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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...

引用

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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Connecting Language and vision for Natural Language-Based Vehicle Retrieval

Connecting Language and Vision for Natural Language-Based Ve...

引用

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

作者： Bai, Shuai Zheng, Zhedong Wang, Xiaohan Lin, Junyang Zhang, Zhu Zhou, Chang Yang, Hongxia Yang, Yi Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Technol Sydney ReLER Lab Sydney NSW Australia Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Vehicle search is one basic task for the efficient traffic management in terms of the AI City. Most existing practices focus on the image-based vehicle matching, including vehicle re-identification and vehicle tracking. In this paper, we apply one new modality, i.e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario. The natural language-based vehicle search poses one new challenge of fine-grained understanding of both vision and language modalities. To connect language and vision, we propose to jointly train the state-of-the-art vision models with the transformer-based language model in an end-to-end manner. Except for the network structure design and the training strategy, several optimization objectives are also revisited in this work. The qualitative and quantitative experiments verify the effectiveness of the proposed method. Our proposed method has achieved the 1st place on the 5th AI City Challenge, yielding competitive performance 18.69% MRR accuracy on the private test set. We hope this work can pave the way for the future study on using language description effectively and efficiently for real-world vehicle retrieval systems. The code will be available at https://***/ShuaiBai623/AIC2021-T5-CLV.

关键词： Training Smart cities computer architecture Search problems Robustness pattern recognition Task analysis

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A Review and Efficient Implementation of Scene Graph Generation Metrics

A Review and Efficient Implementation of Scene Graph Generat...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Julian Lorenz Robin Schön Katja Ludwig Rainer Lienhart University of Augsburg Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Scene graph generation has emerged as a prominent research field in computer vision, witnessing significant advancements in the recent years. However, despite these strides, precise and thorough definitions for the metrics used to evaluate scene graph generation models are lacking. In this paper, we address this gap in the literature by providing a review and precise definition of commonly used metrics in scene graph generation. Our comprehensive examination clarifies the underlying principles of these metrics and can serve as a reference or introduction to scene graph ***, to facilitate the usage of these metrics, we introduce a standalone Python package called SGBench that efficiently implements all defined metrics, ensuring their accessibility to the research community. Additionally, we present a scene graph benchmarking web service, that enables researchers to compare scene graph generation methods and increase visibility of new methods in a central *** of our code can be found under https://***/sgbench/.

关键词： Measurement computer vision Solid modeling Reviews Web services conferences Computational modeling

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NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

NTIRE 2021 Multi-modal Aerial View Object Classification Cha...

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

作者： Liu, Jerrick Inkawhich, Nathan Nina, Oliver Timofte, Radu Duan, Yuru Li, Gongzhe Geng, Xueli Cai, Huanqia Air Force Res Lab Albuquerque NM 87117 USA Univ Illinois Urbana IL 61801 USA Duke Univ Durham NC 27706 USA Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Northwestern Polytech Univ Changan Campus Xian Shaanxi Peoples R China Beihang Univ Beijing Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Xian Peoples R China Dengzhuang South Rd Beijing Peoples R China

ISBN: (纸本)9781665448994

In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO and SAR imagery. Both EO and SAR sensors possess different advantages and drawbacks. The purpose of this competition is to analyze how to use both sets of sensory information in complementary ways. We discuss the top methods submitted for this competition and evaluate their results on our blind test set. Our challenge results show significant improvement of more than 15% accuracy from our current baselines for each track of the competition.

关键词： Image sensors conferences Radar tracking Radar polarimetry Sensors pattern recognition Task analysis

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Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation

Efficient Pre-trained Features and Recurrent Pseudo-Labeling...

引用

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

作者： Zhang, Youshan Davison, Brian D. Lehigh Univ Comp Sci & Engn Bethlehem PA 18015 USA

ISBN: (纸本)9781665448994

Domain adaptation (DA) mitigates the domain shift problem when transferring knowledge from one annotated domain to another similar but different unlabeled domain. However, existing models often utilize one of the ImageNet models as the backbone without exploring others, and fine-tuning or retraining the backbone ImageNet model is also time-consuming. Moreover, pseudo-labeling has been used to improve the performance in the target domain, while how to generate confident pseudo labels and explicitly align domain distributions has not been well addressed. In this paper, we show how to efficiently opt for the best pre-trained features from seventeen well-known ImageNet models in unsupervised DA problems. In addition, we propose a recurrent pseudo-labeling model using the best pre-trained features (termed PRPL) to improve classification performance. To show the effectiveness of PRPL, we evaluate it on three benchmark datasets, Office+Caltech-10, Office31, and Office-Home. Extensive experiments show that our model reduces computation time and boosts the mean accuracy to 98.1%, 92.4%, and 81.2%, respectively, substantially outperforming the state of the art.

关键词： Training Adaptation models computer vision Computational modeling conferences Optimized production technology Benchmark testing

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Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning

Supervised Contrastive Replay: Revisiting the Nearest Class ...

