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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22908 条记录，以下是4471-4480 订阅

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Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries

Depth Completion with Twin Surface Extrapolation at Occlusio...

引用

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

作者： Imran, Saif Liu, Xiaoming Morris, Daniel Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665445092

Depth completion starts from a sparse set of known depth values and estimates the unknown depths for the remaining image pixels. Most methods model this as depth interpolation and erroneously interpolate depth pixels into the empty space between spatially distinct objects, resulting in depth-smearing across occlusion boundaries. Here we propose a multi-hypothesis depth representation that explicitly models both foreground and background depths in the difficult occlusion-boundary regions. Our method can be thought of as performing twin-surface extrapolation, rather than interpolation, in these regions. Next our method fuses these extrapolated surfaces into a single depth image leveraging the image data. Key to our method is the use of an asymmetric loss function that operates on a novel twin-surface representation. This enables us to train a network to simultaneously do surface extrapolation and surface fusion. We characterize our loss function and compare with other common losses. Finally, we validate our method on three different datasets;KITTI, an outdoor real-world dataset, NYU2, indoor real-world depth dataset and Virtual KITTI, a photo-realistic synthetic dataset with dense groundtruth, and demonstrate improvement over the state of the art.

关键词： Extrapolation Interpolation computer vision Fuses Estimation Channel estimation Robustness

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Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation

Smoothing the Disentangled Latent Style Space for Unsupervis...

引用

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

作者： Liu, Yahui Sangineto, Enver Chen, Yajing Bao, Linchao Zhang, Haoxian Sebe, Nicu Lepri, Bruno Wang, Wei De Nadai, Marco Univ Trento Trento Italy Tencent AI Lab Shenzhen Peoples R China Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9781665445092

Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequently show abrupt changes in the image appearance during interpolation, and usually perform poorly in interpolations across domains. In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation. Moreover, we propose a novel evaluation metric to properly measure the smoothness of latent style space of I2I translation models. The proposed method can be plugged in existing translation approaches, and our extensive experiments on different datasets show that it can significantly boost the quality of the generated images and the graduality of the interpolations.

关键词： Training Interpolation computer vision Smoothing methods Protocols Computational modeling Semantics

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Automatic Multiple Choice Question Evaluation Using Tesseract OCR and YOLOv8

Automatic Multiple Choice Question Evaluation Using Tesserac...

引用

Artificial Intelligence (CAI), ieee conference on

作者： Saikat Mahmud Kawshik Biswas Api Alam Rifat Al Mamun Rudro Nusrat Jahan Anannya Israt Jahan Mouri Kamruddin Nur Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

This paper presents a novel approach for automating the grading of multiple-choice question (MCQ) answer sheets using computer vision and pattern recognition techniques. The system examines student’s marked answer sheet images by comparing with the question sheet image and answer keys. The computer vision and pattern recognition helps extracting pertinent data such as question number detection, MCQ option detection and the answer markings. The proposed approach reliably produces the output report that displays the students’ correct answers with an accuracy of 0.98 F1 score and 0.99 mAP from any form or unstructured question script. This approach can provide a dependable and effective grading system, reducing manual work and offering prompt feedback to students without any constraints on the answer sheets.

关键词： computer vision Accuracy Optical character recognition Object detection Manuals Reliability Data mining

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Joint Generative and Contrastive Learning for Unsupervised Person Re-identification

Joint Generative and Contrastive Learning for Unsupervised P...

引用

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

作者： Chen, Hao Wang, Yaohui Lagadec, Benoit Dantcheva, Antitza Bremond, Francois INRIA Le Chesnay France Univ Cote Azur Nice France European Syst Integrat Le Cannet France

ISBN: (纸本)9781665445092

Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework. While the GAN provides online data augmentation for contrastive learning, the contrastive module learns view-invariant features for generation. In this context, we propose a mesh-based view generator. Specifically, mesh projections serve as references towards generating novel views of a person. In addition, we propose a view-invariant loss to facilitate contrastive learning between original and generated views. Deviating from previous GAN-based unsupervised ReID methods involving domain adaptation, we do not rely on a labeled source dataset, which makes our method more flexible. Extensive experimental results show that our method significantly outperforms state-of-the-art methods under both, fully unsupervised and unsupervised domain adaptive settings on several large scale ReID datsets.

