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

共 22906 条记录，以下是4731-4740 订阅

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Face recognition using deep learning feature injection: An accurate hybrid network combining neural networks based on feature extraction with convolutional neural network 48

Face recognition using deep learning feature injection: An a...

引用

48th Latin American computer conference (CLEI)

作者： Sisco, Yilber J. Carmona, Rhadames Univ Cent Venezuela Escuela Comp Ctr Comp Graf Caracas Venezuela

ISBN: (数字)9781665476713

ISBN: (纸本)9781665476713

In this work we evaluate, compare and combine several face recognition models. Three multi-layer neural networks are trained with different descriptors: Histograms of Gradient Orientation (HOG), Scale Invariant Feature Transform (SIFT) and Local Binary pattern Histograms (LBPH). A fourth model uses a Convolutional Neural Networks (CNN), trained with the raw images. The combination of these networks into a hybrid network results in a noticeable accuracy improvement, not only by comparing the results with the individual networks, but also with respect to the current state of the art. To validade our hybrid model, we include different sets of images, acquired under different conditions such as lighting, pose and expression.

关键词： computer vision Machine Learning Histograms of Gradient Orientation (HOG) Scale Invariant Feature Transformations (SIFT) Local Binary pattern Histograms (LBPH) Convolutional Neural Network Artificial Intelligence Transfer Learning Feature extraction

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Unpaired Image-to-Image Translation via Latent Energy Transport

Unpaired Image-to-Image Translation via Latent Energy Transp...

引用

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

作者： Zhao, Yang Chen, Changyou Univ Buffalo SUNY Buffalo NY 14260 USA

ISBN: (纸本)9781665445092

Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. Most works apply adversarial learning in the ambient image space, which could be computationally expensive and challenging to train. In this paper, we propose to deploy an energy-based model (EBM) in the latent space of a pretrained autoencoder for this task. The pretrained autoencoder serves as both a latent code extractor and an image reconstruction worker. Our model, LETIT1, is based on the assumption that two domains share the same latent space, where latent representation is implicitly decomposed as a content code and a domain-specific style code. Instead of explicitly extracting the two codes and applying adaptive instance normalization to combine them, our latent EBM can implicitly learn to transport the source style code to the target style code while preserving the content code, an advantage over existing image translation methods. This simplified solution is also more efficient in the one-sided unpaired image translation setting. Qualitative and quantitative comparisons demonstrate superior translation quality and faithfulness for content preservation. Our model is the first to be applicable to 1024x1024-resolution unpaired image translation to the best of our knowledge. Code is available at https://***/YangNaruto/latentenergy-transport.

关键词： Visualization computer vision Adaptation models Codes Computational modeling Scalability Adversarial machine learning

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Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder

Soft-IntroVAE: Analyzing and Improving the Introspective Var...

引用

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

作者： Daniel, Tal Tamar, Aviv Technion Dept Elect Engn Haifa Israel

ISBN: (纸本)9781665445092

The recently introduced introspective variational autoencoder (IntroVAE) exhibits outstanding image generations, and allows for amortized inference using an image encoder. The main idea in IntroVAE is to train a VAE adversarially, using the VAE encoder to discriminate between generated and real data samples. However, the original IntroVAE loss function relied on a particular hinge-loss formulation that is very hard to stabilize in practice, and its theoretical convergence analysis ignored important terms in the loss. In this work, we take a step towards better understanding of the IntroVAE model, its practical implementation, and its applications. We propose the Soft-IntroVAE, a modified IntroVAE that replaces the hinge-loss terms with a smooth exponential loss on generated samples. This change significantly improves training stability, and also enables theoretical analysis of the complete algorithm. Interestingly, we show that the IntroVAE converges to a distribution that minimizes a sum of KL distance from the data distribution and an entropy term. We discuss the implications of this result, and demonstrate that it induces competitive image generation and reconstruction. Finally, we describe an application of Soft-IntroVAE to unsupervised image translation, and demonstrate compelling results. Code and additional information is available on the project website - ***/soft-intro-vae-web.

