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检索条件"任意字段=2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013"

共 4491 条记录，以下是131-140 订阅

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Visual Semantic Relatedness Dataset for Image Captioning

Visual Semantic Relatedness Dataset for Image Captioning

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Sabir, Ahmed Moreno-Noguer, Francesc Padró, Lluís Universitat Politècnica de Catalunya TALP Research Center Barcelona Spain CSIC-UPC Institut de Ròbotica i Informàtica Industrial Barcelona Spain

ISBN: (纸本)9798350302493

Modern image captioning system relies heavily on extracting knowledge from images to capture the concept of a static story. In this paper, we propose a textual visual context dataset for captioning, in which the publicly available dataset COCO Captions [30] has been extended with information about the scene (such as objects in the image). Since this information has a textual form, it can be used to leverage any NLP task, such as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach. Our dataset is available at https://***/ahmedssabir/Textual-Visual-Semantic-Dataset. © 2023 ieee.

关键词： computer vision

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Privacy Leakage of Adversarial Training Models in Federated Learning Systems

Privacy Leakage of Adversarial Training Models in Federated ...

引用

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

作者： Zhang, Jingyang Chen, Yiran Li, Hai Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further reveal this unsettling property of AT by designing a novel privacy attack that is practically applicable to the privacy-sensitive Federated Learning (FL) systems. Using our method, the attacker can exploit AT models in the FL system to accurately reconstruct users' private training images even when the training batch size is large. Code is available at https://***/zjysteven/PrivayAttack_AT_FL.

关键词： Training Deep learning Privacy computer vision conferences Neural networks Collaborative work

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Contrastive Learning for Depth Prediction

Contrastive Learning for Depth Prediction

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Fan, Rizhao Poggi, Matteo Mattoccia, Stefano University of Bologna Department of Computer Science and Engineering Italy

ISBN: (纸本)9798350302493

Depth prediction is at the core of several computer vision applications, such as autonomous driving and robotics. It is often formulated as a regression task in which depth values are estimated through network layers. Unfortunately, the distribution of values on depth maps is seldom explored. Therefore, this paper proposes a novel framework combining contrastive learning and depth prediction, allowing us to pay more attention to depth distribution and consequently enabling improvements to the overall estimation process. Purposely, we propose a window-based contrastive learning module, which partitions the feature maps into non-overlapping windows and constructs contrastive loss within each one. Forming and sorting positive and negative pairs, then enlarging the gap between the two in the representation space, constraints depth distribution to fit the feature of the depth map. Experiments on KITTI and NYU datasets demonstrate the effectiveness of our framework. © 2023 ieee.

关键词： Forecasting

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Variational Autoencoders for Generating Hyperspectral Imaging Honey Adulteration Data

Variational Autoencoders for Generating Hyperspectral Imagin...

引用

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

作者： Phillips, Tessa Abdulla, Waleed Univ Auckland Auckland New Zealand

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Honey fraud and adulteration are an increasing concern globally. Hyperspectral imaging and machine learning can detect adulterated honey within a known set of honey, where we have captured data at different sugar concentrations. Previous work in this area has used a minimal number of honey types, as sample preparation and data capture is a time-consuming process. This paper develops a new approach using variational autoencoders (VAEs) for generating adulterated honey data for unseen honey types. The results show that the binary adulteration detector can achieve on average 81.3% accuracy on unseen honey types by adding the generated data to the existing training data. Without including the generated data while training, the classifier can only achieve 44% on unseen honey types.

关键词： Training computer vision conferences Training data Machine learning Detectors pattern recognition

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Robustness and Adaptation to Hidden Factors of Variation

Robustness and Adaptation to Hidden Factors of Variation

引用

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

作者： Paul, William Burlina, Philippe Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We tackle here a specific, still not widely addressed aspect, of AI robustness, which consists of seeking invariance / insensitivity of model performance to hidden factors of variations in the data. Towards this end, we employ a two step strategy that a) does unsupervised discovery, via generative models, of sensitive factors that cause models to under-perform, and b) intervenes models to make their performance invariant to these sensitive factors' influence. We consider 3 separate interventions for robustness, including: data augmentation, semantic consistency, and adversarial alignment. We evaluate our method using metrics that measure trade offs between invariance (insensitivity) and overall performance (utility) and show the benefits of our method for 3 settings (unsupervised, semi-supervised and generalization).

