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Found 9 papers, 0 papers with code
Unearthing Common Inconsistency for Generalisable Deepfake Detection
While current image-level detection method fails to generalize to unseen domains, owing to the domain-shift phenomenon brought by CNN's strong inductive bias towards Deepfake texture, video-level one shows its potential to have both generalization across multiple domains and robustness to compression.
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
This marks the first time that ViT has been introduced to diffusion autoencoders in computational pathology, allowing the model to better capture the complex and intricate details of histopathology images.
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
We propose a novel approach to learn enhanced modality-agnostic representations by employing a meta-learning strategy in training, even when only limited full modality samples are available.
Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations
In our method, a self-attention based Temporal Convolutional Network (TCN) is used to learn a representation that is most reflective of the disease trajectory.
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer
Image-based brain cancer prediction models, based on radiomics, quantify the radiologic phenotype from magnetic resonance imaging (MRI).
Large-Scale Data Mining of Rapid Residue Detection Assay Data From HTML and PDF Documents: Improving Data Access and Visualization for Veterinarians
Active ingredients, sensitivity, matrices, and species that have been evaluated for commercial rapid assay tests are typically reported on manufacturers' websites or in PDF documents that are available to consumers but may require a special access request.
Deformable Kernel Convolutional Network for Video Extreme Super-Resolution
Thanks to newly designed Deformable Kernel Convolution Alignment (DKC_Align) and Deformable Kernel Spatial Attention (DKSA) modules, DKSAN can better exploit both spatial and temporal redundancies to facilitate the information propagation across different layers.
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details.
Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization
Image demosaicing and super-resolution are two important tasks in color imaging pipeline.
