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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution

Cascaded Local Implicit Transformer for Arbitrary-Scale Supe...

引用

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

作者： Chen, Hao-Wei Xu, Yu-Syuan Hong, Min-Fong Tsai, Yi-Min Kuo, Hsien-Kai Lee, Chun-Yi Natl Tsing Hua Univ Elsa Lab Hsinchu Taiwan MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350301298

Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT), which integrates the attention mechanism and frequency encoding technique into a local implicit image function. We design a cross-scale local attention block to effectively aggregate local features and a local frequency encoding block to combine positional encoding with Fourier domain information for constructing high-resolution images. To further improve representative power, we propose a Cascaded LIT (CLIT) that exploits multi-scale features, along with a cumulative training strategy that gradually increases the upsampling scales during training. We have conducted extensive experiments to validate the effectiveness of these components and analyze various training strategies. The qualitative and quantitative results demonstrate that LIT and CLIT achieve favorable results and outperform the prior works in arbitrary super-resolution tasks.

关键词： Low-level vision

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Driver Cell Phone Usage Detection From HOV/HOT NIR Images

Driver Cell Phone Usage Detection From HOV/HOT NIR Images

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Artan, Yusuf Bulan, Orhan Loce, Robert P. Paul, Peter Xerox Res Ctr Webster Webster NY 14580 USA

ISBN: (纸本)9781479943098

Distracted driving due to cell phone usage is an increasingly costly problem in terms of lost lives and damaged property. Motivated by its impact on public safety and property, several state and federal governments have enacted regulations that prohibit driver mobile phone usage while driving. These regulations have created a need for cell phone usage detection for law enforcement. In this paper, we propose a computer vision based method for determining driver cell phone usage using a near infrared (NIR) camera system directed at the vehicle's front windshield. The developed method consists of two stages;first, we localize the driver's face region within the front windshield image using the deformable part model (DPM). Next, we utilize a local aggregation based image classification technique to classify a region of interest (ROI) around the drivers face to detect the cell phone usage. We propose two classification architectures by using full face and half face images for classification and compare their performance in terms of accuracy, specificity, and sensitivity. We also present a comparison of various local aggregation-based image classification methods using bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD) and Fisher vectors (FV). A data set of 1500 images was collected on a public roadway and is used to perform the experiments.

关键词： cell phone usage detection image classification Image classification cell phone use Drivers images driver circuits full face computer vision Windscreen Public safety Law Enforcement

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Hilbert Sinkhorn Divergence for Optimal Transport

Hilbert Sinkhorn Divergence for Optimal Transport

引用

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

作者： Li, Qian Wang, Zhichao Li, Gang Pang, Jun Xu, Guandong Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia Deakin Univ Ctr Cyber Secur Res & Innovat Geelong Vic 3216 Australia Univ Luxembourg Fac Sci Technol & Med Luxembourg Luxembourg

ISBN: (纸本)9781665445092

The Sinkhorn divergence has become a very popular metric to compare probability distributions in optimal transport. However, most works resort to the Sinkhorn divergence in Euclidean space, which greatly blocks their applications in complex data with nonlinear structure. It is therefore of theoretical demand to empower the Sinkhorn divergence with the capability of capturing nonlinear structures. We propose a theoretical and computational framework to bridge this gap. In this paper, we extend the Sinkhorn divergence in Euclidean space to the reproducing kernel Hilbert space, which we term "Hilbert Sinkhorn divergence" (HSD). In particular, we can use kernel matrices to derive a closed form expression of the HSD that is proved to be a tractable convex optimization problem. We also prove several attractive statistical properties of the proposed HSD, i.e., strong consistency, asymptotic behavior and sample complexity. Empirically, our method yields state-of-the-art performances on image classification and topological data analysis.

关键词： Measurement Manifolds Training Probability distribution Complexity theory pattern recognition Manifold learning

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Improving Unsupervised Image Clustering With Robust Learning

Improving Unsupervised Image Clustering With Robust Learning

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

作者： Park, Sungwon Han, Sungwon Kim, Sundong Kim, Danu Park, Sungkyu Hong, Seunghoon Cha, Meeyoung Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Inst for Basic Sci Korea Data Sci Grp Daejeon South Korea

ISBN: (纸本)9781665445092

Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizing pseudo-labels of existing image clustering models as a noisy dataset that may include misclassified samples. Its retraining process can revise misaligned knowledge and alleviate the overconfidence problem in predictions. The model's flexible structure makes it possible to be used as an add-on module to other clustering methods and helps them achieve better performance on multiple datasets. Extensive experiments show that the proposed model can adjust the model confidence with better calibration and gain additional robustness against adversarial noise.

