Twin-Space Decoupling and Interaction for Efficient Vision-Language Transfer

Abstract

Pre-trained visual language models have become excellent basic models for many downstream tasks in transfer learning. However, due to the serious gap between the data scale of downstream tasks and the large-scale data used by pre-trained models, migration to downstream tasks will face the dilemma of discriminability and generalization. Therefore, it is necessary to learn task-specific knowledge while retaining general knowledge. How to accurately identify and distinguish these two types of representations remains a challenge. This paper proposes a dual-subspace driven cross-modal semantic interaction and dynamic feature fusion framework, which uses a decentralized covariance dual-subspace decomposition method to decouple visual and text features by constructing task subspaces and general knowledge subspaces, and performs refined modal interactions on the decoupled general features and task features through a cross-modal semantic interaction adapter module. Finally, a cross-level semantic fusion module based on a gating mechanism is used to achieve dynamic fusion of different semantics from shallow to deep. We verify the effectiveness of this method on three tasks: generalization to novel classes, novel target datasets, and domain generalization. Compared with a variety of advanced methods, the proposed method has achieved excellent performance in all evaluation tasks.

1. Introduction

In recent years, vision-language models (VLMs) [1], trained on large-scale image–text pairs through contrastive learning [2,3], have achieved cross-modal alignment between images and texts. These models have demonstrated strong generalization and cross-task adaptability across a wide range of practical applications. For instance, in medical image classification [4,5], VLMs can leverage textual descriptions of disease types to assist in distinguishing image features of different conditions (e.g., differentiating pneumonia from normal lung images), thereby improving classification accuracy. In medical image segmentation [6,7], VLMs enhance segmentation precision by exploiting knowledge acquired during pretraining. In the biological domain [8,9], they facilitate more accurate semantic matching of biological signals. In robotic control [10,11], their outstanding zero-shot capabilities strengthen the understanding of complex environments. Despite these advantages, VLMs still face several challenges in downstream applications. The large number of parameters makes direct fine-tuning computationally expensive and prone to catastrophic forgetting of pre-trained knowledge, while the reliance on manually crafted prompts requires domain expertise, which is particularly difficult to obtain in specialized fields such as rare disease identification.

Two mainstream transfer schemes have recently emerged in this field. The first is prompt learning, which introduces learnable prompt vectors, such as CoOp [12] and MaPLe [13]. By freezing the pre-trained parameters and optimizing only a small number of prompts, this approach achieves a balance between training efficiency and few-shot adaptability. The second is the use of lightweight modules, such as CLIP-Adapter [14], which improves cross-modal alignment through residual lightweight components, and Tip-Adapter [15], which enables efficient adaptation via a non-trainable key–value cache mechanism. Both approaches aim to enhance task adaptability at low computational cost. However, they still face notable limitations. On the one hand, most methods emphasize single-modality modeling while overlooking the intrinsic semantic dependencies between image and text in decision-making. On the other hand, in few-shot scenarios, they are susceptible to interference and even catastrophic forgetting of pre-trained general knowledge, which undermines the effectiveness of representations. As a result, models struggle to fully exploit pre-trained semantic information, thereby constraining their adaptability and robustness.

Moreover, although existing studies primarily focus on improving the accuracy and adaptability of vision-language models (VLMs), relatively little attention has been paid to their portability and practical usability in real-world applications. A practical vision-language framework should not only perform well on benchmark datasets but also exhibit the capability to flexibly transfer across diverse tasks and computational environments. To this end, the framework proposed in this paper is designed with particular emphasis on a modular dual-subspace decoupling strategy and a lightweight feature interaction mechanism, enabling “plug-and-play” integration with various visual backbones and text encoders. This design allows the framework to adapt to multiple downstream tasks without extensive retraining, thereby ensuring high scalability and resource efficiency.

To address these challenges, this paper presents a dual-subspace-driven cross-modal semantic interaction and dynamic feature fusion framework, with three main contributions:

• A decentralized covariance dual-subspace decomposition method is developed to map visual and textual features into a shared subspace and a task-specific subspace. This design enables knowledge separation and collaborative modeling, effectively mitigating catastrophic forgetting of pre-trained knowledge and excessive feature coupling during task adaptation.
• Based on the dual-subspace architecture, a cross-modal semantic interaction adapter module and a gated cross-level semantic fusion module are introduced. The former performs independent interactions between shared and task-specific subspace features to avoid interference between general and task knowledge, while the latter employs a gating mechanism to dynamically adjust feature weights across levels, ensuring consistent semantic alignment.
• The proposed framework is evaluated on multiple benchmark datasets. Experimental results indicate that the method consistently achieves state-of-the-art performance compared with existing approaches, confirming its effectiveness and robustness.

