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Found 9 papers, 2 papers with code
Magnetic Resonance Image Processing Transformer for General Reconstruction
Result: The MR-IPT model provided superior performance in multiple downstream tasks compared to conventional CNN networks.
Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference
By incorporating relevant visual information and leveraging linguistic knowledge, our approach bridges the gap between language and vision, leading to improved understanding and inference capabilities in NLI tasks.
Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification
Compared with the previous learning paradigm of pre-training and fine-tuning by cross entropy loss, the recently proposed supervised contrastive learning approach has received tremendous attention due to its powerful feature learning capability and robustness.
Region-based U-net for accelerated training and enhanced precision in deep brain segmentation
Segmentation of brain structures on MRI is the primary step for further quantitative analysis of brain diseases.
K-space Cold Diffusion: Learning to Reconstruct Accelerated MRI without Noise
In this paper, we propose a k-space cold diffusion model that performs image degradation and restoration in k-space without the need for Gaussian noise.
Attention Hybrid Variational Net for Accelerated MRI Reconstruction
However, the drawback of these structures is that they are not fully utilizing the information from both domains (k-space and image).
Importance Sparsification for Sinkhorn Algorithm
Sinkhorn algorithm has been used pervasively to approximate the solution to optimal transport (OT) and unbalanced optimal transport (UOT) problems.
Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification
As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs.
Single Image based Head Pose Estimation with Spherical Parameterization and 3D Morphing
Specifically, the rectangular coordinates of only four non-coplanar feature points from a predefined 3D facial model as well as the corresponding ones automatically/ manually extracted from a 2D face image are first normalized to exclude the effect of external factors (i. e., scale factor and translation parameters).
