# Convolutional Prompting meets Language Models for Continual Learning

arXiv: https://arxiv.org/abs/2403.20317 · HTML: https://mlsanity.com/paper/2403.20317/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 58 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 30 of 45)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [Learning to Prompt for Continual Learning](https://mlsanity.com/paper/2112.08654/) (2021)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Lifelong Learning with Dynamically Expandable Networks](https://mlsanity.com/paper/1708.01547/) (2017)
- [DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning](https://mlsanity.com/paper/2204.04799/) (2022)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [Gradient based sample selection for online continual learning](https://mlsanity.com/paper/1903.08671/) (2019)
- [CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning](https://mlsanity.com/paper/2211.13218/) (2022)
- [Orthogonal Gradient Descent for Continual Learning](https://mlsanity.com/paper/1910.07104/) (2019)
- [Gradient Projection Memory for Continual Learning](https://mlsanity.com/paper/2103.09762/) (2021)
- [Visual Classification via Description from Large Language Models](https://mlsanity.com/paper/2210.07183/) (2022)
- [On Tiny Episodic Memories in Continual Learning](https://mlsanity.com/paper/1902.10486/) (2019)
- [DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion](https://mlsanity.com/paper/2111.11326/) (2021)
- [Rainbow Memory: Continual Learning with a Memory of Diverse Samples](https://mlsanity.com/paper/2103.17230/) (2021)
- [S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning](https://mlsanity.com/paper/2207.12819/) (2022)
- [Overcoming Catastrophic Forgetting by Incremental Moment Matching](https://mlsanity.com/paper/1703.08475/) (2017)
- [Selective Experience Replay for Lifelong Learning](https://mlsanity.com/paper/1802.10269/) (2018)
- [Task-Free Continual Learning](https://mlsanity.com/paper/1812.03596/) (2018)
- [REMIND Your Neural Network to Prevent Catastrophic Forgetting](https://mlsanity.com/paper/1910.02509/) (2019)
- [Progressive Prompts: Continual Learning for Language Models](https://mlsanity.com/paper/2301.12314/) (2023)

## Built on by (most cited first, 30 of 32)

- [Towards Lifelong Learning of Large Language Models: A Survey](https://mlsanity.com/paper/2406.06391/) (2024)
- [CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning](https://mlsanity.com/paper/2505.24816/) (2025)
- [Dynamic Integration of Task-Specific Adapters for Class Incremental Learning](https://mlsanity.com/paper/2409.14983/) (2024)
- [RainbowPrompt: Diversity-Enhanced Prompt-Evolving for Continual Learning](https://mlsanity.com/paper/2507.22553/) (2025)
- [CAPrompt: Cyclic Prompt Aggregation for Pre-Trained Model Based Class Incremental Learning](https://mlsanity.com/paper/2412.08929/) (2024)
- [LoRanPAC: Low-rank Random Features and Pre-trained Models for Bridging Theory and Practice in Continual Learning](https://mlsanity.com/paper/2410.00645/) (2024)
- [AnaCP: Toward Upper-Bound Continual Learning via Analytic Contrastive Projection](https://mlsanity.com/paper/2511.13880/) (2025)
- [Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning](https://mlsanity.com/paper/2408.01076/) (2024)
- [EKPC: Elastic Knowledge Preservation and Compensation for Class-Incremental Learning](https://mlsanity.com/paper/2506.12351/) (2025)
- [CKAA: Cross-subspace Knowledge Alignment and Aggregation for Robust Continual Learning](https://mlsanity.com/paper/2507.09471/) (2025)
- [Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning](https://mlsanity.com/paper/2411.15469/) (2024)
- [Parameter Efficient Continual Learning with Dynamic Low-Rank Adaptation](https://mlsanity.com/paper/2505.11998/) (2025)
- [Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need](https://mlsanity.com/paper/2406.03216/) (2024)
- [REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning](https://mlsanity.com/paper/2406.04772/) (2024)
- [Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning](https://mlsanity.com/paper/2412.01004/) (2024)
- [Distribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identification](https://mlsanity.com/paper/2504.15041/) (2025)
- [One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning](https://mlsanity.com/paper/2509.24483/) (2025)
- [Unsupervised Few-Shot Continual Learning for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2406.18574/) (2024)
- [Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning](https://mlsanity.com/paper/2503.18403/) (2025)
- [LoRA-Based Continual Learning with Constraints on Critical Parameter Changes](https://mlsanity.com/paper/2504.13407/) (2025)
- [PROL : Rehearsal Free Continual Learning in Streaming Data via Prompt Online Learning](https://mlsanity.com/paper/2507.12305/) (2025)
- [Is Parameter Isolation Better for Prompt-Based Continual Learning?](https://mlsanity.com/paper/2601.20894/) (2026)
- [Is Prompt Selection Necessary for Task-Free Online Continual Learning?](https://mlsanity.com/paper/2604.04420/) (2026)
- [AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection](https://mlsanity.com/paper/2604.16207/) (2026)
- [ReFu: Recursive Fusion for Exemplar-Free 3D Class-Incremental Learning](https://mlsanity.com/paper/2409.12326/) (2024)
- [From Offline to Online Memory-Free and Task-Free Continual Learning via Fine-Grained Hypergradients](https://mlsanity.com/paper/2502.18762/) (2025)
- [Adaptive Model Ensemble for Continual Learning](https://mlsanity.com/paper/2509.19819/) (2025)
- [Teaching Prompts to Coordinate: Hierarchical Layer-Grouped Prompt Tuning for Continual Learning](https://mlsanity.com/paper/2511.12090/) (2025)
- [Advancing Analytic Class-Incremental Learning through Vision-Language Calibration](https://mlsanity.com/paper/2602.13670/) (2026)
- [Beyond Prompt Degradation: Prototype-guided Dual-pool Prompting for Incremental Object Detection](https://mlsanity.com/paper/2603.02286/) (2026)

## Abstract (arXiv)

Continual Learning (CL) enables machine learning models to learn from continuously shifting new training data in absence of data from old tasks. Recently, pretrained vision transformers combined with prompt tuning have shown promise for overcoming catastrophic forgetting in CL. These approaches rely on a pool of learnable prompts which can be inefficient in sharing knowledge across tasks leading to inferior performance. In addition, the lack of fine-grained layer specific prompts does not allow these to fully express the strength of the prompts for CL. We address these limitations by proposing ConvPrompt, a novel convolutional prompt creation mechanism that maintains layer-wise shared embeddings, enabling both layer-specific learning and better concept transfer across tasks. The intelligent use of convolution enables us to maintain a low parameter overhead without compromising performance. We further leverage Large Language Models to generate fine-grained text descriptions of each category which are used to get task similarity and dynamically decide the number of prompts to be learned. Extensive experiments demonstrate the superiority of ConvPrompt and improves SOTA by ~3% with significantly less parameter overhead. We also perform strong ablation over various modules to disentangle the importance of different components.