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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11-20 订阅

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The Casual Conversations v2 Dataset A diverse, large benchmark for measuring fairness and robustness in audio/vision/speech models

The Casual Conversations v2 Dataset A diverse, large benchma...

引用

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

作者： Porgali, Bilal Albiero, Vitor Ryda, Jordan Ferrer, Cristian Canton Hazirbas, Caner Meta AI Menlo Pk CA 94025 USA

ISBN: (纸本)9798350302493

This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regards to 11 attributes that are self-provided or labeled by trained annotators. The dataset includes 26,467 videos of 5,567 unique paid participants, with an average of almost 5 videos per person, recorded in Brazil, India, Indonesia, Mexico, Vietnam, Philippines, and the USA, representing diverse demographic characteristics. The participants agreed for their data to be used in assessing fairness of AI models and provided self-reported age, gender, language/dialect, disability status, physical adornments, physical attributes and geo-location information, while trained annotators labeled apparent skin tone using the Fitzpatrick Skin Type and Monk Skin Tone scales, and voice timbre. Annotators also labeled for different recording setups and per-second activity annotations.

关键词： Large dataset

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Difficulty Estimation with Action Scores for computer vision Tasks

Difficulty Estimation with Action Scores for Computer Vision...

引用

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

作者： Arriaga, Octavio Palacio, Sebastian Valdenegro-Toro, Matias Univ Bremen Bremen Germany German Res Ctr Artificial Intelligence Kaiserslautern Germany Univ Groningen Groningen Netherlands

ISBN: (纸本)9798350302493

As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficulty score based on the accumulated loss per epoch. Our proposed method does not require any modification to the model, neither any external supervision, and it can be easily applied to a wide range of machine learning tasks. We provide results for the tasks of image classification, image segmentation, and object detection. We compare our score against similar metrics and provide theoretical and empirical evidence of their difference. Furthermore, we show applications of our proposed score for detecting incorrect labels, and test for possible biases.

关键词： Object detection

来源：评论

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Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results - PBVS 2023

Multi-modal Aerial View Image Challenge: Translation from Sy...

引用

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

作者： Low, Spencer Nina, Oliver Sappa, Angel D. Blasch, Erik Inkawhich, Nathan Brigham Young Univ Provo UT USA Air Force Res Lab Dayton OH USA ESPOL Polytechn Univ Guayaquil Ecuador Comp Vis Ctr Barcelona Spain Air Force Res Lab Arlington VA USA Air Force Res Lab Rome NY USA

ISBN: (纸本)9798350302493

This paper unveils the discoveries and outcomes of the inaugural iteration of the Multi-modal Aerial View Image Challenge (MAVIC) aimed at image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. To accomplish the task of image translation, the competition utilizes images obtained from both synthetic aperture radar (SAR) and electro-optical (EO) sources. Specifically, the challenge centers on the translation from the SAR modality to the EO modality, an area of research that has garnered attention. The inaugural challenge demonstrates the feasibility of the task. The dataset utilized in this challenge is derived from the UNIfied COincident Optical and Radar for recognition (UNICORN) dataset. We introduce an new version of the UNICORN dataset that is focused on enabling the sensor translation task. Performance evaluation is conducted using a combination of measures to ensure high fidelity and high accuracy translations.

关键词： Synthetic aperture radar

来源：评论

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AFRIFASHION1600: A Contemporary African Fashion Dataset for computer vision

AFRIFASHION1600: A Contemporary African Fashion Dataset for ...

引用

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

作者： Oyewusi, Wuraola Fisayo Adekanmbi, Olubayo Ibejih, Sharon Osakuade, Opeyemi Okoh, Ifeoma Salami, Mary Data Sci Nigeria Lagos Nigeria

ISBN: (纸本)9781665448994

This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and has an image size of 128 x 128. This is a niche dataset that aims to improve visibility, inclusion, and familiarity of African fashion in computer vision ***1600 dataset is available here.

关键词： computer vision conferences Computational modeling Machine learning pattern recognition Task analysis

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A case for using rotation invariant features in state of the art feature matchers

A case for using rotation invariant features in state of the...

引用

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

作者： Bokman, Georg Kahl, Fredrik Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781665487399

The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.

关键词： computer vision conferences Lighting pattern matching

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On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders

On Disentanglement and Mutual Information in Semi-Supervised...

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

作者： Gordon Rodriguez, Elliott Columbia Univ Dept Stat New York NY 10027 USA

ISBN: (纸本)9781665448994

In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.

