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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是4201-4210 订阅

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Generalized Domain Adaptation

Generalized Domain Adaptation

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Mitsuzumi, Yu Irie, Go Ikami, Daiki Shibata, Takashi NTT Corp NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9781665445092

Many variants of unsupervised domain adaptation (UDA) problems have been proposed and solved individually. Its side effect is that a method that works for one variant is often ineffective for or not even applicable to another, which has prevented practical applications. In this paper, we give a general representation of UDA problems, named Generalized Domain Adaptation (GDA). GDA covers the major variants as special cases, which allows us to organize them in a comprehensive framework. Moreover, this generalization leads to a new challenging setting where existing methods fail, such as when domain labels are unknown, and class labels are only partially given to each domain. We propose a novel approach to the new setting. The key to our approach is self-supervised class-destructive learning, which enables the learning of class-invariant representations and domain-adversarial classifiers without using any domain labels. Extensive experiments using three benchmark datasets demonstrate that our method outperforms the state-of-the-art UDA methods in the new setting and that it is competitive in existing UDA variations as well.

关键词： computer vision Benchmark testing pattern recognition

来源：评论

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Architectural Adversarial Robustness: The Case for Deep Pursuit

Architectural Adversarial Robustness: The Case for Deep Purs...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cazenavette, George Murdock, Calvin Lucey, Simon Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665445092

Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by refraining each layer of a feed forward network as an approximate solution to a sparse coding problem. Iterative solutions using basis pursuit are theoretically more stable and have improved adversarial robustness. However, cascading layer-wise pursuit implementations suffer from error accumulation in deeper networks. In contrast, our new method of deep pursuit approximates the activations of all layers as a single global optimization problem, allowing us to consider deepen real-world architectures with skip connections such as residual networks. Experimentally, our approach demonstrates improved robustness to adversarial noise.

关键词： Deep learning Resistance computer vision Sensitivity computer architecture Sparse representation Robustness

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MTN: Forensic Analysis of MP4 Video Files Using Graph Neural Networks

MTN: Forensic Analysis of MP4 Video Files Using Graph Neural...

引用

2023 ieee/cvf conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Xiang, Ziyue Singh Yadav, Amit Kumar Bestagini, Paolo Tubaro, Stefano Delp, Edward J. School of Electrical and Computer Engineering West LafayetteIN United States Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Milano Italy

ISBN: (纸本)9798350302493

MP4 video files are stored using a tree data structure. These trees contain rich information that can be used for forensic analysis. In this paper, we propose MP4 Tree Network (MTN), an approach based on an end-to-end Graph Neural Networks (GNNs) that is used for forensic analysis of MP4 trees. MTN does not use any video pixel data. MTN is trained using Self-Supervised Learning (SSL), which generates semantic-preserving node embeddings for the nodes in an MP4 tree. We also propose a data augmentation technique for MP4 trees, which helps train MTN in data-scarce scenarios. MTN achieves good performance across 3 video forensics tasks on the EVA-7K dataset. We show that MTN can gain more comprehensive understanding about the MP4 trees and is more robust to potential attacks compared to existing methods. © 2023 ieee.

关键词： Trees (mathematics)

来源：评论

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Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning

Semantic-aware Knowledge Distillation for Few-Shot Class-Inc...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cheraghian, Ali Rahman, Shafin Fang, Pengfei Roy, Soumava Kumar Petersson, Lars Harandi, Mehrtash Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia North South Univ Dhaka Bangladesh Monash Univ Melbourne Vic Australia

ISBN: (纸本)9781665445092

Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner. Due to the limited number of examples for training, the techniques developed for standard incremental learning cannot be applied verbatim to FSCIL. In this work, we introduce a distillation algorithm to address the problem of FSCIL and propose to make use of semantic information during training. To this end, we make use of word embeddings as semantic information which is cheap to obtain and which facilitate the distillation process. Furthermore, we propose a method based on an attention mechanism on multiple parallel embeddings of visual data to align visual and semantic vectors, which reduces issues related to catastrophic forgetting. Via experiments on MiniImageNet, CUB200, and CIFAR100 dataset, we establish new state-of-the-art results by outperforming existing approaches.

关键词： Training Visualization computer vision Semantics Training data Power capacitors pattern recognition

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Cross-View Regularization for Domain Adaptive Panoptic Segmentation

Cross-View Regularization for Domain Adaptive Panoptic Segme...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Huang, Jiaxing Guan, Dayan Xiao, Aoran Lu, Shijian Nanyang Technol Univ Sch Comp Sci Engn Singapore Singapore

ISBN: (纸本)9781665445092

Panoptic segmentation unifies semantic segmentation and instance segmentation which has been attracting increasing attention in recent years. However, most existing research was conducted under a supervised learning setup whereas unsupervised domain adaptive panoptic segmentation which is critical in different tasks and applications is largely neglected. We design a domain adaptive panoptic segmentation network that exploits inter-style consistency and inter-task regularization for optimal domain adaptive panoptic segmentation. The inter-style consistency lever-ages semantic invariance across the same image of the different styles which 'fabricates' certain self-supervisions to guide the network to learn domain-invariant features. The inter-task regularization exploits the complementary nature of instance segmentation and semantic segmentation and uses it as a constraint for better feature alignment across domains. Extensive experiments over multiple domain adaptive panoptic segmentation tasks (e.g. synthetic-to-real and real-to-real) show that our proposed network achieves superior segmentation performance as compared with the state-of-the-art.

