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

共 3320 条记录，以下是241-250 订阅

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Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data

Good at captioning, bad at counting: Benchmarking GPT-4V on ...

引用

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

作者： Zhang, Chenhui Wang, Sherrie MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350365474

Large vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear to what extent capabilities on natural images transfer to Earth observation (EO) data, which are predominantly satellite and aerial images less common in VLM training data. In this work, we propose a comprehensive benchmark to gauge the progress of VLMs toward being useful tools for EO data by assessing their abilities on scene understanding, localization and counting, and change detection. Motivated by real-world applications, our benchmark includes scenarios like urban monitoring, disaster relief, land use, and conservation. We discover that, although state-of-the-art VLMs like GPT-4V possess world knowledge that leads to strong performance on location understanding and image captioning, their poor spatial reasoning limits usefulness on object localization and counting. Our benchmark is publicly available on this website. A full version of this paper can be found here.

关键词： benchmark Earth observation foundation model vision-language model

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Towards CNN map representation and compression for camera relocalisation 31

Towards CNN map representation and compression for camera re...

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

作者： Contreras, Luis Mayol-Cuevas, Walterio Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocalisation results and evaluate the response to different data inputs. We use a CNN map representation and introduce the notion of map compression under this paradigm by using smaller CNN architectures without sacrificing relocalisation performance. We evaluate this approach in a series of publicly available datasets over a number of CNN architectures with different sizes, both in complexity and number of layers. This formulation allows us to improve relocalisation accuracy by increasing the number of training trajectories while maintaining a constant-size CNN.

关键词： Training Task analysis Cameras computer architecture Trajectory Biological neural networks

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All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers

All You Can Embed: Natural Language based Vehicle Retrieval ...

引用

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

作者： Scribano, Carmelo Sapienza, Davide Franchini, Giorgia Verucchi, Micaela Bertogna, Marko Univ Modena & Reggio Emilia Modena Italy Univ Ferrara Ferrara Italy Univ Parma Parma Italy

ISBN: (纸本)9781665448994

Combining Natural Language with vision represents a unique and interesting challenge in the domain of Artificial Intelligence. The AI City Challenge Track 5 for Natural Language-Based Vehicle Retrieval focuses on the problem of combining visual and textual information, applied to a smart-city use case. In this paper, we present All You Can Embed (AYCE), a modular solution to correlate single-vehicle tracking sequences with natural language. The main building blocks of the proposed architecture are (i) BERT to provide an embedding of the textual descriptions, (ii) a convolutional backbone along with a Transformer model to embed the visual information. For the training of the retrieval model, a variation of the Triplet Margin Loss is proposed to learn a distance measure between the visual and language embeddings. The code is publicly available at https://***/cscribano/AYCE_2021.

关键词： Training Visualization Target tracking Natural languages Urban areas computer architecture Loss measurement

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NTIRE 2022 Challenge on Learning the Super-Resolution Space

NTIRE 2022 Challenge on Learning the Super-Resolution Space

引用

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

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Kim, Kang-wook Kim, Younggeun Lee, Jae-young Li, Zechao Pan, Jinshan Shim, Dongseok Song, Ki-Ung Tang, Jinhui Wang, Cong Zhao, Zhihao Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This challenge aims to raise awareness that the super-resolution problem is ill-posed. Since many high-resolution images map to the same low-resolution image, we asked the participants to create methods that sample diverse super-resolution from the space of possible high-resolution images given a low-resolution image. For evaluation, we use the same protocol as introduced in the last year's super-resolution space challenge of NTIRE 2021. We compare the submissions of the participating teams and relate them to the approaches from last year. This challenge contains two tracks: 4x and 8x scale factor. In total, 3 teams competed in the final testing phase.

关键词： computer vision Protocols conferences Superresolution pattern recognition Testing

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Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning

Practical Cross-modal Manifold Alignment for Robotic Grounde...

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

作者： Nguyen, Andre T. Richards, Luke E. Kebe, Gaoussou Youssouf Raff, Edward Darvish, Kasra Ferraro, Frank Matuszek, Cynthia Booz Allen Hamilton Mclean VA 22102 USA Univ Maryland Baltimore Cty Baltimore MD 21228 USA

ISBN: (纸本)9781665448994

We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings by sampling triples of anchor, positive, and negative data points from RGB-depth images and their natural language descriptions. We show that our approach can benefit from, but does not require, post-processing steps such as Pro-crustes analysis, in contrast to some of our baselines which require it for reasonable performance. We demonstrate the effectiveness of our approach on two datasets commonly used to develop robotic-based grounded language learning systems, where our approach outperforms four baselines, including a state-of-the-art approach, across five evaluation metrics.

关键词： Manifolds Measurement Learning systems computer vision conferences Natural languages Robot sensing systems

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Yoga-82: A New Dataset for Fine-grained Classification of Human Poses

Yoga-82: A New Dataset for Fine-grained Classification of Hu...

