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Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation

Unsupervised Detection of Cancerous Regions in Histology Ima...

引用

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

作者： Stepec, Dejan Skocaj, Danijel Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana 1000 Slovenia XLAB Doo Pot Za Brdom 100 Ljubljana 1000 Slovenia

ISBN: (纸本)9781665448994

Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance and is a widely studied problem in different domains. Due to the nature of anomaly occurrences and underlying generating processes, it is hard to characterize them and obtain labeled data. Obtaining labeled data is especially difficult in biomedical applications, where only trained domain experts can provide labels, which often come in large diversity and complexity. Recently presented approaches for unsupervised detection of visual anomalies approaches omit the need for labeled data and demonstrate promising results in domains, where anomalous samples significantly deviate from the normal appearance. Despite promising results, the performance of such approaches still lags behind supervised approaches and does not provide a one-fits-all solution. In this work, we present an image-to-image translation-based framework that significantly surpasses the performance of existing unsupervised methods and approaches the performance of supervised methods in a challenging domain of cancerous region detection in histology imagery.

关键词： Visualization computer vision Image analysis Histopathology conferences Biomedical measurement pattern recognition

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PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object Detection in Autonomous Driving Systems

PseudoProp: Robust Pseudo-Label Generation for Semi-Supervis...

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

作者： Hu, Shu Liu, Chun-Hao Dutta, Jayanta Chang, Ming-Ching Lyu, Siwei Ramakrishnan, Naveen Univ Buffalo SUNY Buffalo NY USA Bosch Ctr Artificial Intelligence Sunnyvale CA 94085 USA SUNY Albany Albany NY 12222 USA Amazon Seattle WA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised object detection methods are widely used in autonomous driving systems, where only a fraction of objects are labeled. To propagate information from the labeled objects to the unlabeled ones, pseudo-labels for unlabeled objects must be generated. Although pseudo-labels have proven to improve the performance of semisupervised object detection significantly, the applications of image-based methods to video frames result in numerous miss or false detections using such generated pseudo-labels. In this paper, we propose a new approach, PseudoProp, to generate robust pseudo-labels by leveraging motion continuity in video frames. Specifically, PseudoProp uses a novel bidirectional pseudo-label propagation approach to compensate for misdetection. A feature-based fusion technique is also used to suppress inference noise. Extensive experiments on the large-scale Cityscapes dataset demonstrate that our method outperforms the state-of-the-art semi-supervised object detection methods by 7.4% on mAP(75).

关键词： Training computer vision conferences Object detection Predictive models Feature extraction pattern recognition

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An End-to-End vision Transformer Approach for Image Copy Detection

An End-to-End Vision Transformer Approach for Image Copy Det...

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

作者： Lee, Jiahe Steven Hsu, Wynne Lee, Mong Li Natl Univ Singapore Inst Data Sci Singapore Singapore Natl Univ Singapore Ctr Trusted Internet & Community Singapore Singapore

ISBN: (纸本)9798350365474

Image copy detection is one of the pivotal tools to safeguard online information integrity. The challenge lies in determining whether a query image is an edited copy, which necessitates the identification of candidate source images through a retrieval process. The process requires discriminative features comprising of both global descriptors that are designed to be augmentation-invariant and local descriptors that can capture salient foreground objects to assess whether a query image is an edited copy of some source reference image. This work describes an end-to-end solution that leverage a vision Transformer model to learn such discriminative features and perform implicit matching between the query image and the reference image. Experimental results on two benchmark datasets demonstrate that the proposed solution outperforms state-of-the-art methods. Case studies illustrate the effectiveness of our approach in matching reference images from which the query images have been copy-edited.

关键词： image copy detection image retrieval

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Making use of unlabeled data: Comparing strategies for marine animal detection in long-tailed datasets using self-supervised and semi-supervised pre-training

Making use of unlabeled data: Comparing strategies for marin...

