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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Class-Incremental Experience Replay for Continual Learning under Concept Drift

Class-Incremental Experience Replay for Continual Learning u...

引用

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

作者： Korycki, Lukasz Krawczyk, Bartosz Virginia Commonwealth Univ Dept Comp Sci Richmond VA 23284 USA

ISBN: (纸本)9781665448994

Modern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning and data stream mining. Continual learning focuses on accumulating knowledge and avoiding forgetting, assuming information once learned should be stored. Data stream mining focuses on adaptation to concept drift and discarding outdated information, assuming that only the most recent data is relevant. While these two areas are mainly being developed in separation, they offer complementary views on the problem of learning from dynamic data. There is a need for unifying them, by offering architectures capable of both learning and storing new information, as well as revisiting and adapting to changes in previously seen concepts. We propose a novel continual learning approach that can handle both tasks. Our experience replay method is fueled by a centroid-driven memory storing diverse instances of incrementally arriving classes. This is enhanced with a reactive subspace buffer that tracks concept drift occurrences in previously seen classes and adapts clusters accordingly. The proposed architecture is thus capable of both remembering valid and forgetting outdated information, offering a holistic framework for continual learning under concept drift.

关键词： computer vision conferences computer architecture Machine learning Data mining Task analysis

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

作者： Collier, Mark Mustafa, Basil Kokiopoulou, Efi Jenatton, Rodolphe Berent, Jesse Google AI Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Large scale image classification datasets often contain noisy labels. We take a principled probabilistic approach to modelling input-dependent, also known as heteroscedastic, label noise in these datasets. We place a multivariate Nor-mal distributed latent variable on the final hidden layer of a neural network classifier. The covariance matrix of this latent variable, models the aleatoric uncertainty due to label noise. We demonstrate that the learned covariance structure captures known sources of label noise between semantically similar and co-occurring classes. Compared to standard neural network training and other baselines, we show significantly improved accuracy on Imagenet ILSVRC 2012 79.3% (+ 2.6%), Imagenet-21k 47.0% (+ 1.1%) and JFT 64.7% (+ 1.6%). We set a new state-of-the-art result on Webvision 1.0 with 76.6% top-1 accuracy. These datasets range from over 1M to over 300M training examples and from 1k classes to more than 21k classes. Our method is simple to use, and we provide an implementation that is a drop-in replacement for the final fully-connected layer in a deep classifier.

关键词： Training Correlation Uncertainty Neural networks Estimation Probabilistic logic pattern recognition

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Integrated Grading Framework for Histopathological Breast Cancer: Multi-level vision Transformers, Textural Features, and Fusion Probability Network 27th

Integrated Grading Framework for Histopathological Breast C...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Balaha, Hossam Magdy Ali, Khadiga M. Mahmoud, Ali Ghazal, Mohammed El-Baz, Ayman Department of Bioengineering J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Pathology Department Faculty of Medicine Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781032

Breast cancer (BC) remains a significant global health concern, necessitating accurate and efficient diagnostic approaches. In this study, we propose a comprehensive framework that integrates feature extraction, selection, and classification using Support Vector Machines (SVM) along with hyperparameter optimization. Additionally, we employ multi-level vision Transformers (ViTs) for patch classification, aiming to capture both local and global information from histopathological slides. Moreover, we introduce a Fusion Probability Network (FPN) to combine the outputs of SVM and ViTs. Through this multi-faceted approach, we aim to improve diagnostic performance and contribute to more effective BC diagnosis. Sensitivity analyses and ablation studies across various sample sizes confirm the framework’s effectiveness. Results show high accuracy (up to 96.50%), precision (up to 93.33%), recall (up to 93%), specificity (up to 97.67%), F1 score (up to 92.99%), and balanced accuracy (up to 95.33%). Ablation studies highlight the significance of the feature extraction pipeline in enhancing the framework’s effectiveness and robustness, as well as its adaptability to diverse patch morphologies. Overall, our study offers promising avenues for improving BC grading, with potential implications for enhancing clinical decision-making and patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Exploring Sparsity in Image Super-Resolution for Efficient Inference

Exploring Sparsity in Image Super-Resolution for Efficient I...

引用

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

作者： Wang, Longguang Dong, Xiaoyu Wang, Yingqian Ying, Xinyi Lin, Zaiping An, Wei Guo, Yulan Natl Univ Def Technol Changsha Peoples R China Univ Tokyo Tokyo Japan RIKEN AIP Tokyo Japan

ISBN: (纸本)9781665445092

Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since missing details in low-resolution (LR) images mainly exist in regions of edges and textures, less computational resources are required for those flat regions. Therefore, existing CNN-based methods involve redundant computation in flat regions, which increases their computational cost and limits their applications on mobile devices. In this paper, we explore the sparsity in image SR to improve inference efficiency of SR networks. Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation. Within our SMSR, spatial masks learn to identify "important" regions while channel masks learn to mark redundant channels in those "unimportant" regions. Consequently, redundant computation can be accurately localized and skipped while maintaining comparable performance. It is demonstrated that our SMSR achieves state-of-the-art performance with 41%/33%/27% FLOPs being reduced for x2/3/4 SR.

关键词： Performance evaluation computer vision Codes Image edge detection Superresolution Mobile handsets Computational efficiency

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Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency

Weakly Supervised Instance Segmentation for Videos with Temp...

