Prompt-based class-incremental learning methods typically construct a prompt pool consisting of multiple trainable key-prompts and perform instance-level matching to select the most suitable prompt embeddings, which has shown promising results. However, existing approaches face several limitations, including fixed prompt pools, manual selection of prompt embeddings, and strong reliance on the pretrained backbone for prompt selection. To address these issues, we propose a Layer-importance guided Dual Expandable Prompt Pool (LDEPrompt), which enables adaptive layer selection as well as dynamic freezing and expansion of the prompt pool. Extensive experiments on widely used class-incremental learning benchmarks demonstrate that LDEPrompt achieves state-of-the-art performance, validating its effectiveness and scalability.
Figures & tables
Figure 1: Illustration of LDEPrompt. The upper part (from left to right) shows: (1) the forward-pass evaluation of layer importance before training; (2) task-specific prompt retrieval from the global prompt pool at layer K using the CLS token; and (3) the construction of a layer-specific prompt pool of size n , where selected prompts are frozen while newly initialized prompts remain trainable;(4) During training, prompts within each layer are merged to form the final task-specific representations.
Method
CIFAR B0-Inc10
CUB B0-Inc20
VTAB B0-Inc10
Avg( ↑ )
Last( ↑ )
Avg( ↑ )
Last( ↑ )
Avg( ↑ )
Last( ↑ )
L2P [ 17 ]
88.83 ± 1.39
83.46 ± 2.00
77.21 ± 0.91
68.22 ± 0.84
78.71 ± 6.22
60.57 ± 7.60
Dualprompt [ 16 ]
89.39 ± 1.12
84.58 ± 0.77
84.18 ± 0.49
74.27 ± 0.49
88.59 ± 2.28
77.64 ± 3.33
CODA-Prompt [ 12 ]
90.60 ± 0.33
87.01 ± 0.19
83.89 ± 1.52
75.33 ± 1.06
88.16 ± 2.10
76.80 ± 2.69
Aper-VPT-S [ 19 ]
85.20 ± 0.43
79.77 ± 0.45
90.21 ± 1.42
86.23 ± 0.21
91.21 ± 0.46
83.78 ± 0.60
Aper-VPT-D [ 19 ]
86.26 ± 0.79
80.28 ± 0.87
90.06 ± 1.35
86.12 ± 0.11
91.23 ± 0.62
83.70 ± 1.39
Table 1: Average and final accuracy comparison across three datasets. Bold numbers indicate the best performance, while underlined values represent the second-best. All methods are evaluated under the exemplar-free setting.
Figure 2: Performance curve of various methods under different settings.
#
CIFAR B0-Inc10
CUB B0-Inc20
VTAB B0-Inc10
(1)
88.23
87.23
85.34
(2)
89.03
89.42
90.12
(3)
91.56
91.91
92.87
(4)
92.00
92.55
94.55
Table 2: Ablation results(Avg) for each component.
Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. Parameter-efficient fine-tuning with pre-trained models reduces parameter overhead but can suffer from cumulative interference and suboptimal alignment between inference samples and specialized modules. We propose Dynamic LoRA-Experts and Prototype-Ensemble Matching (DLEPEM), a two-stage rehearsal-free framework. DLEPEM allocates a task-specific LoRA-Expert for each incremental task to reduce cross-task interference, then combines frozen pre-trained-model prototypes with task-adaptive LoRA-Expert prototypes for reliable task-level discrimination. Experiments on standard CIL and Few-Shot CIL benchmarks demonstrate strong performance under the evaluated protocols.
Hongwei Zhao, Rui Liu, Yansong Liu
School of Computer Science and Engineering, Beihang University
Incremental Object Detection (IOD) aims to continuously learn new object categories without forgetting previously learned ones. Recently, prompt-based methods have gained popularity for their replay-free design and parameter efficiency. However, due to prompt coupling and prompt drift, these methods often suffer from prompt degradation during continual adaptation. To address these issues, we propose a novel prompt-decoupled framework called PDP. PDP innovatively designs a dual-pool prompt decoupling paradigm, which consists of a shared pool used to capture task-general knowledge for forward transfer, and a private pool used to learn task-specific discriminative features. This paradigm explicitly separates task-general and task-specific prompts, preventing interference between prompts and mitigating prompt coupling. In addition, to counteract prompt drift resulting from inconsistent supervision where old foreground objects are treated as background in subsequent tasks, PDP introduces a Prototypical Pseudo-Label Generation (PPG) module. PPG can dynamically update the class prototype space during training and use the class prototypes to further filter valuable pseudo-labels, maintaining supervisory signal consistency throughout the incremental process. PDP achieves state-of-the-art performance on MS-COCO (with a 9.2% AP improvement) and PASCAL VOC (with a 3.3% AP improvement) benchmarks, highlighting its potential in balancing stability and plasticity. The code and dataset are released at: https://github.com/zyt95579/PDP\_IOD/tree/main
Continual learning (CL) aims to incrementally acquire knowledge from sequential data while avoiding catastrophic forgetting. Recently, prompt tuning has attracted increasing attention as an efficient approach for adapting pre-trained models to CL tasks. However, existing prompt design paradigms commonly suffer from retrieval dependence and classifier bias, which make model adaptation sensitive to prompt selection and bias predictions toward newly arrived classes. To address these challenges, we propose Decoupled Prompt Tuning for Continual Learning (DPT4CL), which decouples the CLIP textual prompt into a task-shared prompt distribution and class-specific prompts. The task-shared prompt distribution is derived by optimizing an Information Bottleneck objective to facilitate cross-task knowledge transfer and alleviate classifier bias, while class-specific prompts enhance inter-class separability without relying on explicit prompt retrieval. Furthermore, we establish a unified excess risk bound from an information-theoretic perspective, providing theoretical support for the robust generalization and forgetting mitigation of the proposed framework. Extensive experiments on standard CL benchmarks demonstrate that DPT4CL achieves state-of-the-art performance. The source code is available at https://github.com/Cloudfly-Z/DPT4CL
Yunfei Zhang, Wen Wen, Tieliang Gong +1
National Engineering Lab for Big Data Analytics, School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China