引用

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

作者： Mai, Zheda Li, Ruiwen Kim, Hyunwoo Sanner, Scott Univ Toronto Toronto ON Canada LG AI Res Seoul South Korea

ISBN: (纸本)9781665448994

Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data while mitigating catastrophic forgetting. While memory replay has shown promising results, the recency bias in online learning caused by the commonly used Softmax classifier remains an unsolved challenge. Although the Nearest-Class-Mean (NCM) classifier is significantly undervalued in the CL community, we demonstrate that it is a simple yet effective substitute for the Softmax classifier. It addresses the recency bias and avoids structural changes in the fully-connected layer for new classes. Moreover, we observe considerable and consistent performance gains when replacing the Softmax classifier with the NCM classifier for several state-of-the-art replay methods. To leverage the NCM classifier more effectively, data embeddings belonging to the same class should be clustered and well-separated from those with a different class label. To this end, we contribute Supervised Contrastive Replay (SCR), which explicitly encourages samples from the same class to cluster tightly in embedding space while pushing those of different classes further apart during replay-based training. Overall, we observe that our proposed SCR substantially reduces catastrophic forgetting and outperforms state-of-the-art CL methods by a significant margin on a variety of datasets.

关键词： Training computer vision conferences Performance gain pattern recognition

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Single Image Dehazing Using Bounded Channel Difference Prior

Single Image Dehazing Using Bounded Channel Difference Prior

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

作者： Zhao, Xuan Air To Air Missile Res Inst Luoyang Henan Peoples R China

ISBN: (纸本)9781665448994

The single image dehazing task has made significant progress recently, aiming to recover the contrast and color of the scattered image. Many patch prior based dehazing methods have been explored, and this paper proposes another single image dehazing method by analyzing the prior information of local dehazed patches. With our observation, when the estimated transmission value varies from the ground-truth transmission value to 1, the output value of a metric function decrease correspondingly, which is defined based on the difference maps among three RGB channels of local dehazed patches normalized using global atmospheric light. Under additional bounding, the local transmission value can be estimated accurately. To reduce computation time, the whole image is divided into many small patches, and within each patch, we estimate a transmission value accurately. We further use weighted interpolation and guided filtering to refine the edges and details of the rough transmission map. Finally, we evaluate the proposed method using Fattal's synthetic haze images, SOTS dataset, and a wide variety of real-world haze images. Experiments show that our method outperforms other state-of-the-art dehazing algorithms by a large margin, especially on synthetic noisy haze images.

关键词： Interpolation computer vision Image color analysis Filtering Image edge detection conferences Estimation

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Appearance-based Gaze Estimation using Attention and Difference Mechanism

Appearance-based Gaze Estimation using Attention and Differe...

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

作者： Murthy, L. R. D. Biswas, Pradipta Indian Inst Sci CPDM I3D Lab Bangalore Karnataka India

ISBN: (纸本)9781665448994

Appearance-based gaze estimation problem received wide attention over the past few years. Even though model-based approaches existed earlier, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. In this paper, we proposed two novel techniques to improve gaze estimation accuracy. Our first approach, I2D-Net uses a difference layer to eliminate any common features from left and right eyes of a participant that are not pertinent to gaze estimation task. Our second approach, AGE-Net adapted the idea of attention-mechanism and assigns weights to the features extracted from eye images. I2D-Net performed on par with the existing state-of-the-art approaches while AGE-Net reported state-of-the-art accuracy of 4.09 degrees and 7.44 degrees error on MPIIGaze and RT-Gene datasets respectively. We performed ablation studies to understand the effectiveness of the proposed approaches followed by analysis of gaze error distribution with respect to various factors of MPIIGaze dataset.

关键词： Deep learning computer vision Adaptation models conferences Estimation Lighting Feature extraction

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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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ST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos

ST-Gait++: Leveraging spatio-temporal convolutions for gait-...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Maria Luísa Lima Willams de Lima Costa Estefania Talavera Martínez Veronica Teichrieb Voxar Labs Centro de Informática Universidade Federal de Pernambuco Recife Brazil University of Twente Enschede Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Emotion recognition is relevant for human behaviour understanding, where facial expression and speech recognition have been widely explored by the computer vision community. Literature in the field of behavioural psychology indicates that gait, described as the way a person walks, is an additional indicator of emotions. In this work, we propose a deep framework for emotion recognition through the analysis of gait. More specifically, our model is composed of a sequence of spatial-temporal Graph Convolutional Networks that produce a robust skeleton-based representation for the task of emotion classification. We evaluate our proposed framework on the E-Gait dataset, composed of a total of 2177 samples. The results obtained represent an improvement of ≈ 5% in accuracy compared to the state of the art. In addition, during training we observed a faster convergence of our model compared to the state-of-the-art methodologies.

关键词： Training Emotion recognition computer vision Portable computers Graph convolutional networks conferences Psychology

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