This work aims to provide a better understanding of the representation power of pretrained CNNs commonly adopted by the community when integrated with state-of-the-art VRSs algorithms. In particular, we evaluate the recommendation performance of a suite of VRSs using several pretrained CNNs as the image feature extractors on two datasets from a real-world e-commerce platform. Additionally, we propose a novel qualitative and quantitative evaluation paradigm to assess the visual diversity of recommended items compared to the interacted user's items.

关键词： Visualization conferences Supervised learning Semantics Feature extraction pattern recognition Convolutional neural networks

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ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

ClassSR: A General Framework to Accelerate Super-Resolution ...

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

作者： Kong, Xiangtao Zhao, Hengyuan Qiao, Yu Dong, Chao Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synergy Syst Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

We aim at accelerating super-resolution (SR) networks on large images (2K-8K). The large images are usually decomposed into small sub-images in practical usages. Based on this processing, we found that different image regions have different restoration difficulties and can be processed by networks with different capacities. Intuitively, smooth areas are easier to super-solve than complex textures. To utilize this property, we can adopt appropriate SR networks to process different sub-images after the decomposition. On this basis, we propose a new solution pipeline - ClassSR that combines classification and SR in a unified framework. In particular, it first uses a Class-Module to classify the sub-images into different classes according to restoration difficulties, then applies an SR-Module to perform SR for different classes. The Class-Module is a conventional classification network, while the SR-Module is a network container that consists of the to-be-accelerated SR network and its simplified versions. We further introduce a new classification method with two losses - Class-Loss and Average-Loss to produce the classification results. After joint training, a majority of sub-images will pass through smaller networks, thus the computational cost can be significantly reduced. Experiments show that our ClassSR can help most existing methods (e.g., FSRCNN, CARN, SRResNet, RCAN) save up to 50% FLOPs on DIV8K datasets. This general framework can also be applied in other low-level vision tasks.

关键词： Training computer vision Superresolution Pipelines Containers Image restoration pattern recognition

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GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review

GAN-Based Data Augmentation and Anonymization for Skin-Lesio...

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

作者： Bissoto, Alceu Valle, Eduardo Avila, Sandra Univ Campinas UNICAMP Inst Comp IC Campinas Brazil Univ Campinas UNICAMP RECOD Lab Sch Elect & Comp Engn FEEC Campinas Brazil

ISBN: (纸本)9781665448994

Despite the growing availability of high-quality public datasets, the lack of training samples is still one of the main challenges of deep-learning for skin lesion analysis. Generative Adversarial Networks (GANs) appear as an enticing alternative to alleviate the issue, by synthesizing samples indistinguishable from real images, with a plethora of works employing them for medical applications. Nevertheless, carefully designed experiments for skin-lesion diagnosis with GAN-based data augmentation show favorable results only on out-of-distribution test sets. For GAN-based data anonymization - where the synthetic images replace the real ones - favorable results also only appear for out-of-distribution test sets. Because of the costs and risks associated with GAN usage, those results suggest caution in their adoption for medical applications.

关键词： Training Biomedical equipment computer vision conferences Medical services Generative adversarial networks Skin

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