2. Related Work

2.1. Vision-Language Models

Visual Language Models (VLMs) have made significant progress in multiple tasks with their powerful multimodal modeling capabilities, exhibiting clear advantages over traditional models that rely solely on visual or textual supervision. In recent years, representative models such as CLIP [16], ALIGN [17], and FILIP [18] have achieved breakthroughs in joint image-text modeling. These models are typically based on self-supervised strategies, utilizing large-scale architectures and vast amounts of image-text pairs for training, thereby learning unified representations across modalities. Many modal tasks [19,20] have achieved promising results relying on the powerful cross-modal data alignment capabilities of VLMs. CLIP is an excellent work in visual language modeling, which aligns images and textual descriptions through contrastive learning, demonstrating strong zero-shot capabilities in a series of downstream tasks. Tasks such as real-time big data processing [21], camera AR tracking [22], intelligent IoT multi-target monitoring [23,24], texture classification [25], and process monitoring [26] also leverage VLMs to enhance scene adaptation and generalization performance. Despite the remarkable effectiveness of visual language models, they still face the problem of severe degradation in generalization ability when applied to downstream tasks.

2.2. Efficient Transfer Learning

In recent years, research on the efficient adaptation of visual–language models (VLMs) has mainly focused on prompt learning and adapter-based methods. Compared with traditional manually designed prompt templates, prompt learning methods substantially reduce the dependence on human expertise, making them particularly suitable for domains with high professional barriers such as finance, medicine, and biology. Represented by CoOp, this approach replaces fixed prompt templates with learnable continuous context vectors, enabling adaptive modeling of downstream tasks and significantly improving the recognition accuracy of base classes. However, CoOp exhibits weak generalization ability to novel categories and struggles to transfer effectively to unseen classes.

To address this issue, CoCoOp [27] introduced a lightweight meta-network to dynamically generate prompt initialization based on image features, which improved generalization but incurred high training cost and introduced noise. KgCoOp [28] regularized learnable prompts by constraining them toward fixed templates such as “a photo of a [class],” thereby improving generalization while maintaining fitting ability. CoPL [29] enhanced the proportion of task-relevant visual information in prompts by weighting the similarity between image local features and prompts. Although these methods improved prompt design, they rely on a single prompt and remain insufficient to capture the complexity and diversity of image information. ProDA [30] introduced multiple learnable cues for each category to construct diverse descriptions, adapting to richer visual representations, but the improvement in generalization remained limited. MaPLe proposed a multi-modal prompt learning strategy that leverages both visual and textual cues simultaneously, allowing the model to adaptively balance information from different modalities and further enhance generalization to unseen classes.

In parallel, adapter-based lightweight parameter tuning methods have shown promising progress in VLMs. CLIP-Adapter enhanced the matching accuracy of downstream tasks by inserting lightweight adapter modules into visual and text encoders, leveraging residual connections to adjust feature distributions, and outperforming simple prompt tuning methods. For resource-constrained scenarios, Tip-Adapter designed a training-free adapter structure. By combining a key–value caching mechanism with pre-trained knowledge, it achieved efficient adaptation with zero training overhead.

Overall, these studies demonstrate that partial module learning has gradually replaced traditional full-model fine-tuning and become a prevailing trend. However, they also reveal a core challenge: different layers of VLMs exhibit heterogeneous performance in transfer learning. Effectively leveraging pre-trained knowledge in few-sample scenarios to adapt to diverse and complex downstream tasks remains an open problem requiring further investigation.

3. Model

3.1. Problem Definition

Vision–Language Models (VLMs) face several challenges when adapted to downstream classification tasks. In few-shot scenarios, models are prone to overfitting, which can lead to catastrophic forgetting of the general knowledge acquired during pretraining, thereby limiting cross-task transferability. To address this issue, this paper proposes a dual-subspace-driven cross-modal semantic interaction and dynamic feature fusion framework. In each Transformer layer, three core modules, Multi-Modal Feature Disentanglement (MMFD), Cross-Modal Semantic Interaction Adaper (CMSIA), and Gated Cross-Layer Semantic Fusion (GCSF),are introduced to separate universal and task-specific features, enable cross-modal interactions, and integrate multi-level semantic information. This design aims to enhance task discriminability while preserving generalization capability. The detailed implementation process is as follows.

The training dataset comprises $n$ RGB images of size $H\times W\times 3$, denoted as $\{I_i{\}}_{i=1}^n$, with corresponding class labels $\{y_i{\}}_{i=1}^n$. Textual descriptions are generated using CLIP’s generic prompt template, A photo of a $\left[C_k\right]$, where $C_k$ represents the $k$-th class. This process produces a textual set $\{T_k{\}}_{k=1}^K$. Each image and its corresponding text are processed through independent $L$-layer encoders. At the $i$-th layer, the visual and textual features ${ x}_i^t$ and $x_i^v$, are decomposed into two distinct components via a Multi-Modal Feature Disentanglement (MMFD) module. The universal subspace $(x_i^{v\_univ},x_i^{t\_univ})$ encodes generalizable knowledge acquired during pretraining, providing a robust foundation applicable across multiple tasks, while the task-specific subspace $(x_i^{v\_task},x_i^{t\_task})$ captures information that is specialized for the current downstream classification task, enabling the model to adapt effectively to task-specific nuances. The decomposed features are further processed by a Cross-Modal Semantic Interaction (CMSIA) Adaper module, which models interactions within both universal and task-specific subspaces. This disentangled interaction yields four sets of cross-modal features, denoted as $x_i^{v\_univ\_mutual},{ x}_i^{t\_univ\_\_mutual}, x_i^{v\_task\_mutual}$ and $x_i^{t\_task\_\_mutual}$ respectively. These representations allow the model to capture complementary information between modalities while preserving the separation between general and task-specific knowledge.

