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Found 6 papers, 2 papers with code
DmADs-Net: Dense multiscale attention and depth-supervised network for medical image segmentation
In addition, in the feature fusion phase, a Feature Refinement and Fusion Block is created to enhance the fusion of different semantic information. We validated the performance of the network using five datasets of varying sizes and types.
Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification
Despite this, some studies have directly used CLIP's image encoder for tasks like few-shot classification, introducing a misalignment between its pre-training objectives and feature extraction methods.
ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse
The rapid expansion of foundation pre-trained models and their fine-tuned counterparts has significantly contributed to the advancement of machine learning.
The Kernel Beta Process
A new Le ́vy process prior is proposed for an uncountable collection of covariate- dependent feature-learning measures; the model is called the kernel beta process (KBP).
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation
The model clusters the images into classes, and each image is segmented into a set of objects, also allowing the opportunity to assign a word to each object (localized labeling).
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations
The beta process is employed as a prior for learning the dictionary, and this non-parametric method naturally infers an appropriate dictionary size.
