Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Toward Generative Understanding: Incremental Few-Shot Semantic Segmentation With Diffusion Models.

Qun Li, Lu Huang, Fu Xiao

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 14, 2026

Summary

This summary is machine-generated.

Related Concept Videos

Diffusion

Diffusion

Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Hardware-enhanced data security for Internet of Things.

Fundamental research·2026

Same author

Structural insights into single-pass transmembrane receptor GC-A activation by distinct antihypertensive antibodies.

Nature communications·2026

Same author

Prediction accuracy of ligand binding kinetics.

Biophysical journal·2026

Same author

Uncertainty-Guided Iterative Contrastive Fusion for Reliable Survival Prediction in Rectal Cancer.

IEEE transactions on medical imaging·2026

Same author

Association between composite dietary antioxidant index and <i>Helicobacter pylori</i> infection: a population-based and Mendelian randomisation study.

The British journal of nutrition·2025

Same author

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Journal of chemical information and modeling·2025

Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

This study introduces a novel diffusion-based generative framework for incremental few-shot semantic segmentation (iFSS). The method effectively learns new classes without forgetting old ones, outperforming existing approaches with less data.

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Incremental Few-shot Semantic Segmentation (iFSS) addresses continual learning challenges, including catastrophic forgetting of base classes when learning new ones.
Existing iFSS methods struggle with feature drift and limited generalization due to bidirectional coupling bottlenecks.
Current techniques like knowledge distillation and background learning show partial effectiveness but require improvement.

Purpose of the Study:

To propose a novel diffusion-based generative framework for iFSS.
To overcome limitations of existing methods, specifically feature drift and catastrophic forgetting.
To enable efficient and effective learning of novel classes with limited data while preserving base class knowledge.

Main Methods:

Related Experiment Videos

Last Updated: Jan 17, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A diffusion-based generative framework is introduced, bridging generative and discriminative tasks.
An innovative binary-to-RGB mask mapping mechanism utilizes pre-trained diffusion models.
Class-specific semantic embedding optimization and color embeddings focus on target regions and enhance contrast, followed by a lightweight post-processor for mask refinement.

Main Results:

The proposed framework achieves state-of-the-art performance on PASCAL-5ⁱ and COCO-20ⁱ datasets.
It requires significantly less data compared to existing methods.
Demonstrates competitive results in cross-domain few-shot segmentation tasks, showcasing strong generalization capabilities.

Conclusions:

The diffusion-based framework effectively addresses catastrophic forgetting and feature drift in iFSS.
Leveraging diffusion priors and optimized embeddings enables rapid novel-class adaptation and robust performance.
This approach offers a promising direction for continual learning in semantic segmentation.