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Self Texture Transfer Networks for Low Bitrate Image Compression

Self Texture Transfer Networks for Low Bitrate Image Compres...

引用

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

作者： Iwai, Shoma Miyazaki, Tomo Sugaya, Yoshihiro Omachi, Shinichiro Tohoku Univ Grad Sch Engn Dept Commun Sendai Miyagi Japan

ISBN: (纸本)9781665448994

Lossy image compression causes a loss of texture, especially at low bitrate. To mitigate this problem, we propose a novel image compression method that utilizes a reference-based image super-resolution model. We use two image compression models and a self texture transfer model. The image compression models encode and decode a whole input image and selected reference patches. The reference patches are small but compressed with high quality. The self texture transfer model transfers the texture of reference patches into similar regions in the compressed image. The experimental results show that our method can reconstruct accurate texture by transferring the texture of reference patches.

关键词： computer vision Image coding conferences Bit rate Superresolution pattern recognition Image reconstruction

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Federated Learning with Position-Aware Neurons

Federated Learning with Position-Aware Neurons

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

作者： Li, Xin-Chun Xu, Yi-Chu Song, Shaoming Li, Bingshuai Li, Yinchuan Shao, Yunfeng Zhan, De-Chuan Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Federated Learning (FL) fuses collaborative models from local nodes without centralizing users' data. The permutation invariance property of neural networks and the non-i.i.d. data across clients make the locally updated parameters imprecisely aligned, disabling the coordinate-based parameter averaging. Traditional neurons do not explicitly consider position information. Hence, we propose Position-Aware Neurons (PANs) as an alternative, fusing position-related values (i.e., position encodings) into neuron outputs. PANs couple themselves to their positions and minimize the possibility of dislocation, even updating on heterogeneous data. We turn on/off PANs to disable/enable the permutation invariance property of neural networks. PANs are tightly coupled with positions when applied to FL, making parameters across clients pre-aligned and facilitating coordinate-based parameter averaging. PANs are algorithm-agnostic and could universally improve existing FL algorithms. Furthermore, "FL with PANs" is simple to implement and computationally friendly.

关键词： Training computer vision Fuses Neurons Optimization methods Collaborative work Encoding

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Re-ranking Person Re-identification with k-reciprocal Encoding 30

Re-ranking Person Re-identification with <i>k</i>-reciprocal...

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhong, Zhun Zheng, Liang Cao, Donglin Li, Shaozi Xiamen Univ Cognit Sci Dept Xiamen Peoples R China Univ Technol Sydney Sydney NSW Australia Xiamen Univ Fujian Key Lab Brain Inspired Comp Tech & Applica Xiamen Peoples R China

ISBN: (纸本)9781538604571

When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method(1).

关键词： Probes Encoding Measurement Feature extraction computer vision Robustness

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VLPD: Context-Aware Pedestrian Detection via vision-Language Semantic Self-Supervision

VLPD: Context-Aware Pedestrian Detection via Vision-Language...

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

作者： Liu, Mengyin Jiang, Jie Zhu, Chao Yin, Xu-Cheng Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Tencent Data Platform Dept Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Detecting pedestrians accurately in urban scenes is significant for realistic applications like autonomous driving or video surveillance. However, confusing human-like objects often lead to wrong detections, and small scale or heavily occluded pedestrians are easily missed due to their unusual appearances. To address these challenges, only object regions are inadequate, thus how to fully utilize more explicit and semantic contexts becomes a key problem. Meanwhile, previous context-aware pedestrian detectors either only learn latent contexts with visual clues, or need laborious annotations to obtain explicit and semantic contexts. Therefore, we propose in this paper a novel approach via vision-Language semantic self-supervision for context-aware Pedestrian Detection (VLPD) to model explicitly semantic contexts without any extra annotations. Firstly, we propose a self-supervised vision-Language Semantic (VLS) segmentation method, which learns both fully-supervised pedestrian detection and contextual segmentation via self-generated explicit labels of semantic classes by vision-language models. Furthermore, a self-supervised Prototypical Semantic Contrastive (PSC) learning method is proposed to better discriminate pedestrians and other classes, based on more explicit and semantic contexts obtained from VLS. Extensive experiments on popular benchmarks show that our proposed VLPD achieves superior performances over the previous state-of-the-arts, particularly under challenging circumstances like small scale and heavy occlusion. Code is available at https://***/lmy98129/VLPD.

