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Found 9 papers, 7 papers with code
VQ-NeRV: A Vector Quantized Neural Representation for Videos
This block incorporates a codebook mechanism to discretize the network's shallow residual features and inter-frame residual information effectively.
PE-MVCNet: Multi-view and Cross-modal Fusion Network for Pulmonary Embolism Prediction
In a clinical setting, physicians tend to rely on the contextual information provided by Electronic Medical Records (EMR) to interpret medical imaging.
Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo Labeling Leveraging Strong and Weak Data Augmentation Strategies
Traditional supervised learning methods have historically encountered certain constraints in medical image segmentation due to the challenging collection process, high labeling cost, low signal-to-noise ratio, and complex features characterizing biomedical images.
LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution
Given the broad application of infrared technology across diverse fields, there is an increasing emphasis on investigating super-resolution techniques for infrared images within the realm of deep learning.
Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases
To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR).
SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentation
These findings demonstrate that our method exhibits strong performance in PE segmentation tasks, potentially enhancing the accuracy of automatic segmentation of PE and providing a powerful diagnostic tool for clinical physicians.
ZS-SRT: An Efficient Zero-Shot Super-Resolution Training Method for Neural Radiance Fields
This framework aims to guide the NeRF model to synthesize high-resolution novel views via single-scene internal learning rather than requiring any external high-resolution training data.
CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data
We innovatively propose a cross distillation module to provide additional supervision to alleviate the noise problem, and propose a collaborative ensemble module to overcome the target conflict problem.
Recurrent Neural Network from Adder's Perspective: Carry-lookahead RNN
The recurrent network architecture is a widely used model in sequence modeling, but its serial dependency hinders the computation parallelization, which makes the operation inefficient.
