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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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QFabric: Multi-Task Change Detection Dataset

QFabric: Multi-Task Change Detection Dataset

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

作者： Verma, Sagar Panigrahi, Akash Gupta, Siddharth Univ Paris Saclay INRIA Cent Supelec Ctr Vis Numer Paris France Granular AI Somerville NJ USA

ISBN: (纸本)9781665448994

Detecting change through multi-image, multi-date remote sensing is essential to developing and understanding of global conditions. Despite recent advancements in remote sensing realized through deep learning, novel methods for accurate multi-image change detection remain unrealized. Recently, several promising methods have been proposed to address this topic, but a paucity of publicly available data limits the methods that can be assessed. In particular, there exists limited work on categorizing the nature and status of change across an observation period. This paper introduces the first labeled dataset available for such a task. We present an open-source change detection dataset, termed QFabric, with 450,000 change polygons annotated across 504 locations in 100 different cities covering a wide range of geographies and urban fabrics. QFabric is a temporal multi-task dataset with 6 change types and 9 change status classes. The geography and environment metadata around each polygon provides context that can be leveraged to build robust deep neural networks. We apply multiple benchmarks on our dataset for change detection, change type and status classification tasks. Project page: https://***/qfabric

关键词： Deep learning Urban areas Predictive models Benchmark testing Metadata Fabrics pattern recognition

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An Empirical Study of Vehicle Re-Identification on the AI City Challenge

An Empirical Study of Vehicle Re-Identification on the AI Ci...

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

作者： Luo, Hao Chen, Weihua Xu, Xianzhe Gu, Jianyang Zhang, Yuqi Liu, Chong Jiang, Yiqi He, Shuting Wang, Fan Li, Hao Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four points, i.e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge. (1) Both cropping training data and using synthetic data can help the model learn more discriminative features. (2) Since there is a new scenario in the test set that dose not appear in the training set, UDA methods perform well in the challenge. (3) Post-processing techniques including re-ranking, image-to-track retrieval, inter-camera fusion, etc, significantly improve final performance. (4) We ensemble CNN-based models and transformer-based models which provide different representation diversity. With aforementioned techniques, our method finally achieves 0.7445 mAP score, yielding the first place in the competition. Codes are available at https://***/michuanhaohao/AICITY2021_Track2_DMT.

关键词： Training computer vision conferences Computational modeling Urban areas Training data Data models

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Learning Contextual Causality between Daily Events from Time-consecutive Images

Learning Contextual Causality between Daily Events from Time...

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

作者： Zhang, Hongming Huo, Yintong Zhao, Xinran Song, Yangqiu Roth, Dan HKUST Hong Kong Peoples R China CUHK Hong Kong Peoples R China UPenn Philadelphia PA 19104 USA

ISBN: (纸本)9781665448994

Conventional textual-based causal knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causal knowledge records (e.g., ConceptNet) typically do not consider the context. In this paper, we move out of the textual domain to explore a more scalable way of acquiring causal knowledge and investigate the possibility of learning contextual causality from the visual signal. Specifically, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representations, it is possible to discover meaningful causal knowledge from the videos. Further analysis also shows that the contextual property of causal relations indeed exists and considering the contextual property can help better predict the causal relation between events. The Vis-Causal dataset and experiment code are available at https://***/HKUST-KnowComp/Vis_Causal.

关键词： Training Visualization computer vision Annotations Knowledge acquisition conferences 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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Ternary Feature Masks: zero-forgetting for task-incremental learning

Ternary Feature Masks: zero-forgetting for task-incremental ...

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

作者： Masana, Marc Tuytelaars, Tinne Van de Weijer, Joost Comp Vis Ctr Barcelona Spain Katholieke Univ Leuven ESAT PSI Leuven Belgium

ISBN: (纸本)9781665448994

We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue - and show experimentally - that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters for each new task;with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches.

关键词： computer vision Scalability conferences Encoding pattern recognition Computational efficiency Task analysis

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Learning A Cascaded Non-Local Residual Network for Super-resolving Blurry Images

Learning A Cascaded Non-Local Residual Network for Super-res...