关键词： Training computer vision Codes Image synthesis Computational modeling Stability analysis Entropy

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Feature Decomposition and Reconstruction Learning for Effective Facial Expression recognition

Feature Decomposition and Reconstruction Learning for Effect...

引用

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

作者： Ruan, Delian Yan, Yan Lai, Shenqi Chai, Zhenhua Shen, Chunhua Wang, Hanzi Xiamen Univ Xiamen Peoples R China Meituan Vis Intelligence Ctr Beijing Peoples R China Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665445092

In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as the combination of the shared information (expression similarities) across different expressions and the unique information (expression-specific variations) for each expression. More specifically, FDRI, mainly consists of two crucial networks: a Feature Decomposition Network (FDN) and a Feature Reconstruction Network (MN). In particular, FDN first decomposes the basic features extracted from a backbone network into a set of facial action-aware latent features to model expression similarities. Then, FRN captures the intra-feature and inter feature relationships for latent features to characterize expression-specific variations, and reconstructs the expression feature. To this end, two modules including an intra-feature relation modeling module and an inter-feature relation modeling module are developed in FRN. Experimental results on both the in-the-lab databases (including CK+, MMI, and Oulu-CASIA) and the in-the-wild databases (including RAF-DB and SFEW) show that the proposed FDRL method consistently achieves higher recognition accuracy than several state-of-the-art methods. This clearly highlights the benefit of feature decomposition and reconstruction for classifying expressions.

关键词： computer vision Databases Face recognition Feature extraction

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

作者： Spencer Low Oliver Nina Angel D. Sappa Erik Blasch Nathan Inkawhich Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH ESPOL Polytechnic University Ecuador Computer Vision Center Spain Air Force Research Laboratory Arlington VA Air Force Research Laboratory Rome NY

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.

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Learning a Proposal Classifier for Multiple Object Tracking

Learning a Proposal Classifier for Multiple Object Tracking

引用

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

作者： Dai, Peng Weng, Renliang Choi, Wongun Zhang, Changshui He, Zhangping Ding, Wei Tsinghua Univ Beijing Peoples R China Aibee Inc Beijing Peoples R China

ISBN: (纸本)9781665445092

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. However, it is not trivial to solve the data-association problem in an end-to-end fashion. In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph. This framework is similar to the two-stage object detector Faster RCNN, and can solve the MOT problem in a data-driven way. For proposal generation, we propose an iterative graph clustering method to reduce the computational cost while maintaining the quality of the generated proposals. For proposal scoring, we deploy a trainable graph-convolutional-network (GCN) to learn the structural patterns of the generated proposals and rank them according to the estimated quality scores. For trajectory inference, a simple deoverlapping strategy is adopted to generate tracking output while complying with the constraints that no detection can be assigned to more than one track. We experimentally demonstrate that the proposed method achieves a clear performance improvement in both MOTA and IDF1 with respect to previous state-of-the-art on two public benchmarks.

关键词： Deep learning computer vision Detectors Market research Trajectory Computational efficiency Proposals

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Semantic Image Matting

Semantic Image Matting

引用

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

作者： Sun, Yanan Tang, Chi-Keung Tai, Yu-Wing HKUST Hong Kong Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), and/or objects containing very fine details (e.g., hairs). Although conventional matting formulation can be applied to all of the above cases, no previous work has attempted to reason the underlying causes of matting due to various foreground semantics. We show how to obtain better alpha mattes by incorporating into our framework semantic classification of matting regions. Specifically, we consider and learn 20 classes of matting patterns, and propose to extend the conventional trimap to semantic trimap. The proposed semantic trimap can be obtained automatically through patch structure analysis within trimap regions. Meanwhile, we learn a multi-class discriminator to regularize the alpha prediction at semantic level, and content-sensitive weights to balance different regularization losses. Experiments on multiple benchmarks show that our method outperforms other methods and has achieved the most competitive state-of-theart performance. Finally, we contribute a large-scale Semantic Image Matting Dataset with careful consideration of data balancing across different semantic classes. Code and dataset are available at https://***/nowsyn/ SIM.