To exploit hierarchical information across layers, a Gated Cross-Layer Fusion (GCSF) module aggregates the interacted features and produces fused hierarchical visual and textual representations $x_i^{v\_fused}$ and $x_i^{t\_fused}$.

The fused features are integrated with the original layer features through residual connections:

$$x_i^t=x_i^t+\alpha ·x_i^{t\_fused}, x_i^v=x_i^v+\alpha ·x_i^{v\_fused}$$

(1)

After $L$ Transformer layers, the final visual and textual representations, $\{V_i{\}}_{i=1}^L$ and $\{T_i{\}}_{i=1}^L$, are extracted. Cross-modal similarity is computed using cosine similarity:

$$\mathrm{s}\mathrm{i}\mathrm{m}(V_L,T_L^k)={\displaystyle \frac{V_L·T_L^k}{\Vert V_L\Vert ·\Vert T_L^k\Vert }}$$

(2)

where $\Vert ·\Vert$ denotes the $L_2$ norm.

The similarity scores are scaled by a temperature parameter τ and converted into class probabilities via the SoftMax function:

$$p(C)={\displaystyle \frac{\exp (\mathrm{sim}(V_i,T_i)/\tau )}{\sum _{j=1}^K\exp (\mathrm{sim}(V_i,T_j)/\tau )}}$$

(3)

The predicted class is corresponding to the label with the highest probability as follows:

$$C=\arg \max p(C)$$

(4)

This framework enables effective disentanglement of universal and task-specific knowledge, facilitates fine-grained cross-modal interaction, and leverages hierarchical fusion to enhance both generalization and task-specific performance on downstream classification tasks.

3.2. Overall Framework

As illustrated in Figure 1, this study builds upon CLIP by sequentially integrating a Multi-Modal Feature Disentanglement module (MMFD), a Cross-Modal Semantic Interaction Adaper module (CMSIA), and a Gated Cross-Layer Semantic Fusion module (GCSF) after each Transformer layer. The MMFD module decomposes the output of each layer into features in a universal subspace and a task-specific subspace. The universal subspace preserves general semantic knowledge accumulated during pretraining, while the task-specific subspace captures information relevant to the downstream task, providing a foundation for subsequent fine-grained cross-modal interaction.

Figure 1. The overall architecture of our proposed method.

To mitigate interference between universal and task-specific knowledge during interaction, the CMSIA module enforces independent subspace interactions. Specifically, cross-modal semantic interactions are performed exclusively between the visual and textual universal subspace features, and separately between the visual and textual task-specific subspace features. This design ensures that each type of knowledge is aligned within its respective modality while preventing contamination of the universal knowledge by task-specific information. As a result, the model maintains pre-trained knowledge integrity and strengthens cross-modal consistency for task-relevant features, thereby improving adaptability to downstream tasks.

Recognizing that different encoder layers exhibit distinct patterns of knowledge retention—where lower layers primarily encode fundamental visual and linguistic patterns and higher layers capture abstract, task-oriented semantic representations—this study introduces the GCSF module to adaptively compute layer-dependent fusion weights. The module assigns greater weights to foundational universal knowledge at lower layers and emphasizes task-specific knowledge at higher layers. The fused features are then integrated with the original Transformer outputs via residual connections, preserving essential information from the original features while incorporating enhanced representations optimized through cross-modal interaction.

Collectively, these innovations enable the model to effectively balance universal and task-specific knowledge, retain the advantages of CLIP pretraining, and achieve substantial improvements in both accuracy and robustness on downstream tasks.

3.3. Implement

3.3.1. Covariance-Guided Dual-Subspace Feature Decoupling Mechanism

Inspired by the work of Wang [31], who demonstrated that constraining model updates along the directions corresponding to the smallest singular values during incremental learning can effectively preserve the generalization knowledge in pre-trained models, we propose a non-centralized covariance-based dual-subspace decomposition method. We extend this principle to the multimodal scenario, achieving a statistical separation of multimodal features by jointly decoupling textual and visual representations. It should be noted that the “universal subspace” and the “task-specific subspace” do not correspond to strict semantic partitions; rather, they represent a statistical approximation based on the covariance structure of the features. The universal subspace corresponds to the principal directions with higher variance, which typically capture stable and transferable features learned during large-scale pre-training, thereby helping to retain generalization ability and mitigate catastrophic forgetting when adapting to downstream tasks. In contrast, the task-specific subspace is spanned by low-variance or residual directions, which exhibit higher plasticity, allowing the model to flexibly adapt to the distribution of downstream tasks without disrupting the pre-trained representations. A schematic illustration is shown in Figure 2.

Figure 2. The feature disentanglement process.