关键词： detection recognition: Categorization retrieval

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Consensus Maximization with Linear Matrix Inequality Constraints 30

Consensus Maximization with Linear Matrix Inequality Constra...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Speciale, Pablo Paudel, Danda P. Oswald, Martin R. Kroeger, Till Gool, Luc V. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Swiss Fed Inst Technol D ITET Comp Vis Lab Zurich Switzerland Microsoft Corp Redmond WA 98052 USA Katholieke Univ Leuven ESAT PSI VISICS Leuven Belgium

ISBN: (纸本)9781538604571

Consensus maximization has proven to be a useful tool for robust estimation. While randomized methods like RANSAC are fast, they do not guarantee global optimality and fail to manage large amounts of outliers. On the other hand, global methods are commonly slow because they do not exploit the structure of the problem at hand. In this paper, we show that the solution space can be reduced by introducing Linear Matrix Inequality (LMI) constraints. This leads to significant speed ups of the optimization time even for large amounts of outliers, while maintaining global optimality. We study several cases in which the objective variables have a special structure, such as rotation, scaled-rotation, and essential matrices, which are posed as LMI constraints. This is very useful in several standard computer vision problems, such as estimating Similarity Transformations, Absolute Poses, and Relative Poses, for which we obtain compelling results on both synthetic and real datasets. With up to 90 percent outlier rate, where RANSAC often fails, our constrained approach is consistently faster than the non-constrained one - while finding the same global solution.

关键词： outliers Solution space linear matrix inequalities Special structures estimation theory Interim Local Management Interface Hand images computer vision Speeding optimality Posing

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SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation

SG-Net: Spatial Granularity Network for One-Stage Video Inst...

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

作者： Liu, Dongfang Cui, Yiming Tan, Wenbo Chen, Yingjie Purdue Univ W Lafayette IN 47907 USA Univ Florida Gainesville FL 32611 USA Hangzhou Dian Zi Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, leaving plenty of room for improvement. In contrast, we approach the VIS task from a new perspective and propose a one-stage spatial granularity network (SG-Net). Compared to the conventional two-stage methods, SG-Net demonstrates four advantages: 1) Our method has a one-stage compact architecture and each task head (detection, segmentation, and tracking) is crafted interdependently so they can effectively share features and enjoy the joint optimization;2) Our mask prediction is dynamically performed on the sub-regions of each detected instance, leading to high-quality masks of fine granularity;3) Each of our task predictions avoids using expensive proposal-based RoI features, resulting in much reduced runtime complexity per instance;4) Our tracking head models objects' centerness movements for tracking, which effectively enhances the tracking robustness to different object appearances. In evaluation, we present state-of-the-art comparisons on the YouTube-VIS dataset. Extensive experiments demonstrate that our compact one-stage method can achieve improved performance in both accuracy and inference speed. We hope our SG-Net could serve as a strong and flexible baseline for the VIS task. Our code will be available here(1).

关键词： computer vision Runtime Tracking computer architecture Predictive models Feature extraction Robustness

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Domain Consensus Clustering for Universal Domain Adaptation

Domain Consensus Clustering for Universal Domain Adaptation

引用

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

作者： Li, Guangrui Kang, Guoliang Zhu, Yi Wei, Yunchao Yang, Yi Univ Technol Sydney ReLER Lab AAII Sydney NSW Australia Carnegie Mellon Univ Pittsburgh PA 15213 USA Amazon Web Serv Seattle WA USA

