Incremental Learning

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13 papers in the last four weeks, up 63% on the four weeks before. 0.1% of all new papers.

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ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow
Apr 3, 2026Jiekai Wu, Rong Fu, Chuangqi Li +10Class-Incremental Learning Image Segmentation
Independent Researcher · The Institute of Collaborative Innovation, University of Macau, Macau 999078, China · Department of Information and Computing Sciences, Faculty of Science, Utrecht University, Utrecht 3584 CC, Netherlands +10
Incremental Learning of Sparse Attention Patterns in Transformers
Feb 22, 2026Oğuz Kaan Yüksel, Rodrigo Alvarez Lucendo, Nicolas FlammarionSelf-Attention Transformer Attention
TML Lab, EPFL
BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning
Nov 14, 2025Lan Li, Tao Hu, Da-Wei Zhou +3Class-Incremental Learning VLM Adaptation
School of Artificial Intelligence, Nanjing University, China · National Key Laboratory for Novel Software Technology, Nanjing University, China
Mixtures of SubExperts for Large Language Continual Learning
Nov 9, 2025Haeyong Kang, Hee Suk Yoon, Dahua Feng +1Continual Learning for LLMs Mixture-of-Experts Language Models
Korea Advanced Institute of Science and Technology (KAIST)
HERMAN: Hierarchical Representation Matching for CLIP-based Class-Incremental Learning
Sep 26, 2025Zhen-Hao Xie, Yan Wang, Lan Li +3Class-Incremental Learning Vision-Language Models
School of Artificial Intelligence and the State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
The Bayesian Approach to Continual Learning: An Overview
Jul 11, 2025Tameem AdelClass-Incremental Learning Continual Learning
NPL, Maxwell Centre, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 OHE, United Kingdom.
TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree
Jun 12, 2025Yu-Yang Qian, Yuan-Ze Xu, Zhen-Yu Zhang +2Continual Learning for LLMs Low-Rank Adaptation
National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · RIKEN AIP, Tokyo, Japan
SEAL: Searching Expandable Architectures for Incremental Learning
May 15, 2025Matteo Gambella, Manuel RoveriDynamic Neural Networks Continual Learning
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning
Mar 13, 2025Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais +2Class-Incremental Learning Prototype-Based Classification
Media Integration and Communication Center (MICC), University of Florence, Italy · Global Optimization Laboratory, University of Florence, Italy · LAMP Team, Computer Vision Center, Barcelona, Spain +1
AIR: Analytic Imbalance Rectifier for Continual Learning
Aug 19, 2024Di Fang, Yinan Zhu, Zhiping Lin +3Class-Imbalanced Learning Class-Incremental Learning
School of Future Technology, South China University of Technology, Guangzhou, China · Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning
Jul 8, 2024Xiaojie Li, Yibo Yang, Jianlong Wu +4Class-Incremental Learning Incremental Learning
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · King Abdullah University of Science and Technology, Jeddah, Saudi Arabia +1
Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor
Jul 31, 2022Caleb Princewill NwokochaInterpretable ML Incremental Learning
DRDN: Decoupled Representation Dynamic Network for From-Scratch ViT Class-Incremental Learning
Date pendingBingchen Huang, Yifu Chen, Zhiling Wang +1Class-Incremental Learning Vision Transformer
Meituan Vision AI Department, Beijing 100190, China

Apr 3, 2026, cs.CVJ/K move · Enter open · S save

ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow

Jiekai Wu, Rong Fu, Chuangqi Li, Zijian Zhang+9

Independent Researcher · The Institute of Collaborative Innovation, University of Macau, Macau 999078, China · Department of Information and Computing Sciences, Faculty of Science, Utrecht University, Utrecht 3584 CC, Netherlands+10

Class-Incremental Learning Image Segmentation Remote Sensing Image Segmentation Prototype Learning Domain-Incremental Learning Remote Sensing+2

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Remote sensing segmentation in real deployment is inherently continual: new semantic categories emerge, and acquisition conditions shift across seasons, cities, and sensors. Despite recent progress, many incremental approaches still treat training steps as isolated updates, which leaves representation drift and forgetting insufficiently controlled. We present ProtoFlow, a time-aware prototype dynamics framework that models class prototypes as trajectories and learns their evolution with an explicit temporal vector field. By jointly enforcing low-curvature motion and inter-class separation, ProtoFlow stabilizes prototype geometry throughout incremental learning. Experiments on standard class- and domain-incremental remote sensing benchmarks show consistent gains over strong baselines, including up to 1.5-2.0 points improvement in mIoUall, together with reduced forgetting. These results suggest that explicitly modeling temporal prototype evolution is a practical and interpretable strategy for robust continual remote sensing segmentation. Open-source code:https://github.com/dudududke/protoflow.

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