To implement this feature decoupling, we design the Multi-Modal Feature Disentanglement (MMFD) module, which is integrated after each layer of the text and visual encoders in the CLIP model. The specific process is as follows: First, we obtain the initial representations of the universal and task-specific subspaces by applying two independent linear transformations to map the original visual features $X^v\in {\mathbb{R}}^{\mathrm{M}\times \mathrm{B}\times D_v}$ and text features $X^t\in {\mathbb{R}}^{\mathrm{M}\times \mathrm{B}\times D_t}$ to their respective candidate subspaces:

$$X^t=X^t{W}^t+b^t$$

(5)

$$X^v=X^v{W}^v+b^v$$

(6)

Here, $W^t\in {\mathbb{R}}^{D_t\times D_t}$ and $W^v\in {\mathbb{R}}^{D_v\times D_v}$ are the transformation matrices for the text and visual features, respectively, $D_t$ and $D_v$ are the dimensions of the transformed subspaces, and $b^t$ and $b^v$ are the corresponding bias terms, $\mathrm{M}$ denotes the number of tokens, and $\mathrm{B}$ denotes the batch size

Next, we reshape the transformed features into two-dimensional matrices $X^{t\_flat}{\in \mathbb{R}}^{(\mathrm{M}\times \mathrm{B})\times D_t}$ and $X^{v\_flat}{\in \mathbb{R}}^{(\mathrm{M}\times \mathrm{B})\times D_v}$, and compute the non-centralized covariance matrices:

$${\Sigma }^t={\displaystyle \frac{{(X^{t\_flat})}^{\mathrm{T}}{X}^{t\_flat}}{\mathrm{M}\times \mathrm{B}-1}}\in {\mathbb{R}}^{D_t\times D_t}$$

(7)

$${\Sigma }^v={\displaystyle \frac{{{(X}^{v\_flat})}^{\mathrm{T}}{X}^{v\_flat}}{\mathrm{M}\times \mathrm{B}-1}}\in {\mathbb{R}}^{D_v\times D_v}$$

(8)

We then perform eigenvalue decomposition on these covariance matrices:

$${\Sigma }^t=U^t{\Lambda }^t{{(U}^t)}^{\mathrm{T}}$$

(9)

$${\Sigma }^v=U^v{\Lambda }^v{{(U}^v)}^{\mathrm{T}}$$

(10)

here, $U^t\in {\mathbb{R}}^{D_t\times D_t}$ and $U^v\in {\mathbb{R}}^{D_v\times D_v}$ are the unitary matrices of eigenvectors for the text and visual features, respectively, and ${\Lambda }^t$ and ${\Lambda }^t$ are the diagonal matrices of eigenvalues, sorted in descending order.

The eigenvectors are sorted in descending order according to their corresponding eigenvalues and then divided by a proportion $k\in (\mathrm{0,1})$. Let $N_t$ and $N_v$ denote the number of feature vectors in the text and visual matrices, respectively. The first $\lfloor k{N}_t\rfloor$ and $\lfloor k{N}_v\rfloor$ eigenvectors form the universal subspace, while the remaining eigenvectors form the task-specific subspace. The universal subspace captures directions with larger variance, preserving pre-trained knowledge, whereas the task-specific subspace is more flexible, allowing adaptation to downstream tasks.

$$U^{t\_univ}=[u_1^t,u_2^t,\dots ,u_{\lfloor k{N}_t\rfloor }^t]\in {\mathbb{R}}^{D_t\times \lfloor k{N}_t\rfloor }$$

(11)

$$U^{v\_univ}=[u_1^v,u_2^v,\dots ,u_{\lfloor k{N}_v\rfloor }^v]\in {\mathbb{R}}^{D_v\times \lfloor k{N}_v\rfloor }$$

(12)

Here, $\lfloor \cdot \rfloor$ denotes the floor operation

The remaining eigenvectors form the null space, which represents the part of the feature space with minimal influence from pre-trained knowledge. This space can be used to guide the model to focus on task-specific features:

$$U^{t\_task}=[u_{\lfloor k{N}_t\rfloor +1}^t,u_{\lfloor k{N}_t\rfloor +2}^t,\dots ,u_{N_t}^t]\in {\mathbb{R}}^{D_t\times (N_t-\lfloor k{N}_t\rfloor )}$$

(13)

$$U^{v\_task}=[u_{\lfloor k{N}_v\rfloor +1}^v,u_{\lfloor k{N}_v\rfloor +2}^v,\dots ,u_{N_v}^v]\in {\mathbb{R}}^{D_v\times (N_v-\lfloor k{N}_v\rfloor )}$$

(14)

Finally, we project the transformed features $X^t$ and $X^v$ onto their respective subspace bases:

$$X^{t\_task}=X^t{U}^{t\_task}(U^{t\_task}{)}^{\mathrm{T}}{\in \mathbb{R}}^{(\mathrm{M}\times \mathrm{B})\times D_t}$$

(15)

$$X^{v\_task}=X^v{U}^{v\_task}(U^{v\_task}{)}^{\mathrm{T}}{\in \mathbb{R}}^{(\mathrm{M}\times \mathrm{B})\times D_v}$$

(16)

$$X^{t\_univ}=X^t{U}^{t\_univ}(U^{t\_univ}{)}^{\mathrm{T}}{\in \mathbb{R}}^{(\mathrm{M}\times \mathrm{B})\times D_t}$$

(17)

$$X^{v\_univ}=X^v{U}^{v\_univ}(U^{v\_univ}{)}^{\mathrm{T}}{\in \mathbb{R}}^{(\mathrm{M}\times \mathrm{B})\times D_v}$$

(18)

After projecting, the decoupled text and visual feature matrices are reshaped back to their original dimensions to obtain $X^{t\_task}$, $X^{v\_task}$, $X^{t\_univ}$ and $X^{v\_univ}$.

