Visual Branch is What You Need for CLIP-based Class-Incremental Learning

arXiv:2609.37888 · cs.CV, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-30

Terminology

• Accuracy — 66× in this paper · explained in 1 episode
• Modality Gap — 21× in this paper · explained in 4 episodes
• Class-Incremental Learning — 19× in this paper
• Continual Learning — 19× in this paper · explained in 2 episodes
• Embeddings — 16× in this paper · explained in 2 episodes
• Adaptation — 15× in this paper · explained in 1 episode
• Robustness — 11× in this paper · explained in 5 episodes
• Regularization — 10× in this paper · explained in 3 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Generalization — 6× in this paper · explained in 13 episodes
• Vision-language models — 6× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 5× in this paper · explained in 5 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Visual Incremental SVM — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes

Sources

• Efficient Lifelong Learning with A-GEM
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• What do Vision Transformers Learn? A Visual Exploration
• Distilling the Knowledge in a Neural Network
• Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts
• Class Incremental Learning with Pre-trained Vision-Language Models
• Continual Learning on CLIP via Incremental Prompt Tuning with Intrinsic Textual Anchors
• Fine-Grained Visual Classification of Aircraft
• DINOv2: Learning Robust Visual Features without Supervision
• UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
• C3Box: A CLIP-based Class-Incremental Learning Toolbox
• SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning
• Lifelong Learning with Dynamically Expandable Networks

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