This study introduces CLIP-Flow,a novel network for generating images from a given image or *** effectively utilize the rich semantics contained in both modalities,we designed a semantics-guided methodology for image-...
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This study introduces CLIP-Flow,a novel network for generating images from a given image or *** effectively utilize the rich semantics contained in both modalities,we designed a semantics-guided methodology for image-and text-to-image *** particular,we adopted Contrastive Language-Image Pretraining(CLIP)as an encoder to extract semantics and StyleGAN as a decoder to generate images from such ***,to bridge the embedding space of CLIP and latent space of StyleGAN,real NVP is employed and modified with activation normalization and invertible *** the images and text in CLIP share the same representation space,text prompts can be fed directly into CLIP-Flow to achieve text-to-image *** conducted extensive experiments on several datasets to validate the effectiveness of the proposed image-to-image synthesis *** addition,we tested on the public dataset Multi-Modal CelebA-HQ,for text-to-image *** validated that our approach can generate high-quality text-matching images,and is comparable with state-of-the-art methods,both qualitatively and quantitatively.
In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar *** on the optimal quantizer of binary-input discrete memoryless ...
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In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar *** on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized *** nested structure of polar codes ensures that the MMI quantization can be implemented stage by *** results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization ***,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.
Images are used widely nowadays. Images are used in many fields such as medicine to terrain mapping. There is a need to compress the images and represent them in shorter form for effective transmission. Several techni...
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Multi-document summarising (MDS) is a helpful method for information aggregation that creates a clear and informative summary from a collection of papers linked to the same subject. Due to the significant number of in...
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Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliabi...
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Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliability. Metamorphic testing (MT) enhances reliability by generating follow-up tests from mutated DNN source inputs, identifying inconsistencies as defects. Various MT techniques for ADSs include generative/transfer models, neuron-based coverage maximization, and adaptive test selection. Despite these efforts, significant challenges remain, including the ambiguity of neuron coverage’s correlation with misbehaviour detection, a lack of focus on DNN critical pathways, inadequate use of search-based methods, and the absence of an integrated method that effectively selects sources and generates follow-ups. This paper addresses such challenges by introducing DeepDomain, a grey-box multi-objective test generation approach for DNN models. It involves adaptively selecting diverse source inputs and generating domain-oriented follow-up tests. Such follow-ups explore critical pathways, extracted by neuron contribution, with broader coverage compared to their source tests (inter-behavioural domain) and attaining high neural boundary coverage of the misbehaviour regions detected in previous follow-ups (intra-behavioural domain). An empirical evaluation of the proposed approach on three DNN models used in the Udacity self-driving car challenge, and 18 different MRs demonstrates that relying on behavioural domain adequacy is a more reliable indicator than coverage criteria for effectively guiding the testing of DNNs. Additionally, DeepDomain significantly outperforms selected baselines in misbehaviour detection by up to 94 times, fault-revealing capability by up to 79%, output diversity by 71%, corner-case detection by up to 187 times, identification of robustness subdomains of MRs by up to 33 percentage points, and naturalness by two times. The results confirm that stat
Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).I...
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Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure *** paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure *** constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse *** the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are *** is evaluated,and the data results demonstrate that SCD achieves the above objectives.
The advancement of computer vision and surveillance devices has underscored the significance of safeguarding privacy in facial images and videos, necessitating the development of effective face de-identification metho...
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Mobile networks possess significant information and thus are considered a gold mine for the researcher’s *** call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile use...
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Mobile networks possess significant information and thus are considered a gold mine for the researcher’s *** call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile user’s *** is evident from the recent literature that cyber-physical systems(CPS)were used in the analytics and modeling of telecom *** addition,CPS is used to provide valuable services in smart *** general,a typical telecom company hasmillions of subscribers and thus generatesmassive amounts of *** this aspect,data storage,analysis,and processing are the key *** solve these issues,herein we propose a multilevel cyber-physical social system(CPSS)for the analysis and modeling of large internet *** proposed multilevel system has three levels and each level has a specific ***,raw Call Detail Data(CDR)was collected at the first ***,the data preprocessing,cleaning,and error removal operations were *** the second level,data processing,cleaning,reduction,integration,processing,and storage were ***,suggested internet activity record measures were *** proposed system initially constructs a graph and then performs network *** proposed CPSS system accurately identifies different areas of internet peak usage in a city(Milan city).Our research is helpful for the network operators to plan effective network configuration,management,and optimization of resources.
Pedestrian re-identification technology enables accurate identification of individuals and is widely used in modern intelligent video surveillance systems to aid law enforcement, including criminal apprehension and lo...
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Person Re-Identification falls within the scope of computer vision, acting a technique to ascertain the presence of a specified pedestrian within a video or image library. The related research is of great significance...
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