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

作者： Bai, Haoran Cheng, Songsheng Tang, Jinhui Pan, Jinshan Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665448994

Deblurring low-resolution images is quite challenging as blur exists in the images and the resolution of the images is low. Existing deblurring methods usually require high-resolution input while the super-resolution methods usually assume that the blur is known or small. Simply applying the deblurring and super-resolution does not solve this problem well. In this paper, we develop an effective cascaded non-local residual network which cascades the deblurring module and super-resolution module to estimate latent high-resolution images from blurry low-resolution ones. The network first uses the deblurring module to generate intermediate clear features and then develops a non-local residual network (NLRN) as the super-resolution module to generate clear high-resolution images from the intermediate clear features. To better constrain the network and reduce the training difficulty, we develop an effective constraint based on image gradients for edge preservation and adopt the progressive upsampling mechanism. We train the proposed network in an end-to-end manner. Both quantitative and qualitative results on the benchmarks demonstrate the effectiveness of the proposed method. Moreover, the proposed method achieves top-3 performance on the low-resolution track of the NTIRE 2021 Image Deblurring Challenge.

关键词： Training computer vision Image edge detection conferences Superresolution Benchmark testing Image restoration

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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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Adversarial Threats to DeepFake Detection: A Practical Perspective

Adversarial Threats to DeepFake Detection: A Practical Persp...

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

作者： Neekhara, Paarth Dolhansky, Brian Bitton, Joanna Ferrer, Cristian Canton Univ Calif San Diego La Jolla CA 92093 USA Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665448994

Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals. Therefore, detecting DeepFakes is crucial to increase the credibility of social media platforms and other media sharing web sites. State-of-the art DeepFake detection techniques rely on neural network based classification models which are known to be vulnerable to adversarial examples. In this work, we study the vulnerabilities of state-of-the-art DeepFake detection methods from a practical stand point. We perform adversarial attacks on DeepFake detectors in a black box setting where the adversary does not have complete knowledge of the classification models. We study the extent to which adversarial perturbations transfer across different models and propose techniques to improve the transferability of adversarial examples. We also create more accessible attacks using Universal Adversarial Perturbations which pose a very feasible attack scenario since they can be easily shared amongst attackers. We perform our evaluations on the winning entries of the DeepFake Detection Challenge (DFDC) and demonstrate that they can be easily bypassed in a practical attack scenario by designing transferable and accessible adversarial attacks.(1)

关键词： Social networking (online) Perturbation methods Neural networks Multimedia Web sites Detectors pattern recognition Security

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CASSOD-Net: Cascaded and Separable Structures of Dilated Convolution for Embedded vision Systems and Applications

CASSOD-Net: Cascaded and Separable Structures of Dilated Con...

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

作者： Chen, Tse-Wei Wang, Deyu Tao, Wei Wen, Dongchao Yin, Lingxiao Ito, Tadayuki Osa, Kinya Kato, Masami Canon Inc Device Technol Dev Headquarters Tokyo Japan Canon Innovat Solut Beijing Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665448994

The field of view (FOV) of convolutional neural networks is highly related to the accuracy of inference. Dilated convolutions are known as an effective solution to the problems which require large FOVs. However, for general-purpose hardware or dedicated hardware, it usually takes extra time to handle dilated convolutions compared with standard convolutions. In this paper, we propose a network module, Cascaded and Separable Structure of Dilated (CASSOD) Convolution, and a special hardware system to handle the CASSOD networks efficiently. A CASSOD-Net includes multiple cascaded 2 x 2 dilated filters, which can be used to replace the traditional 3x3 dilated filters without decreasing the accuracy of inference. Two example applications, face detection and image segmentation, are tested with dilated convolutions and the proposed CASSOD modules. The new network for face detection achieves higher accuracy than the previous work with only 47% of filter weights in the dilated convolution layers of the context module. Moreover, the proposed hardware system can accelerate the computations of dilated convolutions, and it is 2.78 times faster than traditional hardware systems when the filter size is 3 x 3.

关键词： Image segmentation Machine vision conferences Hardware pattern recognition Face detection Convolutional neural networks

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