关键词： Training computer vision Adaptation models Three-dimensional displays Codes Image synthesis Generative adversarial networks

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Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets

Conv-Adapter: Exploring Parameter Efficient Transfer Learnin...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Hao Chen Ran Tao Han Zhang Yidong Wang Xiang Li Wei Ye Jindong Wang Guosheng Hu Marios Savvides Carneige Mellon University Peking University Microsoft Research Asia Oosto

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While parameter efficient tuning (PET) methods have shown great potential with transformer architecture on Natural Language Processing (NLP) tasks, their effectiveness with large-scale ConvNets is still under-studied on computer vision (CV) tasks. This paper proposes Conv-Adapter, a PET module designed for ConvNets. Conv-Adapter is light-weight, domain-transferable, and architecture-agnostic with generalized performance on different tasks. When transferring on downstream tasks, Conv- Adapter learns tasks-specific feature modulation to the intermediate representations of backbones while keeping the pre-trained parameters frozen. By introducing only a tiny amount of learnable parameters, e.g., only 3.5% full finetuning parameters of ResNet50. It can also be applied for transformer-based backbones. Conv-Adapter outperforms previous PET baseline methods and achieves comparable or surpasses the performance of full fine-tuning on 23 classification tasks of various domains. It also presents superior performance on the few-shot classification with an average margin of 3.39%. Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning 1

关键词： computer vision conferences Transfer learning Modulation Transformers Natural language processing Robustness

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Where are they looking in the 3D space?

Where are they looking in the 3D space?

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Nora Horanyi Linfang Zheng Eunji Chong Aleš Leonardis Hyung Jin Chang School of Computer Science University of Birmingham *** Inc.

We propose a novel depth-aware joint attention target estimation framework that estimates the attention target in 3D space. Our goal is to mimic human’s ability to understand where each person is looking in their proximity. In this work, we tackle the previously unexplored problem of utilising a depth prior along with a 3D joint FOV probability map to estimate the joint attention target of people in the scene. We leverage the insight that besides the 2D image content, strong gaze-related constraints exist in the depth order of the scene and different subject-specific attributes. Extensive experiments show that our method outperforms favourably against existing joint attention target estimation methods on the VideoCoAtt benchmark dataset. Despite the proposed framework being designed for joint attention target estimation, we show that it outperforms single attention target estimation methods on both the GazeFollow image and the VideoAttentionTarget video benchmark datasets.

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Visual Concept Connectome (VCC): Open World Concept Discovery and Their Interlayer Connections in Deep Models

Visual Concept Connectome (VCC): Open World Concept Discover...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Matthew Kowal Richard P. Wildes Konstantinos G. Derpanis York University Samsung AI Centre Toronto Vector Institute

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision. We present a new methodology to understanding such vision models, the Visual Concept Con-nectome (VCC), which discovers human interpretable concepts and their interlayer connections in a fully unsuper-vised manner. Our approach simultaneously reveals fine-grained concepts at a layer, connection weightings across all layers and is amendable to global analysis of network structure (e.g. branching pattern of hierarchical concept assemblies). Previous work yielded ways to extract inter-pretable concepts from single layers and examine their im-pact on classification, but did not afford multilayer concept analysis across an entire network architecture. Quantitative and qualitative empirical results show the effectiveness of VCCs in the domain of image classification. Also, we lever-age VCCs for the application of failure mode debugging to reveal where mistakes arise in deep networks.

