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

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引用

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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BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

BasicVSR: The Search for Essential Components in Video Super...

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

作者： Chan, Kelvin C. K. Wang, Xintao Yu, Ke Dong, Chao Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Tencent PCG Appl Res Ctr Shenzhen Peoples R China Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Chinese Acad Sci SIAT SenseTime Joint Lab Shenzhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Beijing Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.

关键词： computer vision Systematics Superresolution Pipelines Noise reduction computer architecture pattern recognition

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Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection

Beyond Bounding-Box: Convex-hull Feature Adaptation for Orie...

引用

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

作者： Guo, Zonghao Liu, Chang Zhang, Xiaosong Jiao, Jianbin Ji, Xiangyang Ye, Qixiang Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects. In this paper, we propose a convex-hull feature adaptation (CFA) approach for configuring convolutional features in accordance with oriented and densely packed object layouts. CFA is rooted in convex-hull feature representation, which defines a set of dynamically predicted feature points guided by the convex intersection over union (CIoU) to bound the extent of objects. CFA pursues optimal feature assignment by constructing convex-hull sets and dynamically splitting positive or negative convex-hulls. By simultaneously considering overlapping convex-hulls and objects and penalizing convex-hulls shared by multiple objects, CFA alleviates spatial feature aliasing towards optimal feature adaptation. Experiments on DOTA and SKU110K-R datasets show that CFA significantly outperforms the baseline approach, achieving new state-of-the-art detection performance.

关键词： Convolutional codes computer vision Layout Object detection Detectors Benchmark testing Feature extraction

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CAGE: Controllable Articulation GEneration

CAGE: Controllable Articulation GEneration

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conference on computer vision and pattern recognition (CVPR)

作者： Jiayi Liu Hou In Ivan Tam Ali Mahdavi-Amiri Manolis Savva Simon Fraser University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We address the challenge of generating 3D articulated objects in a controllable fashion. Currently, modeling artic-ulated 3D objects is either achieved through laborious manual authoring, or using methods from prior work that are hard to scale and control directly. We leverage the interplay between part shape, connectivity, and motion using a de-noising diffusion-based method with attention modules de-signed to extract correlations between part attributes. Our method takes an object category label and a part connectivity graph as input and generates an object's geometry and motion parameters. The generated objects conform to user-specified constraints on the object category, part shape, and part articulation. Our experiments show that our method outperforms the state-of-the-art in articulated object generation, producing more realistic objects while conforming better to user constraints.

关键词： Geometry computer vision Solid modeling Three-dimensional displays Shape Face recognition Noise reduction

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Exploring Sparsity in Image Super-Resolution for Efficient Inference

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

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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...

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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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Class-Incremental Experience Replay for Continual Learning under Concept Drift

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

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

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

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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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