3.3.2. Cross-Modal Semantic Interaction Adapter Module

To mitigate potential interference between universal and task-specific features in cross-modal learning, we propose a Cross-Modal Semantic Interaction Adapter (CMSIA), as illustrated in Figure 3. This module extends the conventional adapter by introducing two shared low-dimensional interaction matrices between the down-projection and up-projection stages. These matrices enable independent interactions for universal and task-specific features, effectively preserving pretrained knowledge while enhancing downstream task adaptation and preventing interference between general and task-specific features. Specifically, for the universal and task-specific features obtained from the MMFD module, denoted as $X^{t\_task}$, $X^{v\_task},X^{t\_univ}$ and $X^{v\_univ}$, we construct independent interaction pathways for both visual and textual modalities. During the interaction stage, two parallel low-rank matrices $W^{share\_univ}$ and $W^{share\_task}$, are introduced. The universal features of both modalities interact through $W^{share\_univ}$, while the task-specific features interact through $W^{share\_task}$. The computation process is defined as follows:

$$X^{v\_univ\_\_mutual}=W^{v\_up}·\delta \left(W^{share\_univ}·\delta \left(W^{v\_down}·X^{v\_univ}\right)\right)$$

(19)

$$X^{t\_univ\_\_mutual}=W^{t\_up}·\delta \left(W^{share\_univ}·\delta \left(W^{t\_down}·X^{t\_univ}\right)\right)$$

(20)

$$X^{v\_task\_\_mutual}=W^{v\_up}·\delta \left(W^{share\_task}·\delta \left(W^{v\_down}·X^{v\_task}\right)\right)$$

(21)

$$X^{t\_task\_\_mutual}=W^{t\_up}·\delta \left(W^{share\_task}·\delta \left(W^{t\_down}·W^{t\_down}\right)\right)$$

(22)

Figure 3. The modality interaction process.

Here, $W^{v\_down}$ and $W^{t\_down}$ are the downsampling matrices for the visual and text modalities, respectively, while $W^{v\_up}$ and $W^{t\_up}$ are the upsampling matrices. $W^{share\_univ}$ and $W^{share\_task}$ are the interaction matrices for different subspace features, and $\delta$(.) represents the activation function, implemented as Relu. After cross-modal interaction $X^{v\_univ\_\_mutual}$ and $X^{v\_task\_\_mutual}$ capture the universal and task-specific subspace features of the visual modality, while $X^{t\_univ\_\_mutual}$ and $X^{t\_task\_\_mutual}$ encode the corresponding representations for the textual modality.

3.3.3. Gated Cross-Level Semantic Fusion Module

After effectively disentangling the pre-trained universal features and downstream task-specific features using the MMFD module and performing cross-modal interactions via the CMSIA module, the challenge of integrating these features across layers remains. In practice, the model’s dependence on universal and task-specific features varies considerably across layers, rendering single-layer fusion insufficient to capture the full semantic representation. To address this, we introduce the Gated Cross-Level Semantic Fusion (GCSF) module, which dynamically balances the contributions of universal and task-specific features across layers, enabling optimal semantic fusion.

For any modality $m\in \{t,v\}$, the task-specific and universal features are first concatenated:

$$X^{m\_concat}=\left[\begin{array}{l}{X}^{m\_task\_\_mutual},X^{m\_univ\_\_mutual}\end{array}\right]$$

(23)

Next, the fusion weights $\beta \in [0,1]$ are generated using a two-layer neural network:

$${\beta }_m=\sigma \left(W_m^{(2)}·\tanh \left({X^{m\_concat}·W}_m^{(1)}+b_m^{(1)}\right)+b_m^{(2)}\right)$$

(24)

Here, $W_m^{(1)}\in {\mathbb{R}}^{2D\times D}$ and $W_m^{(2)}\in {\mathbb{R}}^{1\times D}$ are the weight matrices of the gating network, $b_t^{(1)}\in R^D$ and $b_t^{(2)}\in R^1$ are the bias terms, and $\sigma (·)$ is the Sigmoid activation function.

Finally, the contributions of task-specific and universal features are dynamically balanced using the generated weights to produce the fused output:

$$X^{m\_fused}={\beta }_m\odot X^{m\_task}+(1-{\beta }_m)\odot X^{m\_univ}$$

(25)

here, $\odot$ denotes element-wise multiplication.

Algorithm 1 provides the pseudocode of our proposed method.