引用

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

作者： Sharma, Tarun Cline, Danelle E. Edgington, Duane CALTECH Pasadena CA 91125 USA MBARI Moss Landing CA USA

ISBN: (纸本)9798350365474

This paper discusses strategies for object detection in marine images from a practitioner's perspective working with real-world long-tail distributed datasets with a large amount of additional unlabeled data on hand. The paper discusses the benefits of separating the localization and classification stages, making the case for robustness in localization through the amalgamation of additional datasets inspired by a widely used approach by practitioners in the camera-trap literature. For the classification stage, the paper compares strategies to use additional unlabeled data, comparing supervised, supervised iteratively, self-supervised, and semi-supervised pre-training approaches. Our findings reveal that semi-supervised pre-training, followed by supervised fine-tuning, yields a significantly improved balanced performance across the long-tail distribution, albeit occasionally with a trade-off in overall accuracy. These insights are validated through experiments on two real-world long-tailed underwater datasets collected by the Monterey Bay Aquarium Research Institute (MBARI).

关键词： computer vision conservation technology long-tail long-tailed distribution marine imaging real-world datasets Self-supervised learning semi-supervised learning underwater animal detection unlabeled data

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Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation

Wearable ImageNet: Synthesizing Tileable Textures via Datase...

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

作者： Cazenavette, George Wang, Tongzhou Torralba, Antonio Efros, Alexei A. Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent methods for Dataset Distillation are able to take in a large set of images of a specific class (e.g., from ImageNet) and synthesize a single image, such that a classifier trained on that image could perform similarly to one trained on the original dataset. It was noticed that the resulting "distilled images" are often quite visually pleasing. In this paper, we describe a simple method for generating tileable distilled textures by sampling random crops from a toroidal canvas of synthetic pixels while enforcing that all such crops serve as effective distilled training data. Such distilled textures not only summarize a given image category in a visually interesting way, but also allow for generation of infinite texture patterns suitable for printing on fabric, clothing, etc. This paper might be just the first step in making the ImageNet dataset into a fashion statement.

关键词： Printing computer vision Wearable computers conferences Clothing Crops Training data

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BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains

BalaGAN: Cross-Modal Image Translation Between Imbalanced Do...

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

作者： Patashnik, Or Danon, Dov Zhang, Hao Cohen-Or, Daniel Tel Aviv Univ Tel Aviv Israel Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665448994

State-of-the-art image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity. We introduce a new unsupervised translation network, BalaGAN, specifically designed to tackle the domain imbalance problem. We leverage the latent modalities of the richer domain to turn the image-to-image translation problem, between two imbalanced domains, into a multi-class translation problem, more resembling the style transfer setting. Specifically, we analyze the source domain and learn a decomposition of it into a set of latent modes or classes, without any supervision. This leaves us with a multitude of balanced crossdomain translation tasks, between all pairs of classes, including the target domain. During inference, the trained network takes as input a source image, as well as a reference style image from one of the modes as a condition, and produces an image which resembles the source on the pixel-wise level, but shares the same mode as the reference. We show that employing modalities within the dataset improves the quality of the translated images, and that BalaGAN outperforms strong baselines of both unconditioned and style-transfer-based image-to-image translation methods, in terms of image quality and diversity.

关键词： Training Image quality computer vision conferences pattern recognition Task analysis

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Real-time body motion analysis for dance pattern recognition

Real-time body motion analysis for dance pattern recognition

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2012 IEEE computer Society conference on computer vision and pattern recognition workshops, CVPRW 2012

作者： Kohn, Bernhard Nowakowska, Aneta Belbachir, Ahmed Nabil AIT Austrian Institute of Technology GmbH Donau-City-Str. 1/5. OG 1220 Vienna Austria

ISBN: (纸本)9781467316118

This paper presents an algorithm for real-time body motion analysis for dance pattern recognition by use of a dynamic stereo vision sensor. Dynamic stereo vision sensors asynchronously generate events upon scene dynamics, so that motion activities are on-chip segmented by the sensor. Using this sensor body motion analysis and tracking can be efficiently performed. For dance pattern recognition we use a machine learning method based on the Hidden Markov Model. Emphasis is laid on the analysis of the suitability for use in embedded systems. For testing the algorithm we use a dance choreography consisting of eight different activities and a training set of 430 recorded activities performed by 15 different persons. A cross validation on the data reached an average recognition rate of 94%. © 2012 IEEE.

