检索结果-内蒙古大学图书馆

OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer

学校读者我要写书评

暂无评论

OSRT: Omnidirectional Image Super-Resolution with Distortion...

Conference on computer vision and pattern recognition (CVPR)

作者： Fanghua Yu Xintao Wang Mingdeng Cao Gen Li Ying Shan Chao Dong ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ARC Tencent PCG The University of Tokyo Platform Technologies Tencent Online Video Shanghai Artificial Intelligence Laboratory

Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are insufficient. Previous methods attempt to solve this issue by image super-resolution (SR) on equirectangular projection (ERP) images. However, they omit geometric properties of ERP in the degradation process, and their models can hardly generalize to real ERP images. In this paper, we propose Fisheye downsampling, which mimics the real-world imaging process and synthesizes more realistic low-resolution samples. Then we design a distortion-aware Transformer (OSRT) to modulate ERP distortions continuously and self-adaptively. Without a cumbersome process, OSRT outperforms previous methods by about 0.2dB on PSNR. Moreover, we propose a convenient data augmentation strategy, which synthesizes pseudo ERP images from plain images. This simple strategy can alleviate the over-fitting problem of large networks and significantly boost the performance of ODISR. Extensive experiments have demonstrated the state-of-the-art performance of our OSRT.

关键词：

ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Mo, Ningkai Gan, Wanshui Yokoya, Naoto Chen, Shifeng ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The University of Tokyo Japan RIKEN Japan

In this paper, a computation efficient regression framework is presented for estimating the 6D pose of rigid objects from a single RGB-D image, which is applicable to handling symmetric objects. This framework is designed in a simple architecture that efficiently extracts point-wise features from RGB-D data using a fully convolutional network, called XYZNet, and directly regresses the 6D pose without any post refinement. In the case of symmetric object, one object has multiple ground-truth poses, and this one-to-many relationship may lead to estimation ambiguity. In order to solve this ambiguity problem, we design a symmetry-invariant pose distance metric, called average (maximum) grouped primitives distance or A(M)GPD. The proposed A(M)GPD loss can make the regression network converge to the correct state, i.e., all minima in the A(M)GPD loss surface are mapped to the correct poses. Extensive experiments on YCB-Video and TLESS datasets demonstrate the proposed framework's substantially superior performance in top accuracy and low computational cost. The relevant code is available in https://***/GANWANSHUI/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational efficiency

An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

学校读者我要写书评

暂无评论

IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

TIC: Text-Guided Image Colorization

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Ghosh, Subhankar Roy, Prasun Bhattacharya, Saumik Pal, Umapada Blumenstein, Michael Faculty of Engineering and IT University of Technology SydneyNSW Australia The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Indian Institute of Technology Kharagpur India

Image colorization is a well-known problem in computer vision. However, due to the ill-posed nature of the task, image colorization is inherently challenging. Though several attempts have been made by researchers to make the colorization pipeline automatic, these processes often produce unrealistic results due to a lack of conditioning. In this work, we attempt to integrate textual descriptions as an auxiliary condition, along with the grayscale image that is to be colorized, to improve the fidelity of the colorization process. To the best of our knowledge, this is one of the first attempts to incorporate textual conditioning in the colorization pipeline. To do so, we have proposed a novel deep network that takes two inputs (the grayscale image and the respective encoded text description) and tries to predict the relevant color gamut. As the respective textual descriptions contain color information of the objects present in the scene, the text encoding helps to improve the overall quality of the predicted colors. We have evaluated our proposed model using different metrics and found that it outperforms the state-of-the-art colorization algorithms both qualitatively and quantitatively. © 2022, CC BY.

关键词： Pipelines

ClueNet: A deep framework for occluded pedestrian pose estimation 30

学校读者我要写书评

暂无评论

ClueNet: A deep framework for occluded pedestrian pose estim...

30th British Machine vision Conference, BMVC 2019

作者： Kishore, Perla Sai Raj Das, Sudip Mukherjee, Partha Sarathi Bhattacharya, Ujjwal Institute of Engineering and Management Kolkata India Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata India

Pose estimation of a pedestrian helps to gather information about the current activity or the instant behaviour of the subject. Such information is useful for autonomous vehicles, augmented reality, video surveillance, etc. Although a large volume of pedestrian detection studies are available in the literature, detection of the same in situations of significant occlusions still remains a challenging task. In this work, we take a step further to propose a novel deep learning framework, called ClueNet, to detect as well as estimate the entire pose of occluded pedestrians in an unsupervised manner. ClueNet is a two stage framework where the first stage generates visual clues for the second stage to accurately estimate the pose of occluded pedestrians. The first stage employs a multi-task network to segment the visible parts and predict a bounding box enclosing the visible and occluded regions for each pedestrian. The second stage uses these predictions from the first stage for pose estimation. Here we propose a novel strategy, called Mask and Predict, to train our ClueNet to estimate the pose even for occluded regions. Additionally, we make use of various other training strategies to further improve our results. The proposed work is first of its kind and the experimental results on CityPersons and MS COCO datasets show the superior performance of our approach over existing methods. © 2019. The copyright of this document resides with its authors.