Algorithm 1 # Pseudocode for Dual-Subspace Cross-Modal Decoupling & Fusion

for each image I_i and text T_k:           Xv, Xt = VisualEncoder(I_i), TextEncoder(T_k)           for layer = 1 to L:                     # 1. Multi-Modal Feature Disentanglement                     Xv_univ, Xv_task = MMFD(Xv)                     Xt_univ, Xt_task = MMFD(Xt)                     # 2. Cross-Modal Semantic Interaction Adapter                     Xv_univ_mut, Xt_univ_mut = CMSIA(Xv_univ, Xt_univ)                     Xv_task_mut, Xt_task_mut = CMSIA(Xv_task, Xt_task)                     # 3. Gated Cross-Level Semantic Fusion                     beta_v = GatingNetwork(concat(Xv_task_mut, Xv_univ_mut))                     beta_t = GatingNetwork(concat(Xt_task_mut, Xt_univ_mut))                     Xv_fused = beta_v $\times$ Xv_task_mut + (1-beta_v) $\times$ Xv_univ_mut                     Xt_fused = beta_t $\times$ Xt_task_mut + (1-beta_t) $\times$ Xt_univ_mut                     # Residual connection                     Xv, Xt = Xv + alpha_res $\times$ Xv_fused, Xt + alpha_res $\times$ Xt_fused

4. Experiments

Based on previous work, we systematically evaluate the performance of the proposed method, covering the generalization ability from base classes to new classes, cross-dataset transfer effect, and domain generalization performance.

4.1. Experimental Setup

4.1.1. Datasets

The proposed method is evaluated on 11 image classification datasets, covering a wide range of tasks: general object recognition (ImageNet [32], Caltech101 [33]), fine-grained recognition (OxfordPets [34], StanfordCars [35], Flowers102 [36], Food101 [37], FGVCAircraft [38]), scene understanding (SUN397 [39]), texture recognition (DTD [40]), satellite imagery (EuroSAT [41]), and action recognition (UCF101 [42]). Detailed information of the datasets is shown in Table 1.

Table 1. Summary of the 11 datasets.

Datasets	Classes	Train	Val	Test	Description
ImageNet	1000	1.28 M	~	50,000	Recognition of generic objects
Caltech101	100	4128	1649	2465	Recognition of generic objects
OxfordPets	37	2944	736	3669	Fine-grained classification of pets
StanfordCars	196	6509	1635	8041	Fine-grained classification of cars
Flowers102	102	4093	1633	2463	Fine-grained classification of flowers
Food101	101	50,500	20,200	30,300	Fine-grained classification of foods
FGVCAircraft	100	3334	3333	3333	Fine-grained classification of aircrafts
SUN397	397	15,880	3970	19,850	Scene classification
DTD	47	2820	1128	1692	Texture classification
EuroSAT	10	13,500	5400	8100	Land use & cover classification with satellite images
UCF101	101	7639	1898	3783	Action recognition

4.1.2. Baselines

We compare the proposed method with several state-of-the-art methods, including CLIP, CoOp, CoCoOp, KgCoOp, ProGrad [43], and AAPL [44]. ProGrad has different settings in cross-dataset experiments and domain generalization experiments from existing experiments, so we compare it with them in this experiment.

4.1.3. Implementation Details

All experiments were conducted on an NVIDIA V100 GPU with 32 GB of memory. The software environment consisted of Python 3.8.2 and PyTorch 2.4.0. For data construction, each experiment utilized a training set comprising 16 images per category, and all images were resized to 224 × 224 RGB. The visual encoder was configured as ViT-B/16, and text prompts followed the default CLIP template, “a photo of a [class]”. Model parameters were optimized using the AdamW optimizer with an initial learning rate of 0.0019. Mixed-precision training was employed to enhance computational efficiency. For the large-scale ImageNet dataset, which contains numerous categories, a batch size of 8 and a training schedule of 3 epochs were adopted to accelerate training. For all other datasets, a batch size of 4 and 10 training epochs was used to ensure sufficient convergence.

4.2. Base-to-Novel Generalization

In this experiment, we evaluate the recognition performance on 11 widely used datasets by reporting the accuracy for base classes (Base), novel classes (Novel), and the harmonic mean (HM), which quantifies the trade-off between the two. The results are summarized in Table 2.

$$\mathrm{H}\mathrm{M}={\displaystyle \frac{2\times \mathrm{B}\mathrm{a}\mathrm{s}\mathrm{e}\times \mathrm{N}\mathrm{e}\mathrm{w}}{\mathrm{B}\mathrm{a}\mathrm{s}\mathrm{e}+\mathrm{N}\mathrm{e}\mathrm{w}}}$$

(26)

Table 2. Comparison with state-of-the-art methods on different datasets in the Base-to-Novel Generalization setting. Bold values indicate the best performance.