关键词： Image segmentation computer vision Adaptive systems Semantics Supervised learning pattern recognition Task analysis

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RAFT-3D: Scene Flow using Rigid-Motion Embeddings

RAFT-3D: Scene Flow using Rigid-Motion Embeddings

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Teed, Zachary Deng, Jia Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

We address the problem of scene flow: given a pair of stereo or RGB-D video frames, estimate pixelwise 3D motion. We introduce RAFT-3D, a new deep architecture for scene flow. RAFT-3D is based on the RAFT model developed for optical flow but iteratively updates a dense field of pixelwise SE3 motion instead of 2D motion. A key innovation of RAFT-3D is rigid-motion embeddings, which represent a soft grouping of pixels into rigid objects. Integral to rigid-motion embeddings is Dense-SE3, a differentiable layer that enforces geometric consistency of the embeddings. Experiments show that RAFT-3D achieves state-of-the-art performance. On FlyingThings3D, under the twoview evaluation, we improved the best published accuracy (delta < 0.05) from 34.3% to 83.7%. On KITTI, we achieve an error of 5.77, outperforming the best published method (6.31), despite using no object instance supervision.

关键词： Technological innovation computer vision Three-dimensional displays Deep architecture pattern recognition Optical flow

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CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation

CGA-Net: Category Guided Aggregation for Point Cloud Semanti...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lu, Tao Wang, Limin Wu, Gangshan Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781665445092

Previous point cloud semantic segmentation networks use the same process to aggregate features from neighbors of the same category and different categories. However, the joint area between two objects usually only occupies a small percentage in the whole scene. Thus the networks are well-trained for aggregating features from the same category point while not fully trained on aggregating points of different categories. To address this issue, this paper proposes to utilize different aggregation strategies between the same category and different categories. Specifically, it presents a customized module, termed as Category Guided Aggregation (CGA), where it first identifies whether the neighbors belong to the same category with the center point or not, and then handles the two types of neighbors with two carefully-designed modules. Our CGA presents a general network module and could be leveraged in any existing semantic segmentation network. Experiments on three different backbones demonstrate the effectiveness of our method.

关键词： computer vision Aggregates Semantics computer architecture pattern recognition

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Mining Better Samples for Contrastive Learning of Temporal Correspondence

Mining Better Samples for Contrastive Learning of Temporal C...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Jeon, Sangryul Min, Dongbo Kim, Seungryong Sohn, Kwanghoon Yonsei Univ Seoul South Korea Ewha Womans Univ Seoul South Korea Korea Univ Seoul South Korea

ISBN: (纸本)9781665445092

We present a novel framework for contrastive learning of pixel-level representation using only unlabeled video. Without the need of ground-truth annotation, our method is capable of collecting well-defined positive correspondences by measuring their confidences and well-defined negative ones by appropriately adjusting their hardness during training. This allows us to suppress the adverse impact of ambiguous matches and prevent a trivial solution from being yielded by too hard or too easy negative samples. To accomplish this, we incorporate three different criteria that ranges from a pixel-level matching confidence to a video-level one into a bottom-up pipeline, and plan a curriculum that is aware of current representation power for the adaptive hardness of negative samples during training. With the proposed method, state-of-the-art performance is attained over the latest approaches on several video label propagation tasks.

关键词： Training computer vision Uncertainty Annotations Pipelines Measurement uncertainty pattern recognition

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Truly shift-invariant convolutional neural networks

Truly shift-invariant convolutional neural networks

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chaman, Anadi Dokmanic, Ivan Univ Illinois Champaign IL 61820 USA Univ Basel Basel Switzerland

ISBN: (纸本)9781665445092

Thanks to the use of convolution and pooling layers, convolutional neural networks were for a long time thought to be shift-invariant. However, recent works have shown that the output of a CNN can change significantly with small shifts in input-a problem caused by the presence of downsampling (stride) layers. The existing solutions rely either on data augmentation or on anti-aliasing, both of which have limitations and neither of which enables perfect shift invariance. Additionally, the gains obtained from these methods do not extend to image patterns not seen during training. To address these challenges, we propose adaptive polyphase sampling (APS), a simple sub-sampling scheme that allows convolutional neural networks to achieve 100% consistency in classification performance under shifts, without any loss in accuracy. With APS, the networks exhibit perfect consistency to shifts even before training, making it the first approach that makes convolutional neural networks truly shift-invariant.

关键词： Training computer vision Adaptive systems Convolution pattern recognition Convolutional neural networks

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A Multiplexed Network for End-to-End, Multilingual OCR

A Multiplexed Network for End-to-End, Multilingual OCR

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Huang, Jing Pang, Guan Kovvuri, Rama Toh, Mandy Liang, Kevin J. Krishnan, Praveen Yin, Xi Hassner, Tal Facebook AI Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

Recent advances in OCR have shown that an end-to-end (E2E) training pipeline that includes both detection and recognition leads to the best results. However, many existing methods focus primarily on Latin-alphabet languages, often even only case-insensitive English characters. In this paper, we propose an E2E approach, Multiplexed Multilingual Mask TextSpotter, that performs script identification at the word level and handles different scripts with different recognition heads, all while maintaining a unified loss that simultaneously optimizes script identification and multiple recognition heads. Experiments show that our method outperforms the single-head model with similar number of parameters in end-to-end recognition tasks, and achieves state-of-the-art results on MLTI7 and MLTI9 joint text detection and script identification benchmarks. We believe that our work is a step towards the end-to-end trainable and scalable multilingual multi-purpose OCR system. Our code and model will be released.

关键词： Multiplexing Training computer vision Text recognition Computational modeling Pipelines Benchmark testing

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