关键词： Augmented reality

WaveDM: Wavelet-Based Diffusion Models for Image Restoration

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Huang, Yi Huang, Jiancheng Liu, Jianzhuang Yan, Mingfu Dong, Yu Lyu, Jiaxi Chen, Chaoqi Chen, Shifeng Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100039 China The University of Hong Kong Hong Kong

Latest diffusion-based methods for many image restoration tasks outperform traditional models, but they encounter the long-time inference problem. To tackle it, this paper proposes a Wavelet-Based Diffusion Model (WaveDM). WaveDM learns the distribution of clean images in the wavelet domain conditioned on the wavelet spectrum of degraded images after wavelet transform, which is more time-saving in each step of sampling than modeling in the spatial domain. To ensure restoration performance, a unique training strategy is proposed where the low-frequency and high-frequency spectrums are learned using distinct modules. In addition, an Efficient Conditional Sampling (ECS) strategy is developed from experiments, which reduces the number of total sampling steps to around 5. Evaluations on twelve benchmark datasets including image raindrop removal, rain steaks removal, dehazing, defocus deblurring, demoiréing, and denoising demonstrate that WaveDM achieves state-of-the-art performance with the efficiency that is comparable to traditional one-pass methods and over 100× faster than existing image restoration methods using vanilla diffusion models. The code is available at https://***/stayalive16/WaveDM. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

MIO: Mutual Information Optimization using Self-Supervised Binary Contrastive Learning

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Manna, Siladittya Pal, Umapada Bhattacharya, Saumik Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India

Self-supervised contrastive learning frameworks have progressed rapidly over the last few years. In this paper, we propose a novel mutual information optimization-based loss function for contrastive learning. We model our pre-training task as a binary classification problem to induce an implicit contrastive effect and predict whether a pair is positive or negative. We further improve the näive loss function using the Majorize-Minimizer principle and such improvement helps us to track the problem mathematically. Unlike the existing methods, the proposed loss function optimizes the mutual information in both positive and negative pairs. We also present a closed-form expression for the parameter gradient flow and compare the behavior of the proposed loss function using its Hessian eigen-spectrum to analytically study the convergence of SSL frameworks. The proposed method outperforms the SOTA contrastive self-supervised frameworks on benchmark datasets like CIFAR-10, CIFAR-100, STL-10, and Tiny-ImageNet. After 200 epochs of pre-training with ResNet-18 as the backbone, the proposed model achieves an accuracy of 86.2%, 58.18%, 77.49%, and 30.87% on CIFAR-10, CIFAR-100, STL-10, and Tiny-ImageNet datasets, respectively, and surpasses the SOTA contrastive baseline by 1.23%, 3.57%, 2.00%, and 0.33%, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

Reconnoitering the class distinguishing abilities of the features, to know them better

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Sadhukhan, Payel Palit, Sarbani Sengupta, Kausik Institute for Advancing Intelligence Tcg Crest West Bengal Kolkata700091 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute West Bengal Kolkata700108 India

The relevance of machine learning (ML) in our daily lives is closely intertwined with its explainability. Explainability can allow end-users to have a transparent and humane reckoning of a ML scheme's capability and utility. It will also foster the user's confidence in the automated decisions of a system. Explaining the variables or features to explain a model's decision is a need of the present times. We could not really find any work, which explains the features on the basis of their class-distinguishing abilities (specially when the real world data are mostly of multi-class nature). In any given dataset, a feature is not equally good at making distinctions between the different possible categorizations (or classes) of the data points. In this work, we explain the features on the basis of their class or category-distinguishing capabilities. We particularly estimate the class-distinguishing capabilities (scores) of the variables for pair-wise class combinations. We validate the explainability given by our scheme empirically on several realworld, multi-class datasets. We further utilize the class-distinguishing scores in a latent feature context and propose a novel decision making protocol. Another novelty of this work lies with a refuse to render decision option when the latent variable (of the test point) has a high class-distinguishing potential for the likely classes. © 2022, CC BY.

关键词： Decision making

Antigen-specific antibody design via direct energy-based preference optimization 24

学校读者我要写书评

暂无评论

Antigen-specific antibody design via direct energy-based pre...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiangxin Zhou Dongyu Xue Ruizhe Chen Zaixiang Zheng Liang Wang Quanquan Gu School of Artificial Intelligence University of Chinese Academy of Sciences and New Laboratory of Pattern Recognition (NLPR) State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences (CASIA) and ByteDance Research ByteDance Research College of Computer Science and Electronic Engineering Hunan University and ByteDance Research School of Artificial Intelligence University of Chinese Academy of Sciences and New Laboratory of Pattern Recognition (NLPR) State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institute of Automation Chinese Academy of Sciences (CASIA)

ISBN: (纸本)9798331314385

Antibody design, a crucial task with significant implications across various disciplines such as therapeutics and biology, presents considerable challenges due to its intricate nature. In this paper, we tackle antigen-specific antibody sequence-structure co-design as an optimization problem towards specific preferences, considering both rationality and functionality. Leveraging a pre-trained conditional diffusion model that jointly models sequences and structures of antibodies with equivariant neural networks, we propose direct energy-based preference optimization to guide the generation of antibodies with both rational structures and considerable binding affinities to given antigens. Our method involves fine-tuning the pre-trained diffusion model using a residue-level decomposed energy preference. Additionally, we employ gradient surgery to address conflicts between various types of energy, such as attraction and repulsion. Experiments on RAbD benchmark show that our approach effectively optimizes the energy of generated antibodies and achieves state-of-the-art performance in designing high-quality antibodies with low total energy and high binding affinity simultaneously, demonstrating the superiority of our approach.

关键词：