Datasets	Metric	CLIP	CoOp	CoCoOp	ProGrad	KgCoOp	AAPL	Ours
ImgNet	Base	72.42	76.47	75.98	77.02	75.83	76.53	77.50
	New	68.14	67.88	70.43	66.66	69.96	70.57	71.30
	H	70.22	71.92	73.10	71.46	72.78	73.43	73.81
Caltech	Base	96.84	98.00	97.96	98.02	97.72	97.87	98.23
	New	94.00	89.31	93.81	93.89	94.39	95.10	93.00
	H	95.40	93.73	95.84	95.91	96.03	96.46	95.54
Pets	Base	91.17	93.67	92.50	95.07	94.65	95.63	95.68
	New	97.26	95.29	97.69	97.63	97.60	97.40	97.93
	H	94.12	94.47	96.43	96.33	96.18	96.51	96.79
Cars	Base	63.37	78.12	70.49	77.68	71.76	70.33	78.00
	New	74.89	60.40	73.59	68.68	75.04	73.50	70.33
	H	68.65	68.13	72.01	72.88	73.56	71.88	73.97
Flowers	Base	72.08	97.60	94.87	95.54	95.00	95.10	96.80
	New	77.80	59.67	71.75	71.87	74.73	70.63	71.73
	H	74.83	74.06	81.71	82.03	83.65	81.06	82.40
Food101	Base	90.10	88.33	90.70	90.37	90.50	90.70	90.43
	New	91.22	82.26	91.29	89.59	91.70	91.60	91.17
	H	90.66	85.19	90.99	89.98	91.09	91.15	90.80
Aircraft	Base	27.19	40.44	33.41	40.54	36.21	34.07	40.83
	New	36.29	22.30	23.71	27.57	33.55	24.17	33.27
	H	31.09	28.75	27.74	32.82	34.83	28.28	36.66
SUN397	Base	69.36	80.6	79.74	81.26	80.29	79.65	82.10
	New	75.35	65.89	76.86	74.17	76.53	76.90	77.37
	H	72.23	72.51	78.27	77.55	78.36	78.25	79.66
DTD	Base	53.24	79.44	77.01	77.35	77.55	73.90	82.63
	New	59.90	41.18	56.00	52.35	54.99	53.43	63.00
	H	56.37	54.24	64.85	62.45	64.35	62.02	71.49
EuroSAT	Base	56.48	92.19	87.49	90.11	85.64	87.00	89.60
	New	64.05	54.74	60.04	60.89	64.34	66.30	79.57
	H	60.03	68.69	71.21	72.67	73.48	75.25	84.29
UCF101	Base	70.53	84.69	82.33	84.33	82.89	82.20	86.10
	New	77.50	56.05	73.45	74.94	76.67	74.27	78.90
	H	73.85	67.46	77.64	79.35	79.65	78.03	82.34
Avg ACC	Base	69.37	82.69	80.47	82.48	80.73	80.27	83.43
	New	74.22	63.22	71.69	70.75	73.60	72.17	75.23
	H	71.70	71.66	75.83	76.16	77.00	76.01	79.12

As shown in Table 2, our method demonstrates consistent advantages across all evaluation metrics. For the Base category, it achieves an average accuracy of 84.02%, outperforming CoOp (82.69%), KgCoOp (80.73%), and AAPL (80.27%), indicating strong fitting ability on known classes. In the Novel category, our method reaches 75.65%, substantially higher than CoOp (63.22%) and AAPL (72.17%), demonstrating superior generalization to unseen classes. Considering the balance between Base and Novel performance, our approach attains the highest Harmonic Mean (HM) of 79.65%, surpassing CoOp (71.66%), ProGrad (76.16%), and KgCoOp (77.00%), thereby exhibiting strong overall generalization.

On individual datasets such as DTD and EuroSAT, our method achieves the best results across all three metrics, with EuroSAT reaching an HM of 84.29%. Even on strong baselines like UCF101 and Caltech101, it maintains either leading or comparable performance, verifying its robustness across diverse tasks. Notably, on a few datasets including Caltech (Novel), Cars (Novel), and Food101 (Novel), the improvements are relatively limited. This can be attributed to dataset characteristics and category complexity: the Caltech Novel classes exhibit high visual similarity and a limited number of samples, which constrains the model’s ability to fully capture intra-class diversity; Cars is a fine-grained dataset with subtle inter-class differences, limiting few-shot generalization; and the Novel classes in Food101 encompass a wide range of visually distinct food items, where individual images cannot cover the full diversity, thereby restricting model performance under few-shot conditions. Overall, these observations indicate that, although our method exhibits robust generalization overall, few-shot performance remains challenging on datasets that are extremely small, highly fine-grained, or internally diverse.

4.3. Few-Shot Learning Evaluation

To evaluate the adaptability of our model in data-scarce scenarios, we conducted experiments under low-sample training conditions of 1, 2, 4, and 8 shots, examining performance fluctuations across different sample sizes. The results are presented in Figure 4. As the number of samples decreases from 16 to 1, overall performance declines, although the reduction remains relatively moderate. Notably, the model maintains high accuracy under 8-shot and 4-shot conditions, demonstrating strong robustness and generalization. Certain datasets, such as Pets and Caltech101, exhibit minimal sensitivity to sample size, with negligible performance fluctuations, indicating effective adaptation to low-sample conditions. In contrast, datasets including EuroSAT, FGVC, and DTD are more sensitive, showing substantial performance degradation as the number of shots decreases. Even at extremely low sample sizes (1 or 2 shots), the model retains reasonable performance overall, highlighting its stability and practical utility in few-shot learning tasks. These results demonstrate that the proposed model consistently achieves strong performance under constrained data conditions, supporting its applicability in real-world few-shot scenarios.

Figure 4. Performance fluctuations under the few-shot setting.

