Image-to-Image synthesis paradigms have been widely used for facial expression synthesis. However, current generators are apt to either produce artifacts for largely posed and non-aligned faces or unduly change the id...
Image-to-Image synthesis paradigms have been widely used for facial expression synthesis. However, current generators are apt to either produce artifacts for largely posed and non-aligned faces or unduly change the identity information like AdaIN-based generator. In this work, we suggest to use image style feature to surrogate the expression cues in the generator, and propose a multi-task learning paradigm to explore this style information via the supervision learning and feature disentanglement. While the supervision learning can make the encoded style specifically represent the expression cues and enable the generator to produce correct expression, the feature disentanglement of content and style cues enables the generator to better preserve the identity information in expression synthesis. Experimental results show that the proposed algorithm can well reduce the artifacts for the synthesis of posed and non-aligned expressions, and achieves competitive performances in terms of FID, PNSR and classification accuracy, compared with four publicly available GANs. The code and pre-trained models are available at https://***/lumanxi236/MTSS.
In automated feature-based motion analysis of multiple frames, correspondence data are usually noisy and fragmented. A technique that gradually refines the initial noisy correspondence data and links fragments of a si...
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The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance a...
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Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. Howeve...
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Discovering time profiled temporal patterns from time stamped transaction datasets is addressed in our previous research works which includes proposing new support estimation techniques, similarity measures for comput...
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Clinical decision-making relies heavily on accurate brain tumor diagnosis from MRI imaging. Manual interpretation is time-consuming and error-prone. To enhance efficiency, automated techniques using deep neural networ...
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We previously proposed a measurement framework for OpenFlow-based networks to promptly locate high-loss links with a small load incurred by the measurement on both the data-plane (e.g., the number of transmissions of ...
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With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public *** Re-ID meets challenge attributable to the large intra-class differences cause...
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With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public *** Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar *** existing methods focus on local attributes by marking local ***,these methods require additional annotations,resulting in complex algorithms and insufferable computation *** cope with these challenges,this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint *** model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it,then features are transferred to the deep layer by adjusting the corresponding weights,which reduces the transmission of redundant information in the process of feature reuse in *** the same time,the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ***,a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difcult-to-separate samples by enlarging the weight of the difcult-to-separate samples during the training *** results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5%and 94.8%,***,Rank-1 on small,medium and large sub-datasets of Vehicle ID dataset reach 81.3%,78.9%,and 76.5%,respectively,which surpasses most existing vehicle Re-ID methods.
Person re-identification (re-ID) gains plenty of achievements as a retrieval problem in constrained camera networks. However, most of the researches are concentrated on visual appearance, they still suffer from the co...
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This paper presented a new prediction model for Pressure-Volume-Temperature (PVT) properties based on the recently introduced learning algorithm called Sensitivity Based Linear Learning Method (SBLLM) for two-layer fe...
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