引用

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

作者： Liu, Qing Ramanathan, Vignesh Mahajan, Dhruv Yuille, Alan Yang, Zhenheng Johns Hopkins Univ Baltimore MD 21218 USA Facebook Menlo Pk CA USA

ISBN: (纸本)9781665445092

Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions. We show that these issues can be better addressed by training with weakly labeled videos instead of images. In videos, motion and temporal consistency of predictions across frames provide complementary signals which can help segmentation. We are the first to explore the use of these video signals to tackle weakly supervised instance segmentation. We propose two ways to leverage this information in our model. First, we adapt inter-pixel relation network (IRN) [1] to effectively incorporate motion information during training. Second, we introduce a new MaskConsist module, which addresses the problem of missing object instances by transferring stable predictions between neighboring frames during training. We demonstrate that both approaches together improve the instance segmentation metric AP(50) on video frames of two datasets: Youtube-VIS and Cityscapes by 5% and 3% respectively.

关键词： Training Measurement Image segmentation computer vision Costs Motion segmentation Computational modeling

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PointNetLK Revisited

PointNetLK Revisited

引用

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

作者： Li, Xueqian Pontes, Jhony Kaesemodel Lucey, Simon Argo AI Pittsburgh PA 15222 USA Univ Adelaide Adelaide SA Australia Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied to mismatched conditions that are not well-represented in the training set, such as unseen object categories, different complex scenes, or unknown depth sensors. In these circumstances, it has often been better to rely on classical non-learning methods (e.g., Iterative Closest Point), which have better generalization ability. Hybrid learning methods, that use learning for predicting point correspondences and then a deterministic step for alignment, have offered some respite, but are still limited in their generalization abilities. We revisit a recent innovation-PointNetLK [1]-and show that the inclusion of an analytical Jacobian can exhibit remarkable generalization properties while reaping the inherent fidelity benefits of a learning framework. Our approach not only outperforms the state-of-the-art in mismatched conditions but also produces results competitive with current learning methods when operating on real-world test data close to the training set.

关键词： Training Jacobian matrices Solid modeling Three-dimensional displays Robustness Sensors pattern recognition

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Spatially Consistent Representation Learning

Spatially Consistent Representation Learning

引用

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

作者： Roh, Byungseok Shin, Wuhyun Kim, Ildoo Kim, Sungwoong Kakao Brain Seoul South Korea

ISBN: (纸本)9781665445092

Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation. Moreover, aggressively cropped views used in existing contrastive methods can minimize representation distances between the semantically different regions of a single image. In this paper, we propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. In particular, we devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region according to geometric translations and zooming operations. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements over the image-level supervised pretraining as well as the state-of-the-art self-supervised learning methods.

关键词： Location awareness Learning systems Image segmentation computer vision Codes Object detection Benchmark testing

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KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment

KOALAnet: Blind Super-Resolution using Kernel-Oriented Adapt...

引用

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

作者： Kim, Soo Ye Sim, Hyeonjun Kim, Munchurl Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations. However, natural images contain various types and amounts of blur: some may be due to the inherent degradation characteristics of the camera, but some may even be intentional, for aesthetic purposes (e.g. Bokeh effect). In the case of the latter, it becomes highly difficult for SR methods to disentangle the blur to remove, and that to leave as is. In this paper, we propose a novel blind SR framework based on kernel-oriented adaptive local adjustment (KOALA) of SR features, called KOALAnet, which jointly learns spatially-variant degradation and restoration kernels in order to adapt to the spatially-variant blur characteristics in real images. Our KOALAnet outperforms recent blind SR methods for synthesized LR images obtained with randomized degradations, and we further show that the proposed KOALAnet produces the most natural results for artistic photographs with intentional blur, which are not over-sharpened, by effectively handling images mixed with in-focus and out-of-focus areas.

关键词： Degradation computer vision Codes Filtering Superresolution Cameras Data models

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Content-aware Input Scaling and Deep Learning Computation Offloading for Low-Latency Embedded vision

Content-aware Input Scaling and Deep Learning Computation Of...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Omkar Prabhune Tianen Chen Younghyun Kim Purdue University University of Wisconsin-Madison

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deploying deep learning (DL) models for visual recognition on embedded systems is often constrained by their limited compute power and storage capacity, and has stringent latency and power requirements. As emerging DL applications continue to evolve, they place increasing demands on computational resources that embedded vision systems are unable to provision. One promising solution to overcome these limitations is computation offloading. However, for performance improvements to be realized, it is essential to carefully partition tasks, taking into account both the quality of the data and the communication *** this paper, we introduce a novel framework for content-aware offloading of DL computations, aimed at maximizing quality-of-service while adhering to latency constraints. Our proposed framework involves the embedded vision system/edge device intelligently compressing data in a content-aware manner using a lightweight model and transmitting it to a more powerful server. The framework consists of two key components: offline training for efficient content-aware data scaling and online control that adapts to the network variations in real-time. To illustrate the effectiveness of our approach, we apply it to multiple downstream tasks such as face identification, person keypoint detection, and instance segmentation, showcasing a significant enhancement in the overall quality of results for various applications.

关键词： Deep learning Training Radio frequency Visualization Accuracy Machine vision Face recognition

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Language-guided Image Reflection Separation

Language-guided Image Reflection Separation

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conference on computer vision and pattern recognition (CVPR)

作者： Haofeng Zhong Yuchen Hong Shuchen Weng Jinxiu Liang Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University AI Innovation Center School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper studies the problem of language-guided re-flection separation, which aims at addressing the ill-posed reflection separation problem by introducing language de-scriptions to provide layer content. We propose a unified framework to solve this problem, which leverages the cross-attention mechanism with contrastive learning strategies to construct the correspondence between language descriptions and image layers. A gated network design and a ran-domized training strategy are employed to tackle the rec-ognizable layer ambiguity. The effectiveness of the pro-posed method is validated by the significant performance advantage over existing reflection separation methods on both quantitative and qualitative comparisons.

关键词： Training computer vision Annotations Natural languages Contrastive learning Logic gates Reflection

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