关键词： computer vision conferences pattern recognition Mutual information

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Quantifying Extrinsic Curvature in Neural Manifolds

Quantifying Extrinsic Curvature in Neural Manifolds

引用

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

作者： Acosta, Francisco Sanborn, Sophia Duc, Khanh Dao Madhav, Manu Miolane, Nina UC Santa Barbara Phys Santa Barbara CA 93106 USA UC Santa Barbara Elect & Comp Engn Santa Barbara CA USA UC Santa Barbara Math Santa Barbara CA USA UC Santa Barbara Santa Barbara CA USA

ISBN: (纸本)9798350302493

The neural manifold hypothesis postulates that the activity of a neural population forms a low-dimensional manifold whose structure reflects that of the encoded task variables. In this work, we combine topological deep generative models and extrinsic Riemannian geometry to introduce a novel approach for studying the structure of neural manifolds. This approach (i) computes an explicit parameterization of the manifolds and (ii) estimates their local extrinsic curvature-hence quantifying their shape within the neural state space. Importantly, we prove that our methodology is invariant with respect to transformations that do not bear meaningful neuroscience information, such as permutation of the order in which neurons are recorded. We show empirically that we correctly estimate the geometry of synthetic manifolds generated from smooth deformations of circles, spheres, and tori, using realistic noise levels. We additionally validate our methodology on simulated and real neural data, and show that we recover geometric structure known to exist in hippocampal place cells. We expect this approach to open new avenues of inquiry into geometric neural correlates of perception and behavior.

关键词： computer vision

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Rethinking the Self-Attention in vision Transformers

Rethinking the Self-Attention in Vision Transformers

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

作者： Kim, Kyungmin Wu, Bichen Dai, Xiaoliang Zhang, Peizhao Yan, Zhicheng Vajda, Peter Kim, Seon Yonsei Univ Seoul South Korea Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665448994

Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on image (ImageNet-1K) and video (Kinetics-400) understanding show we can achieve 95% sparsity on the self-attention maps while maintaining the performance drop to be less than 2 points. This motivates us to rethink the role of self-attention in vision transformer models.

关键词： computer vision conferences Computational modeling pattern recognition

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Learning unbiased classifiers from biased data with meta-learning

Learning unbiased classifiers from biased data with meta-lea...

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

作者： Ragonesi, Ruggero Morerio, Pietro Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis PAVIS Genoa Italy Univ Verona Dept Comp Sci Verona Italy

ISBN: (纸本)9798350302493

It is well known that large deep architectures are powerful models when adequately trained, but may exhibit undesirable behavior leading to confident incorrect predictions, even when evaluated on slightly different test examples. Test data characterized by distribution shifts (from training data distribution), outliers, and adversarial samples are among the types of data affected by this problem. This situation worsens whenever data are biased, meaning that predictions are mostly based on spurious correlations present in the data. Unfortunately, since such correlations occur in the most of data, a model is prevented from correctly generalizing the considered classes. In this work, we tackle this problem from a meta-learning perspective. Considering the dataset as composed of unknown biased and unbiased samples, we first identify these two subsets by a pseudo-labeling algorithm, even if coarsely. Subsequently, we apply a bi-level optimization algorithm in which, in the inner loop, we look for the best parameters guiding the training of the two subsets, while in the outer loop, we train the final model taking benefit from augmented data generated using Mixup. Properly tuning the contributions of biased and unbiased data, together with the regularization introduced by the mixed data has proved to be an effective training strategy to learn unbiased models, showing superior generalization capabilities. Experimental results on synthetically and realistically biased datasets surpass state-of-the-art performance, as compared to existing methods.

关键词： computer vision

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Multi-modal Aerial View Object Classification Challenge Results - PBVS 2023

Multi-modal Aerial View Object Classification Challenge Resu...

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

作者： Low, Spencer Nina, Oliver Sappa, Angel D. Blasch, Erik Inkawhich, Nathan Brigham Young Univ Provo UT 84602 USA Air Force Res Lab Dayton OH USA ESPOL Polytech Univ Ecuador Comp Vis Ctr Guayaquil Ecuador Air Force Res Lab Rome NY USA

ISBN: (纸本)9798350302493

This paper presents the findings and results of the third edition of the Multi-modal Aerial View Object Classification (MAVOC) challenge in a detailed and comprehensive manner. The challenge consists of two tracks. The primary aim of both tracks is to encourage research into building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Participating teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge demonstrated the feasibility of combining both modalities, the 2022 challenge expanded on the capability of multi-modal models. The 2023 challenge introduces a refined version of the UNICORN dataset and demonstrates significant improvements made. The 2023 challenge adopts an updated UNIfied CO-incident Optical and Radar for recognition (UNICORN V2) dataset and competition format. Two tasks are featured: SAR classification and SAR + EO classification. In addition to measuring accuracy of models, we also introduce out-of-distribution measures to encourage model robustness. The majority of this paper is dedicated to discussing the top performing methods and evaluating their performance on our blind test set. It is worth noting that all of the top ten teams outperformed the Resnet-50 baseline. The top team for SAR classification achieved a 173% performance improvement over the baseline, while the top team for SAR + EO classification achieved a 175% improvement.

关键词： Synthetic aperture radar

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