关键词： Visualization computer vision Computational modeling Debugging Network architecture Nonhomogeneous media pattern recognition

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Reciprocal Landmark Detection and Tracking with Extremely Few Annotations

Reciprocal Landmark Detection and Tracking with Extremely Fe...

引用

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

作者： Lin, Jianzhe Sahebzamani, Ghazal Luong, Christina Dezaki, Fatemeh Taheri Jafari, Mohammad Abolmaesumi, Purang Tsang, Teresa Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Vancouver Gen Hosp Vancouver BC Canada Univ British Columbia Dept Med Vancouver BC Canada Univ British Columbia Div Cardiol Vancouver BC Canada

ISBN: (纸本)9781665445092

Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic localization of those landmarks is highly desirable to improve workflow and reduce interobserver variability. Training a machine learning framework to perform such localization is hindered given the sparse nature of gold standard labels;only few percent of cardiac cine series frames are normally manually labeled for clinical use. In this paper, we propose a new end-to-end reciprocal detection and tracking model that is specifically designed to handle the sparse nature of echocardiography labels. The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames. The superiority of the proposed reciprocal model is demonstrated using a series of experiments.

关键词： Training Location awareness computer vision Echocardiography Annotations Computational modeling Machine learning

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ReMix: Towards Image-to-Image Translation with Limited Data

ReMix: Towards Image-to-Image Translation with Limited Data

引用

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

作者： Cao, Jie Hou, Luanxuan Yang, Ming-Hsuan He, Ran Sun, Zhenan CASIA CRIPAC NLPR Beijing Peoples R China CASIA CEBSIT Beijing Peoples R China UCAS AIR Beijing Peoples R China Univ Calif Merced Merced CA USA Google Res Mountain View CA USA Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665445092

Image-to-image (I2I) translation methods based on generative adversarial networks (GANs) typically suffer from overfitting when limited training data is available. In this work, we propose a data augmentation method (ReMix) to tackle this issue. We interpolate training samples at the feature level and propose a novel content loss based on the perceptual relations among samples. The generator learns to translate the in-between samples rather than memorizing the training set, and thereby forces the discriminator to generalize. The proposed approach effectively reduces the ambiguity of generation and renders content-preserving results. The ReMix method can be easily incorporated into existing GAN models with minor modifications. Experimental results on numerous tasks demonstrate that GAN models equipped with the ReMix method achieve significant improvements.

关键词： Training Measurement Image quality computer vision Computational modeling Training data Generative adversarial networks

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Multi-modal Arousal and Valence Estimation under Noisy Conditions

Multi-modal Arousal and Valence Estimation under Noisy Condi...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Denis Dresvyanskiy Maxim Markitantov Jiawei Yu Heysem Kaya Alexey Karpov Ulm University Germany ITMO University Russia St. Petersburg Federal Research Center of the Russian Academy of Sciences St. Petersburg Russia Department of Information and Computing Sciences Utrecht University The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic emotion recognition has gained significant attention over the past two decades due to the central role that emotions play in human communication. While multi-modal systems demonstrate high performances on laboratory-controlled data, their validity on non-lab-controlled, namely ‘in-the-wild’ data, remains a challenge. This work investigates audio-visual deep learning approaches for emotion recognition in-the-wild, with a particular focus on the effectiveness of architectures based on fine-tuned Convolutional Neural Networks (CNN) and Public Dimensional Emotion Model (PDEM) for video and audio modality, respectively. We explore and compare various temporal modeling techniques (e.g., transformer architectures) and fusion strategies by leveraging the embeddings from developed multi-stage trained modality-specific Deep Neural Networks (DNN). The results are reported on the Af-fWild2 dataset following the Affective Behavior Analysis in-the-Wild 2024 (ABAW’24) challenge protocol. Our investigation highlights the complexities of robust multi-modal emotion recognition in an unconstrained environment, providing insights into the usage of various deep learning architectures for tackling this challenging task.

关键词： Deep learning Emotion recognition Protocols conferences Estimation computer architecture Transformers

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