关键词： Hair computer vision Codes Semantics Benchmark testing Data mining Optimization

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3D-MAN: 3D Multi-frame Attention Network for Object Detection

3D-MAN: 3D Multi-frame Attention Network for Object Detectio...

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

作者： Yang, Zetong Zhou, Yin Chen, Zhifeng Ngiam, Jiquan Chinese Univ Hong Kong Hong Kong Peoples R China Waymo LLC Mountain View CA USA Google Res Brain Team Mountain View CA USA

ISBN: (纸本)9781665445092

3D object detection is an important module in autonomous driving and robotics. However, many existing methods focus on using single frames to perform 3D detection, and do not fully utilize information from multiple frames. In this paper, we present 3D-MAN: a 3D multi-frame attention network that effectively aggregates features from multiple perspectives and achieves state-of-the-art performance on Waymo Open Dataset. 3D-MAN first uses a novel fast single-frame detector to produce box proposals. The box proposals and their corresponding feature maps are then stored in a memory bank. We design a multi-view alignment and aggregation module, using attention networks, to extract and aggregate the temporal features stored in the memory bank. This effectively combines the features coming from different perspectives of the scene. We demonstrate the effectiveness of our approach on the large-scale complex Waymo Open Dataset, achieving state-of-the-art results compared to published single-frame and multi-frame methods.

关键词： computer vision Three-dimensional displays Fuses Aggregates Object detection Detectors Feature extraction

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M³P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training

M<SUP>3</SUP>P: Learning Universal Representations via Multi...

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

作者： Ni, Minheng Huang, Haoyang Su, Lin Cui, Edward Bharti, Taroon Wang, Lijuan Zhang, Dongdong Duan, Nan Harbin Inst Technol Res Ctr Social Comp & Informat Retrieval Harbin Peoples R China Microsoft Res Asia Nat Language Comp Shanghai Peoples R China Microsoft Bing Multimedia Team Shanghai Peoples R China Microsoft Cloud AI Redmond WA USA

ISBN: (纸本)9781665445092

We present (MP)-P-3, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training. Our goal is to learn universal representations that can map objects occurred in different modalities or texts expressed in different languages into a common semantic space. In addition, to explicitly encourage fine-grained alignment between images and non-English languages, we also propose Multimodal Code-switched Training (MCT) to combine monolingual pre-training and multimodal pre-training via a code-switch strategy. Experiments are performed on the multilingual image retrieval task across two benchmark datasets, including MSCOCO and Multi3OK. (MP)-P-3 can achieve comparable results for English and new state-of-the-art results for non-English languages.

关键词： Training computer vision Computational modeling Semantics Image retrieval Benchmark testing Data models

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Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks

Unsupervised Learning of Depth and Depth-of-Field Effect fro...

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

作者： Kaneko, Takuhiro NTT Corp NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9781665445092

Understanding the 3D world from 2D projected natural images is a fundamental challenge in computer vision and graphics. Recently, an unsupervised learning approach has garnered considerable attention owing to its advantages in data collection. However, to mitigate training limitations, typical methods need to impose assumptions for viewpoint distribution (e.g., a dataset containing various viewpoint images) or object shape (e.g., symmetric objects). These assumptions often restrict applications;for instance, the application to non-rigid objects or images captured from similar viewpoints (e.g., flower or bird images) remains a challenge. To complement these approaches, we propose aperture rendering generative adversarial networks (AR-GANs), which equip aperture rendering on top of GANs, and adopt focus cues to learn the depth and depth-of-field (DoF) effect of unlabeled natural images. To address the ambiguities triggered by unsupervised setting (i.e., ambiguities between smooth texture and out-of-focus blurs, and between foreground and background blurs), we develop DoF mixture learning, which enables the generator to learn real image distribution while generating diverse DoF images. In addition, we devise a center focus prior to guiding the learning direction. In the experiments, we demonstrate the effectiveness of AR-GANs in various datasets, such as flower, bird, and face images, demonstrate their portability by incorporating them into other 3D representation learning GANs, and validate their applicability in shallow DoF rendering.

关键词： Training computer vision Three-dimensional displays Shape Computational modeling Apertures Rendering (computer graphics)

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