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Exploring Racial Bias within Face recognition via per-subject Adversarially-Enabled Data Augmentation

Exploring Racial Bias within Face Recognition via per-subjec...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Seyma Yucer Samet Akçay Noura Al-Moubayed Toby P. Breckon Department of Computer Science Durham University Durham UK

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Whilst face recognition applications are becoming increasingly prevalent within our daily lives, leading approaches in the field still suffer from performance bias to the detriment of some racial profiles within society. In this study, we propose a novel adversarial derived data augmentation methodology that aims to enable dataset balance at a per-subject level via the use of image-to-image transformation for the transfer of sensitive racial characteristic facial features. Our aim is to automatically construct a synthesised dataset by transforming facial images across varying racial domains, while still preserving identity-related features, such that racially dependant features subsequently become irrelevant within the determination of subject identity. We construct our experiments on three significant face recognition variants: Softmax, CosFace and ArcFace loss over a common convolutional neural network backbone. In a side-by-side comparison, we show the positive impact our proposed technique can have on the recognition performance for (racial) minority groups within an originally imbalanced training dataset by reducing the per-race variance in performance.

关键词： Face recognition Face Training Transforms Machine learning Machine learning algorithms Mutual information

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TTNet: Real-time temporal and spatial video analysis of table tennis

TTNet: Real-time temporal and spatial video analysis of tabl...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Roman Voeikov Nikolay Falaleev Ruslan Baikulov OSAI

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We present a neural network TTNet aimed at real-time processing of high-resolution table tennis videos, providing both temporal (events spotting) and spatial (ball detection and semantic segmentation) data. This approach gives core information for reasoning score updates by an auto-referee *** also publish a multi-task dataset OpenTTGames with videos of table tennis games in 120 fps labeled with events, semantic segmentation masks, and ball coordinates for evaluation of multi-task approaches, primarily oriented on spotting of quick events and small objects tracking. TTNet demonstrated 97.0% accuracy in game events spotting along with 2 pixels RMSE in ball detection with 97.5% accuracy on the test part of the presented *** proposed network allows the processing of downscaled full HD videos with inference time below 6 ms per input tensor on a machine with a single consumer-grade GPU. Thus, we are contributing to the development of real-time multi-task deep learning applications and presenting approach, which is potentially capable of substituting manual data collection by sports scouts, providing support for referees' decision-making, and gathering extra information about the game process.

关键词： Games Task analysis Streaming media Semantics computer architecture Real-time systems

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Are CNN predictions based on reasonable evidence? 32

Are CNN predictions based on reasonable evidence?

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Bargal, Sarah Adel Zunino, Andrea Petsiuk, Vitali Zhang, Jianming Saenko, Kate Murino, Vittorio Sclaroff, Stan Department of Computer Science Boston University Istituto Italiano di Tecnologia Adobe Research Computer Science Department Università di Verona

ISBN: (纸本)9781728125060

We propose Guided Zoom, an approach that utilizes spatial grounding to make more informed predictions. It does so by making sure the model has "the right reasons" for a prediction, being defined as reasons that are coherent with those used to make similar correct decisions at training time. The reason/evidence upon which a deep neural network makes a prediction is defined to be the spatial grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom questions how reasonable the evidence used to make a prediction is. We show that Guided Zoom results in the refinement of a model’s classification accuracy on two fine-grained classification datasets. © 2019 ieee computer Society. All rights reserved.

