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- Recognition of ischaemia and infection in diabetic foot ulcers: Dataset and techniques233
This work proposes a new dataset and computer vision techniques to identify the presence of infection and ischaemia in DFU, and proposes to use a natural data-augmentation method, which identifies the region of interest on foot images and focuses on finding the salient features existing in this area.
- Content-Aware Unsupervised Deep Homography Estimation204
This work proposes an unsupervised deep homography method with a new architecture design that outperforms the state-of-the-art including deep solutions and feature-based solutions.
- Video Inpainting by Jointly Learning Temporal Structure and Spatial Details188
A novel deep learning architecture is proposed which contains two subnetworks: a temporal structure inference network and a spatial detail recovering network, which jointly trains both sub-networks in an end-to-end manner.
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- UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning96
This work designs a self-guided upsample module to tackle the interpolation blur problem caused by bilinear upsampling between pyramid levels, and proposes a pyramid distillation loss to add supervision for intermediate levels via distilling the finest flow as pseudo labels.
- Motion Basis Learning for Unsupervised Deep Homography Estimation with Subspace Projection79
A new framework for unsupervised deep homography estimation is introduced and results show that this approach outperforms the state-of-the-art on the homography benchmark datasets both qualitatively and quantitatively.
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- UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging With Unpaired Data46
Although the pair data are missing, UPHDR-GAN can properly handle the ghosting artifacts caused by moving objects or misalignments with the help of the modified GAN loss, the improved discriminator network and the useful initialization phase.
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- Changes in Gut Microbiota Structure: A Potential Pathway for Silver Nanoparticles to Affect the Host Metabolism32
The results of the present study demonstrate the importance of considering the roles of GM in the toxicity of NPs to the host in evaluations of the health risks ofNPs.
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- Semi-Supervised Skin Detection by Network With Mutual Guidance25
Experimental results show the effectiveness of the proposed dual-task neural network for joint detection of skin and body via a semi-supervised learning strategy outperforms the state-of-the-art in skin detection.
- ASFlow: Unsupervised Optical Flow Learning With Adaptive Pyramid Sampling23
This work presents an unsupervised optical flow estimation method by proposing an adaptive pyramid sampling in the deep pyramid network, and proposes a Content-Aware Pooling module, which promotes local feature gathering by avoiding cross region pooling, so that the learned features become more representative.
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- EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information20
EpiScan can expedite the epitope mapping process for high-throughput antibody sequencing data, supporting vaccine design and drug development, and it is shown that EpiScan, which only uses antibody sequence information, can accurately map epitopes on specific antigen structures.
- Sparse Representation Theory and its Application for Face Recognition13
Experimental results show that face recognition method based on sparse representation is comparable to others, and any test sample can be expressed as a linear span of the all training samples.
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- Automatic segmentation method for bone and blood vessel in murine hindlimb10
The proposed split Bregman method improves the distinguishability between bones and blood vessels, since both the intensity information and the geometrical size are exploited and eliminates the false positive effect on the nonvascular sharp boundaries.
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- Potential effects of Ag ion on the host by changing the structure of its gut microbiota5
Overall, the results demonstrate potential effects of Ag on the host, by changing its GM structure, and the need to consider them when evaluating the health risk of Ag.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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