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Found 8 papers, 1 papers with code
Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images
To address these issues, in this paper, we develop a novel Breast Ultrasound SAM Adapter, termed Breast Ultrasound Segment Anything Model (BUSSAM), which migrates the SAM to the field of breast ultrasound image segmentation by using the adapter technique.
VcT: Visual change Transformer for Remote Sensing Image Change Detection
Then, each pixel of feature map is regarded as a graph node and the graph neural network is proposed to model the structured information for coarse change map prediction.
Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer based Approach
(1) It employs a flexible graph model, termed Batch Graph to jointly encode the visual and semantic relationships of samples within each mini-batch.
Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification
sampleFormer aims to capture the dependence of samples in support and query sets for image representation.
MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention
In this work, we re-think Transformer and extend it to MutualFormer for multi-modality data representation.
AttKGCN: Attribute Knowledge Graph Convolutional Network for Person Re-identification
Discriminative feature representation of person image is important for person re-identification (Re-ID) task.
PH-GCN: Person Re-identification with Part-based Hierarchical Graph Convolutional Network
Given a person image, PH-GCN first constructs a hierarchical graph to represent the pairwise relationships among different parts.
A Framework for Exploring Non-Linear Functional Connectivity and Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric Clustering
We present a computational framework for analysis and visualization of non-linear functional connectivity in the human brain from resting state functional MRI (fMRI) data for purposes of recovering the underlying network community structure and exploring causality between network components.