关键词： Forecasting

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Improving In-field Cassava Whitefly Pest Surveillance with Machine Learning

Improving In-field Cassava Whitefly Pest Surveillance with M...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jeremy Francis Tusubira Solomon Nsumba Flavia Ninsiima Benjamin Akera Guy Acellam Joyce Nakatumba Ernest Mwebaze John Quinn Tonny Oyana Artificial Intelligence Lab Makerere University Google Research Geospatial Data and Computational Intelligence Lab Makerere University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Whiteflies are the major vector responsible for the transmission of cassava related diseases in tropical environments, and knowing the numbers of whiteflies is key in detecting and identifying their spread and prevention. However, the current approach for counting whiteflies is a simple visual inspection, where a cassava leaf is turned upside down to reveal the underside where the whiteflies reside to enable a manual count. Repeated across many cassava farms, this task is quite tedious and time-consuming. In this paper, we propose a method to automatically count white- flies using computer vision techniques. To implement this approach, we collected images of infested cassava leaves and trained a computer vision detector using Haar Cascade and Deep Learning techniques. The two techniques were used to identify the pest in images and return a count. Our results show that this novel method produces a white- fly count with high precision. This method could be applied to similar object detection scenarios similar to the whitefly problem with minor adjustments.

关键词： Machine learning Training Task analysis Object detection computer vision Diseases

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CSPNet: A New Backbone that can Enhance Learning Capability of CNN

CSPNet: A New Backbone that can Enhance Learning Capability ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Chien-Yao Wang Hong-Yuan Mark Liao Yueh-Hua Wu Ping-Yang Chen Jun-Wei Hsieh I-Hau Yeh Institute of Information Science Academia Sinica Taiwan Department of Computer Science National Chiao Tung University Taiwan College of Artificial Intelligence and Green Energy National Chiao Tung University Taiwan Elan Microelectronics Corporation Taiwan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP 50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on ResNet, ResNeXt, and DenseNet.

关键词： Object detection computer architecture Detectors Mathematical model Computational modeling computer vision computer science

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Diagram Image Retrieval and Analysis: Challenges and Opportunities

Diagram Image Retrieval and Analysis: Challenges and Opportu...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Liping Yang Ming Gong Vijayan K. Asari University of New Mexico Albuquerque NM USA University of Dayton Dayton OH USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep learning has achieved significant advances for tasks such as image classification, segmentation, and retrieval; this advance has not yet been realized on scientific and technical drawing images. Research for technical diagram image analysis and retrieval retain much less well developed compared to natural images; one major reason is that the dominant features in scientific diagram images are shape and topology, no color and intensity features, which are essential in retrieval and analysis of natural images. One important purpose of this review, along with some challenges and opportunities, is to draw the attention of researchers and practitioners in the computer vision community to the strong needs of advancing research for diagram image retrieval and analysis, beyond the current focus on natural images, in order to move machine vision closer to artificial general intelligence. This paper investigates recent research on diagram image retrieval and analysis, with an emphasis on methods using content-based image retrieval (CBIR), textures, shapes, topology and geometry. Based on our systematic review of key research on diagram image retrieval and analysis, we then demonstrate and discuss some of the main technical challenges to be overcome for diagram image retrieval and analysis, and point out future research opportunities from technical and application perspectives.

关键词： Shape Image retrieval Patents Topology computer vision Histograms Image color analysis

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Quality Guided Sketch-to-Photo Image Synthesis

Quality Guided Sketch-to-Photo Image Synthesis

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Uche Osahor Hadi Kazemi Ali Dabouei Nasser Nasrabadi West Virginia University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Facial sketches drawn by artists are widely used for visual identification applications and mostly by law enforcement agencies, but the quality of these sketches depend on the ability of the artist to clearly replicate all the key facial features that could aid in capturing the true identity of a subject. Recent works have attempted to synthesize these sketches into plausible visual images to improve visual recognition and identification. However, synthesizing photo-realistic images from sketches proves to be an even more challenging task, especially for sensitive applications such as suspect identification. In this work, we propose a novel approach that adopts a generative adversarial network that synthesizes a single sketch into multiple synthetic images with unique attributes like hair color, sex, etc. We incorporate a hybrid discriminator which performs attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network and an identity preserving network that maintains the identity of the synthesised image throughout the training process. Our approach is aimed at improving the visual appeal of the synthesised images while incorporating multiple attribute assignment to the generator without compromising the identity of the synthesised image. We synthesised sketches using XDOG filter for the CelebA, WVU Multi-modal and CelebA-HQ datasets and from an auxiliary generator trained on sketches from CUHK, IIT-D and FERET datasets. Our results are impressive compared to current state of the art.

