Cover Image

DeCLIP: Decoupled Prompting for CLIP-based Multi-Label Class-Incremental Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Du, Kaile, Ye, Zihan, Xie, Junzhou, Shen, Yixi, Li, Yuyang, Hu, Fuyuan, Shao, Ling, Liu, Guangcan, van de Weijer, Joost, Lyu, Fan
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Rebalancing Multi-Label Class-Incremental Learning
by: Du, Kaile, et al.
Published: (2024)

Confidence Self-Calibration for Multi-Label Class-Incremental Learning
by: Du, Kaile, et al.
Published: (2024)

DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception
by: Wang, Junjie, et al.
Published: (2025)

DeCLIP: Decoding CLIP representations for deepfake localization
by: Smeu, Stefan, et al.
Published: (2024)

CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning
by: Liu, Chengyan, et al.
Published: (2024)

IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment
by: Magistri, Simone, et al.
Published: (2026)

Variational Continual Test-Time Adaptation
by: Lyu, Fan, et al.
Published: (2024)

Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers
by: Goswami, Dipam, et al.
Published: (2024)

Hierarchical Representation Matching for CLIP-based Class-Incremental Learning
by: Wen, Zhen-Hao, et al.
Published: (2025)

External Knowledge Injection for CLIP-Based Class-Incremental Learning
by: Zhou, Da-Wei, et al.
Published: (2025)

Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning
by: Menghini, Cristina, et al.
Published: (2023)

Understanding Model Reprogramming for CLIP via Decoupling Visual Prompts
by: Cai, Chengyi, et al.
Published: (2025)

AREA: Attribute Extraction and Aggregation for CLIP-Based Class-Incremental Learning
by: Xie, Zhen-Hao, et al.
Published: (2026)

Hierarchical Semantic Tree Anchoring for CLIP-Based Class-Incremental Learning
by: Hu, Tao, et al.
Published: (2025)

FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
by: Goswami, Dipam, et al.
Published: (2023)

Inductive Convolution Nuclear Norm Minimization for Tensor Completion with Arbitrary Sampling
by: Li, Wei, et al.
Published: (2026)

MoP-CLIP: A Mixture of Prompt-Tuned CLIP Models for Domain Incremental Learning
by: Nicolas, Julien, et al.
Published: (2023)

Adaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning
by: Guo, Juncen, et al.
Published: (2025)

Continual Learning on CLIP via Incremental Prompt Tuning with Intrinsic Textual Anchors
by: Lu, Haodong, et al.
Published: (2025)

Incremental Object Detection with CLIP
by: Huang, Ziyue, et al.
Published: (2023)

Class-Incremental Learning with CLIP: Adaptive Representation Adjustment and Parameter Fusion
by: Huang, Linlan, et al.
Published: (2024)

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning
by: Sun, Hao, et al.
Published: (2026)

Preserving Cross-Modal Consistency for CLIP-based Class-Incremental Learning
by: Chen, Haoran, et al.
Published: (2025)

Learning Generalizable Prompt for CLIP with Class Similarity Knowledge
by: Jung, Sehun, et al.
Published: (2025)

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning
by: Li, Lan, et al.
Published: (2025)

MedP-CLIP: Medical CLIP with Region-Aware Prompt Integration
by: Peng, Jiahui, et al.
Published: (2026)

C3Box: A CLIP-based Class-Incremental Learning Toolbox
by: Sun, Hao, et al.
Published: (2026)

AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
by: Cao, Yunkang, et al.
Published: (2024)

ZeroDiff++: Substantial Unseen Visual-semantic Correlation in Zero-shot Learning
by: Ye, Zihan, et al.
Published: (2026)

Control-CLIP: Decoupling Category and Style Guidance in CLIP for Specific-Domain Generation
by: Jia, Zexi, et al.
Published: (2025)

ChordPrompt: Orchestrating Cross-Modal Prompt Synergy for Multi-Domain Incremental Learning in CLIP
by: Wang, Zhiyuan, et al.
Published: (2025)

AD-CLIP: Adapting Domains in Prompt Space Using CLIP
by: Singha, Mainak, et al.
Published: (2023)

VTD-CLIP: Video-to-Text Discretization via Prompting CLIP
by: Zhu, Wencheng, et al.
Published: (2025)

LVP-CLIP:Revisiting CLIP for Continual Learning with Label Vector Pool
by: Ma, Yue, et al.
Published: (2024)

GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning
by: Wang, Tianqi, et al.
Published: (2026)

Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning
by: Goswami, Dipam, et al.
Published: (2024)

Neural Antidote: Class-Wise Prompt Tuning for Purifying Backdoors in CLIP
by: Kong, Jiawei, et al.
Published: (2025)

BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
by: Bai, Jiawang, et al.
Published: (2023)

GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection
by: Kim, Donghyeong, et al.
Published: (2025)

AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning
by: Wang, Runqi, et al.
Published: (2023)