关键词： Training computer vision Clustering methods Computational modeling Predictive models Robustness Calibration

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Camera Lens Super-Resolution 32

Camera Lens Super-Resolution

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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Chang Xiong, Zhiwei Tian, Xinmei Zha, Zheng-Jun Wu, Feng Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9781728132938

Existing methods for single image super-resolution (SR) are typically evaluated with synthetic degradation models such as bicubic or Gaussian downsampling. In this paper, we investigate SR from the perspective of camera lenses, named as CameraSR, which aims to alleviate the intrinsic tradeoff between resolution (R) and field-of-view (V) in realistic imaging systems. Specifically, we view the R-V degradation as a latent model in the SR process and learn to reverse it with realistic low- and high-resolution image pairs. To obtain the paired images, we propose two novel data acquisition strategies for two representative imaging systems (i.e., DSLR and smartphone cameras), respectively. Based on the obtained City100 dataset, we quantitatively analyze the performance of commonly-used synthetic degradation models, and demonstrate the superiority of CameraSR as a practical solution to boost the performance of existing SR methods. Moreover, CameraSR can be readily generalized to different content and devices, which serves as an advanced digital zoom tool in realistic imaging systems.

关键词： Low-level vision

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Model-Based Respiratory Motion Compensation for Image-Guided Cardiac Interventions

Model-Based Respiratory Motion Compensation for Image-Guided...

引用

23rd IEEE conference on computer vision and pattern recognition (CVPR)

作者： Schneider, Matthias Sundar, Hari Liao, Rui Hornegger, Joachim Xu, Chenyang Univ Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany Siemens Corp Res Princeton NJ 08540 USA

ISBN: (纸本)9781424469840

In this paper we propose and validate a PCA-based respiratory motion model for motion compensation during image-guided cardiac interventions. In a preparatory training phase, a preoperative 3-D segmentation of the coronary arteries is automatically registered with a cardiac gated biplane cineangiogram, and used to build a respiratory motion model. This motion model is subsequently used as a prior within the intraoperative registration process for motion compensation to restrict the search space. Our hypothesis is that the use of this model-constrained registration increases the robustness and registration accuracy, especially for weak data constraints such as low signal-to-noise ratio, the lack of contrast information, or an intraoperative monoplane setting. This allows for reducing radiation exposure without compromising on registration accuracy. Synthetic data as well as phantom and clinical datasets have been used to validate the model-based registration in terms of registration accuracy, robustness and speed. We were able to significantly accelerate the intraoperative registration with a 3-D TRE of less than 2 mm for both monoplane images and intraprocedure settings with missing contrast information based on 2-D guidewire tracking, which makes it feasible for motion correction in clinical procedures.

关键词： Signal to noise ratio

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COTS: Collaborative Two-Stream vision-Language Pre-Training Model for Cross-Modal Retrieval

COTS: Collaborative Two-Stream Vision-Language Pre-Training ...

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

作者： Lu, Haoyu Fei, Nanyi Huo, Yuqi Gao, Yizhao Lu, Zhiwu Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Large-scale single-stream pre-training has shown dramatic performance in image-text retrieval. Regrettably, it faces low inference efficiency due to heavy attention layers. Recently, two-stream methods like CUP and ALIGN with high inference efficiency have also shown promising performance, however;they only consider instance-level alignment between the two streams (thus there is still room for improvement). To overcome these limitations, we propose a novel Collaborative Two-Stream vision-language pre-training model termed COTS for image-text retrieval by enhancing cross-modal interaction. In addition to instance-level alignment via momentum contrastive learning, we leverage two extra levels of cross-modal interactions in our COTS: (1) Token-level interaction - a masked vision-language modeling (MVLM) learning objective is devised without using a cross-stream network module, where variational autoencoder is imposed on the visual encoder to generate visual tokens for each image. (2) Task-level interaction - a KL-alignment learning objective is devised between text-to-image and image-to-text retrieval tasks, where the probability distribution per task is computed with the negative queues in momentum contrastive learning. Under a fair comparison setting, our COTS achieves the highest performance among all two-stream methods and comparable performance (but with 10,800x faster in inference) ***. the latest single-stream methods. Importantly, our COTS is also applicable to text-to-video retrieval, yielding new state-of-the-art on the widely-used MSR-VTT dataset.