关键词： conferences computer vision pattern recognition

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How Important Is Each Dermoscopy Image?

How Important Is Each Dermoscopy Image?

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Catarina Barata Carlos Santiago Institute for Systems and Robotics Lisbon Portugal

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep neural networks (DNNs) have revolutionized the field of dermoscopy image analysis. Systems based on DNNs are able to achieve impressive diagnostic performances, even outperforming experienced dermatologists. However, DNNs strongly rely on the quantity and quality of the training data. Real world data sets, including those related to dermoscopy, are often severely imbalanced and of reduced dimensions. Thus, models trained on these data sets typically become biased and fail to generalize well to new images. Sample weighting strategies have been proposed to overcome the previous limitations with promising results. Nonetheless, they have been poorly investigated in the context of dermoscopy image analysis. This paper addresses this issue through the extensive comparison of several sample weighting methods, namely class balance and curriculum learning. The results show that each sample weighting strategy influences the performance of the model in different ways, with most finding a compromise between correctly classifying the most common classes or biasing the model towards the less represented classes. Furthermore, the features learned by each model differ significantly, depending on the training strategy.

关键词： Training Lesions Skin Task analysis Neural networks computer architecture Image analysis

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Top-Down Networks: A coarse-to-fine reimagination of CNNs

Top-Down Networks: A coarse-to-fine reimagination of CNNs

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ioannis Lelekas Nergis Tomen Silvia L. Pintea Jan C. van Gemert Computer Vision Lab Delft University of Technology Netherlands

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli. On the contrary, CNNs employ a fine-to-coarse processing, moving from local, edge-detecting filters to more global ones extracting abstract representations of the input. In this paper we reverse the feature extraction part of standard bottom-up architectures and turn them upside-down: We propose top-down networks. Our proposed coarse-to-fine pathway, by blurring higher frequency information and restoring it only at later stages, offers a line of defence against adversarial attacks that introduce high frequency noise. Moreover, since we increase image resolution with depth, the high resolution of the feature map in the final convolutional layer contributes to the explainability of the network's decision making process. This favors object-driven decisions over context driven ones, and thus provides better localized class activation maps. This paper offers empirical evidence for the applicability of the top-down resolution processing to various existing architectures on multiple visual tasks.

关键词： Feature extraction Spatial resolution computer architecture Merging Task analysis Visualization Robustness

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Bilinear Parameterization For Differentiable Rank-Regularization

Bilinear Parameterization For Differentiable Rank-Regulariza...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Marcus Valtonen Örnhag Carl Olsson Anders Heyden Centre for Mathematical Sciences Lund University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Low rank approximation is a commonly occurring problem in many computer vision and machine learning applications. There are two common ways of optimizing the resulting models. Either the set of matrices with a given rank can be explicitly parametrized using a bilinear factorization, or low rank can be implicitly enforced using regularization terms penalizing non-zero singular values. While the former approach results in differentiable problems that can be efficiently optimized using local quadratic approximation, the latter is typically not differentiable (sometimes even discontinuous) and requires first order subgradient or splitting methods. It is well known that gradient based methods exhibit slow convergence for ill-conditioned problems. In this paper we show how many non-differentiable regularization methods can be reformulated into smooth objectives using bilinear parameterization. This allows us to use standard second order methods, such as Levenberg- Marquardt (LM) and Variable Projection (VarPro), to achieve accurate solutions for ill-conditioned cases. We show on several real and synthetic experiments that our second order formulation converges to substantially more accurate solutions than competing state-of-the-art methods. 1

关键词： Convergence computer vision Structure from motion Robustness Optimization Standards Deformable models

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