关键词： Measurement computer vision conferences Semantics Robustness Data models pattern recognition

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MV-TAL: Mulit-view Temporal Action Localization in Naturalistic Driving

MV-TAL: Mulit-view Temporal Action Localization in Naturalis...

引用

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

作者： Li, Wei Chen, Shimin Gu, Jianyang Wang, Ning Chen, Chen Guo, Yandong OPPO Res Inst Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human risky behavior in driving is an important visual recognition problem. In this paper, we propose a multi-view temporal action localization system based on the grayscale video to achieve action recognition in naturalistic driving. Specifically, we adopted SwinTransformer as feature extractor, and a single framework to detect boundary and class at the same time. Also, we improve multiple loss function for explicit constraints of embedded feature distributions. Our proposed framework achieves the overall F1 -score of 0.3154 on A2 dataset.

关键词： Location awareness Measurement Visualization computer vision conferences Aggregates Gray-scale

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User-Guided Variable Rate Learned Image Compression

User-Guided Variable Rate Learned Image Compression

引用

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

作者： Gupta, Rushil Suryateja, B., V Kapoor, Nikhil Jaiswal, Rajat Nangi, Sharmila Kulkarni, Kuldeep Adobe Res Bengaluru India Indian Inst Technol Delhi Delhi India Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a learning-based image compression method that achieves any arbitrary input bitrate via user-guided bit allocation to preferred regions. We verify our hypothesis of incorporating user guidance for bitrate control by experimenting with alternatives that do not have any guidance. We conduct extensive evaluation on CelebA-HQ and CityScapes dataset using standard quantitative metrics and human studies showing that our single model for multiple bitrates achieves similar or better performance as compared to previous learned image compression methods that require re-training for each new bitrate.

关键词： Measurement computer vision Image coding conferences Computational modeling Bit rate pattern recognition

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vision DiffMask: Faithful Interpretation of vision Transformers with Differentiable Patch Masking

Vision DiffMask: Faithful Interpretation of Vision Transform...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Nalmpantis, Angelos Panagiotopoulos, Apostolos Gkountouras, John Papakostas, Konstantinos Aziz, Wilker University of Amsterdam Netherlands

ISBN: (纸本)9798350302493

The lack of interpretability of the vision Transformer may hinder its use in critical real-world applications despite its effectiveness. To overcome this issue, we propose a post-hoc interpretability method called vision DiffMask, which uses the activations of the model's hidden layers to predict the relevant parts of the input that contribute to its final predictions. Our approach uses a gating mechanism to identify the minimal subset of the original input that preserves the predicted distribution over classes. We demonstrate the faithfulness of our method, by introducing a faithfulness task, and comparing it to other state-of-the-art attribution methods on CIFAR-10 and ImageNet-1K, achieving compelling results. To aid reproducibility and further extension of our work, we open source our implementation here. © 2023 ieee.

关键词： computer vision

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SCVRL: Shuffled Contrastive Video Representation Learning

SCVRL: Shuffled Contrastive Video Representation Learning

引用

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

作者： Dorkenwald, Michael Xiao, Fanyi Brattoli, Biagio Tighe, Joseph Modolo, Davide Heidelberg Univ Heidelberg Germany AWS AI Labs Palo Alto CA USA AWS Palo Alto CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCVRL is capable of learning both semantic and motion patterns. For that, we reformulate the popular shuffling pretext task within a modern contrastive learning paradigm. We show that our transformer-based network has a natural capacity to learn motion in self-supervised settings and achieves strong performance, outperforming CVRL on four benchmarks.

关键词： Representation learning Visualization Semantics Self-supervised learning Benchmark testing Transformers pattern recognition

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Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation

Continual Domain Adaptation through Pruning-aided Domain-spe...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Prasanna, B. Sanyal, Sunandini Babu, R. Venkatesh Indian Institute of Science Vision and Ai Lab Bengaluru India

ISBN: (纸本)9798350302493

In this paper, we propose to develop a method to address unsupervised domain adaptation (UDA) in a practical setting of continual learning (CL). The goal is to update the model on continually changing domains while preserving domain-specific knowledge to prevent catastrophic forgetting of past-seen domains. To this end, we build a framework for preserving domain-specific features utilizing the inherent model capacity via pruning. We also perform effective inference using a novel batch-norm based metric to predict the final model parameters to be used accurately. Our approach achieves not only state-of-the-art performance but also prevents catastrophic forgetting of past domains significantly. Our code is made publicly available. 1. © 2023 ieee.

关键词： Domain Knowledge

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