关键词： Visualization computer vision Collaboration Streaming media Probability distribution pattern recognition Task analysis

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GRSA: Generalized Range Swap Algorithm for the Efficient Optimization of MRFs

GRSA: Generalized Range Swap Algorithm for the Efficient Opt...

引用

IEEE conference on computer vision and pattern recognition (CVPR)

作者： Liu, Kangwei Zhang, Junge Yang, Peipei Huang, Kaiqi Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp Beijing Peoples R China

ISBN: (纸本)9781467369640

Markov Random Field (MRF) is an important tool and has been widely used in many vision tasks. Thus, the optimization of MRFs is a problem of fundamental importance. Recently, Veskler and Kumar et. al propose the range move algorithms, which are one of the most successful solvers to this problem. However, two problems have limited the applicability of previous range move algorithms: 1) They are limited in the types of energies they can handle (i.e. only truncated convex functions);2) These algorithms tend to be very slow compared to other graph-cut based algorithms (e.g. alpha-expansion and alpha beta-swap). In this paper, we propose a generalized range swap algorithm (GRSA) for efficient optimization of MRFs. To address the first problem, we extend the GRSA to arbitrary semimetric energies by restricting the chosen labels in each move so that the energy is submodular on the chosen subset. Furthermore, to feasibly choose the labels satisfying the submodular condition, we provide a sufficient condition of the submodularity. For the second problem, unlike previous range move algorithms which execute the set of all possible range moves, we dynamically obtain the iterative moves by solving a set cover problem, which greatly reduces the number of moves during the optimization. Experiments show that the GRSA offers a great speedup over previous range swap algorithms, while it obtains competitive solutions.

关键词： Functions

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Face Parsing from RGB and Depth Using Cross-Domain Mutual Learning

Face Parsing from RGB and Depth Using Cross-Domain Mutual Le...

引用

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

作者： Lee, Jihyun Bhattarai, Binod Kim, Tae-Kyun Korea Adv Inst Sci & Technol Seoul South Korea Imperial Coll London London England

ISBN: (纸本)9781665448994

Existing methods of face parsing have proven effective at classifying each pixel of an RGB image into different facial components. However, there is a lack of face parsing research that utilizes depth domain. To the best of our knowledge, we present the first study to exploit 2.5D data for face parsing. We introduce a novel framework to jointly learn (1) RGB face parsing, (2) depth face parsing and (3) RGB-to-depth domain translation, which can be effective even when only a small amount of annotated depth data is available for training. To this end, we also create the first RGB-D face parsing benchmarks based on CelebAMask-HQ, LaPa and Helen by utilizing an off-the-shelf 3D head reconstruction model. Overall, our approach makes two main contributions. First, our method leverages mutual learning between RGB and depth face parsing, which enables bidirectional knowledge distillation between the two data domains. Second, our method utilizes end-to-end learning of RGB-to-depth domain translation and depth face parsing, which can help overcome the scarcity of annotated depth data. We perform extensive experiments to validate the effectiveness of our method, in which we achieve state-of-the-art results in RGB face parsing. As far as we know, we also report the first results on face parsing from depth data. All experiments are conducted on our new RGB-D face parsing datasets, which are publicly available at https://***/jyunlee/CelebAMaskHQ-D_LaPa-D_Helen-D.

关键词： Training Solid modeling computer vision Three-dimensional displays Head Face recognition conferences

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A MODEL OF HUMAN ESTIMATION OF 3D SHAPE FROM SHADING INFORMATION USING ZERO CROSSINGS 11

A MODEL OF HUMAN ESTIMATION OF 3D SHAPE FROM SHADING INFORMA...

引用

conference A: computer vision and Applications, at the 11th IAPR International conference on pattern recognition

作者： KONDO, S ATSUTA, K TOKAI UNIV FAC ENGNDEPT COMMUN ENGNHIRATSUKAKANAGAWA 25912JAPAN

ISBN: (纸本)081862910X

We propose a model of human estimation of 3D shape from a single image using the estimation theory which the authors have proposed. Firstly it is seen using an example that most human subjects estimate a 3D shape from the single image which is physically contradictory. Secondly it is seen that in order to explain the human false estimate it is neccesary that in human estimation a given image is devided into several regions and different illuminant directions are used depending on the regions. Thirdly a model of human estimation of 3D shape and an algorithm of 3D shape estimation following to the model are proposed. © 1992 IEEE.

关键词： Three dimensional computer graphics

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