关键词： Learning systems

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DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring Dataset for Real-World Outdoor Scenarios

DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring ...

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

作者： Bolten, Tobias Pohle-Froehlich, Regina Toennies, Klaus D. Hsch Niederrhein Inst Pattern Recognit Krefeld Germany Univ Magdeburg Dept Simulat & Graph Magdeburg Germany

ISBN: (纸本)9781665448994

Neuromorphic vision sensors are biologically inspired devices which differ fundamentally from well known frame-based sensors. Even though developments in this research area are increasing, applications that rely entirely on event cameras are still relatively rare. This becomes particularly clear when considering real outdoor scenarios apart from laboratory conditions. One obstacle to the development of event-based vision applications in this context may be the lack of labeled datasets for algorithm development and evaluation. Therefore we describe a recording setting of a DVS-based long time monitoring of an urban public area and provide labeled DVS data that also contain effects of environmental outdoor influences recorded in this process. We also describe the processing chain used for label generation, as well as results from a performed denoising benchmark utilizing various spatio-temporal event stream filters. The dataset contains almost 7 hours of real world outdoor event-data with approximate to 47k labeled regions of interest and can be downloaded at http://***/DVS-OUTLAB/

关键词： Privacy Rain Power demand Neuromorphics Noise reduction Pipelines vision sensors

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Poisons that are learned faster are more effective

Poisons that are learned faster are more effective

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

作者： Sandoval-Segura, Pedro Singla, Vasu Fowl, Liam Geiping, Jonas Goldblum, Micah Jacobs, David Goldstein, Tom Univ Maryland College Pk MD 20742 USA New York Univ New York NY USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Imperceptible poisoning attacks on entire datasets have recently been touted as methods for protecting data privacy. However, among a number of defenses preventing the practical use of these techniques, early-stopping stands out as a simple, yet effective defense. To gauge poisons' vulnerability to early-stopping, we benchmark error-minimizing, error-maximizing, and synthetic poisons in terms of peak test accuracy over 100 epochs and make a number of surprising observations. First, we find that poisons that reach a low training loss faster have lower peak test accuracy. Second, we find that a current state-of-the-art error-maximizing poison is 7x less effective when poison training is stopped at epoch 8. Third, we find that stronger, more transferable adversarial attacks do not make stronger poisons. We advocate for evaluating poisons in terms of peak test accuracy.

关键词： Training Data privacy Privacy computer vision Toxicology Correlation Perturbation methods

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MTUNet: Few-shot Image Classification with Visual Explanations

MTUNet: Few-shot Image Classification with Visual Explanatio...

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

作者： Wang, Bowen Li, Liangzhi Verma, Manisha Nakashima, Yuta Kawasaki, Ryo Nagahara, Hajime Osaka Univ Inst Databil Sci IDS Suita Osaka Japan Osaka Univ Grad Sch Med Suita Osaka Japan

ISBN: (纸本)9781665448994

Few-shot learning (FSL) approaches, mostly neural network-based, are assuming that the pre-trained knowledge can be obtained from base (seen) categories and transferred to novel (unseen) categories. However, the black-box nature of neural networks makes it difficult to understand what is actually transferred, which may hamper its application in some risk-sensitive areas. In this paper, we reveal a new way to perform explainable FSL for image classification, using discriminative patterns and pairwise matching. Experimental results prove that the proposed method can achieve satisfactory explainability on two mainstream datasets. Code is available*.

关键词： Knowledge engineering Visualization computer vision conferences Computational modeling Neural networks Benchmark testing

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