ISBN: (纸本)9781665445092

In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to transfer the knowledge from source to target under unaligned label space. The main challenge of UniDA lies in how to separate common classes (i.e., classes shared across domains), from private classes (i.e., classes only exist in one domain). Previous works treat the private samples in the target as one generic class but ignore their intrinsic structure. Consequently, the resulting representations are not compact enough in the latent space and can be easily confused with common samples. To better exploit the intrinsic structure of the target domain, we propose Domain Consensus Clustering (DCC), which exploits the domain consensus knowledge to discover discriminative clusters on both common samples and private ones. Specifically, we draw the domain consensus knowledge from two aspects to facilitate the clustering and the private class discovery, i.e., the semantic-level consensus, which identifies the cycle-consistent clusters as the common classes, and the sample-level consensus, which utilizes the cross-domain classification agreement to determine the number of clusters and discover the private classes. Based on DCC, we are able to separate the private classes from the common ones, and differentiate the private classes themselves. Finally, we apply a class-aware alignment technique on identified common samples to minimize the distribution shift, and a prototypical regularizer to inspire discriminative target clusters. Experiments on four benchmarks demonstrate DCC significantly outperforms previous state-of-the-arts.

关键词： computer vision Benchmark testing pattern recognition

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An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM 32

An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Geneva, Patrick Maley, James Huang, Guoquan Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9781728132938

It holds great implications for practical applications to enable centimeter-accuracy positioning for mobile and wearable sensor systems. In this paper, we propose a novel, high-precision, efficient visual-inertial (VI)-SLAM algorithm, termed Schmidt-EKF VI-SLAM (SEVIS), which optimally fuses IMU measurements and monocular images in a tightly-coupled manner to provide 3D motion tracking with bounded error. In particular, we adapt the Schmidt Kalman filter formulation to selectively include informative features in the state vector while treating them as nuisance parameters (or Schmidt states) once they become matured. This change in modeling allows for significant computational savings by no longer needing to constantly update the Schmidt states (or their covariance), while still allowing the EKF to correctly account for their cross-correlations with the active states. As a result, we achieve linear computational complexity in terms of map size, instead of quadratic as in the standard SLAM systems. In order to fully exploit the map information to bound navigation drifts, we advocate efficient keyframe-aided 2D-to-2D feature matching to find reliable correspondences between current 2D visual measurements and 3D map features. The proposed SEVIS is extensively validated in both simulations and experiments.

关键词： 3D from Multiview and Sensors Motion and Tracking Optimization Methods Robotics + Driving vision Applications and Sys

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Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections

Iterative Next Boundary Detection for Instance Segmentation ...

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

作者： Gillert, Alexander Resente, Giulia Anadon-Rosell, Alba Wilmking, Martin von Lukas, Uwe Freiherr Fraunhofer Inst Comp Graph Res IGD Rostock Germany Ernst Moritz Arndt Univ Inst Bot & Landscape Ecol Greifswald Germany Ctr Res Ecol & Forestry Applicat CREAF Barcelona Spain Univ Rostock Inst Visual & Analyt Comp Rostock Germany

ISBN: (纸本)9798350301298

We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://***/alexander-g/INBD.

关键词： cell microscopy Medical and biological vision

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NFormer: Robust Person Re-identification with Neighbor Transformer

NFormer: Robust Person Re-identification with Neighbor Trans...

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

作者： Wang, Haochen Shen, Jiayi Liu, Yongtuo Gao, Yan Gavves, Efstratios Univ Amsterdam Amsterdam Netherlands Xiaohongshu Inc Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Person re-identification aims to retrieve persons in highly varying settings across different cameras and scenarios, in which robust and discriminative representation learning is crucial. Most research considers learning representations from single images, ignoring any potential interactions between them. However, due to the high intra-identity variations, ignoring such interactions typically leads to outlier features. To tackle this issue, we propose a Neighbor Transformer Network, or NFormer, which explicitly models interactions across all input images, thus suppressing outlier features and leading to more robust representations overall. As modelling interactions between enormous amount of images is a massive task with lots of distractors, NFormer introduces two novel modules, the Landmark Agent Attention, and the Reciprocal Neighbor Softmax. Specifically, the Landmark Agent Attention efficiently models the relation map between images by a low-rank factorization with a few landmarks in feature space. Moreover, the Reciprocal Neighbor Softmax achieves sparse attention to relevant-rather than all-neighbors only, which alleviates interference of irrelevant representations and further relieves the computational burden. In experiments on four large-scale datasets, NFormer achieves a new state-of-the-art. The code is released at https:// ***/haochenheheda/NFormer.

关键词： Representation learning Training computer vision Codes Computational modeling Focusing Interference

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