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

作者： Verma, Manisha Kumawat, Sudhakar Nakashima, Yuta Raman, Shanmuganathan Osaka Univ Osaka Japan Indian Inst Technol Gandhinagar Gandhinagar India

ISBN: (纸本)9781728193601

Human pose estimation is a well-known problem in computer vision to locate joint positions. Existing datasets for learning of poses are observed to be not challenging enough in terms of pose diversity, object occlusion and view points. This makes the pose annotation process relatively simple and restricts the application of the models that have been trained on them. To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82, for large-scale yoga pose recognition with 82 classes. Yoga-82 consists of complex poses where fine annotations may not be possible. To resolve this, we provide hierarchical labels for yoga poses based on the body configuration of the pose. The dataset contains a three-level hierarchy including body positions, variations in body positions, and the actual pose names. We present the classification accuracy of the state-of-the-art convolutional neural network architectures on Yoga-82. We also present several hierarchical variants of DenseNet in order to utilize the hierarchical labels.

关键词： computer vision

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CorrGAN: Input Transformation Technique Against Natural Corruptions

CorrGAN: Input Transformation Technique Against Natural Corr...

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

作者： Haque, Mirazul Budnik, Christof J. Yang, Wei UT Dallas Richardson TX 75080 USA Siemens Corp Technol Princeton NJ USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corruptions like fog, blur, contrast etc can affect the prediction of DNN in an autonomous vehicle. In real time, these corruptions are needed to be detected and also the corrupted inputs are needed to be de-noised to be predicted correctly. In this work, we propose CorrGAN approach, which can generate benign input when a corrupted input is provided. In this framework, we train Generative Adversarial Network (GAN) with novel intermediate output-based loss function. The GAN can denoise the corrupted input and generate benign input. Through experimentation, we show that up to 75.2% of the corrupted misclassified inputs can be classified correctly by DNN using CorrGAN.

关键词： Deep learning computer vision Perturbation methods conferences Neural networks computer architecture Generative adversarial networks

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WiCV 2018: The Fourth Women In computer vision Workshop 31

WiCV 2018: The Fourth Women In Computer Vision Workshop

引用

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

作者： Demir, Ilke Bazazian, Dena Romero, Adriana Sharmanska, Viktoriia Tchapmi, Lyne P. Facebook Menlo Pk CA 94025 USA Comp Vis Ctr Barcelona Spain Facebook AI Res Montreal PQ Canada Imperial Coll London London England Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present WiCV 2018 - Women in computer vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration, and to provide mentorship and give opportunities to female-identifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.

关键词： conferences computer vision Industries Object recognition Engineering profession Collaboration Machine learning

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Improved Noise2Noise Denoising with Limited Data

Improved Noise2Noise Denoising with Limited Data

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

作者： Calvarons, Adria Font Tech Univ Munich Munich Germany

ISBN: (纸本)9781665448994

Deep learning methods have proven to be very effective for the task of image denoising even when clean reference images are not available. In particular, Noise2Noise, which requires pairs of noisy images during the training phase, has been shown to yield results as good as approaches using pairs of noisy and clean images (Noise2Clean). However, the performance of Noise2Noise drops when the amount of training data is reduced, limiting its capability in practical scenarios. In this work, an analysis of the Noise2Noise learning strategy is done using real noise and synthetic datasets. This paper demonstrates, using diverse network architectures and loss functions, that the duplicity of information in the noisy pairs can be exploited to reach increased denoising performance of Noise2Noise. Additionally, the issue of overfitting in Noise2Noise is analyzed, given its relevance when training with limited data, and an interpretable early termination criterion is proposed.

关键词： Training Limiting Image color analysis Noise reduction Training data Network architecture pattern recognition

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An Adversarial Approach for Explaining the Predictions of Deep Neural Networks

An Adversarial Approach for Explaining the Predictions of De...

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

作者： Rahnama, Arash Tseng, Andrew Modzy Vienna VA 22182 USA

ISBN: (纸本)9781665448994

Machine learning models have been successfully applied to a wide range of applications including computer vision, natural language processing, and speech recognition. A successful implementation of these models however, usually relies on deep neural networks (DNNs) which are treated as opaque black-box systems due to their incomprehensible complexity and intricate internal mechanism. In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning. Our approach identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN. Our algorithm has the advantage of being fast, consistent, and easy to implement and interpret. We present our detailed analysis that demonstrates how the behavior of an adversarial attack, given a DNN and a task, stays consistent for any input test data point proving the generality of our approach. Our analysis enables us to produce consistent and efficient explanations. We illustrate the effectiveness of our approach by conducting experiments using a variety of DNNs, tasks, and datasets. Finally, we compare our work with other well-known techniques in the current literature.

关键词： Deep learning computer vision Machine learning algorithms Computational modeling conferences Speech recognition Prediction algorithms

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