4.4. Cross-Dataset Evaluation

Table 3 presents the performance of the proposed method compared to other models in cross-dataset transfer scenarios. Overall, the proposed approach demonstrates superior performance across multiple key tasks, particularly on ImageNet, Flowers, Aircraft, SUN397, and UCF101. On ImageNet, our method achieves an accuracy of 71.70%, highlighting its capability to handle large-scale general image recognition tasks. For the Flowers dataset, it attains 71.96%, surpassing CoOp by 3.25 percentage points and KgCoOp by 0.71 percentage points, indicating enhanced expressiveness in fine-grained classification. Notably, on the Aircraft dataset, accuracy reaches 24.30%, which is 5.83% higher than CoOp and 1.36% higher than CoCoOp, further demonstrating the method’s adaptability in fine-grained and complex scenes. In the UCF101 action recognition task, our approach achieves 69.50%, exceeding CoOp by 2.95 percentage points and AAPL by 0.20 percentage points, illustrating strong cross-modal transfer capability. While performance is slightly lower than the best results on certain datasets such as Caltech101 and EuroSAT, this can be attributed to specific characteristics of these datasets. Caltech101 contains relatively few samples per category and imbalanced class distributions, which can limit the effectiveness of methods that rely on preserving generalizable subspace knowledge, whereas CoCoOp’s class-adaptive prompts may better capture the features of small-sample categories. EuroSAT, as a remote sensing dataset, exhibits visual patterns that differ significantly from everyday object images; although our method employs multi-modal feature disentanglement and cross-layer semantic fusion, it may not fully adapt to the fine-grained textures and color distributions unique to remote sensing images. In general, the slightly lower performance on these datasets reflects the intrinsic differences in data distribution and cross-domain adaptation challenges, while the proposed method maintains stable and robust performance across the majority of diverse downstream tasks, confirming its overall effectiveness and generalization capability.

Table 3. Comparison of the Proposed Method with State-of-the-Art Methods in the Cross-Dataset Evaluation Setting. Bold values indicate the best performance.

Method	Source	Target
	ImgNet	Caltech	Pets	Cars	Flowers	Food101	Aircraft	SUN397	DTD	EuroSAT	UCF101
CoOp	71.51	93.70	89.14	64.51	68.71	85.30	18.47	64.15	41.92	46.39	66.55
CoCoOp	71.02	94.43	90.14	65.32	71.88	86.06	22.94	67.36	45.73	45.37	68.21
KgCoOp	71.26	94.35	89.96	65.40	71.25	86.38	23.05	66.20	46.38	46.04	68.63
AAPL	71.37	94.17	90.73	65.32	71.77	86.00	23.03	66.80	44.80	41.83	69.30
Ours	71.70	93.50	90.10	64.37	71.96	85.40	24.30	67.50	44.13	40.30	69.50

4.5. Domain Generalization

To evaluate the model’s ability to handle domain shifts and generalize to out-of-distribution data, we trained it on ImageNet and tested it on four variants that introduce different types of domain shifts: ImageNetV2 [45], ImageNet-Sketch [46], ImageNet-A [47], and ImageNet-R [48]. The results are summarized in Table 4. On ImageNetV2, our method achieves an accuracy of 64.64%, surpassing approaches such as CoCoOp, indicating strong adaptability to distribution shifts similar to the training domain. On the more challenging ImageNet-Sketch dataset, it attains 49.27%, highlighting its robust cross-modal feature transfer capability. For ImageNet-R, which contains a variety of rendering styles, the model achieves the highest accuracy of 77.17%, demonstrating strong structural invariance and high-level semantic transferability.

Table 4. Comparison of the Proposed Method with state-of-the-art methods in the Domain Generalization setting. Bold values indicate the best performance.

Method	Source	Target
	ImgNet	ImgNet-V2	ImgNet-Sketch	ImgNet-A	ImgNet-R
CLIP	66.73	60.83	46.15	47.77	73.96
CoOp	71.51	64.20	47.99	49.71	75.21
CoCoOp	71.02	64.07	48.75	50.63	76.18
KgCoOp	71.20	64.10	48.90	50.60	76.70
AAPL	71.37	64.20	48.80	50.60	76.87
Ours	71.70	64.64	49.27	48.27	77.17

Although the performance on ImageNet-A, which consists of adversarially selected examples, is slightly lower than some existing methods, an accuracy of 48.27% remains reasonable and reflects the model’s stability when handling extreme samples. This slight performance drop may be attributed to the adversarial characteristics and extreme noise in ImageNet-A, which limit the model’s ability to fully rely on general subspace knowledge for prediction, while its strong cross-modal knowledge transfer remains effective on regular and structured images. Overall, these results indicate that the proposed method exhibits strong generalization in cross-domain scenarios, effective cross-modal transfer, and robust structural and semantic invariance.

4.6. Ablation Analysis

To systematically evaluate the contributions of each module in the proposed method, we conducted a series of ablation experiments. The method comprises a feature decoupling module, a cross-modal interaction module, and a gated fusion module. The first two modules are interdependent and operate as a cooperative unit, responsible for disentangling multi-modal features and aligning semantics across modalities. The gated fusion module adaptively integrates universal and task-specific features, balancing generalization and discriminability. It is worth noting that traditional adapter usage typically falls into two categories: single-side adapters, added only after each layer of either the visual (V) or textual (T) modality, and dual-side adapters, added after layers of both visual and textual modalities. Our ablation experiments are designed to examine both single- and dual-side scenarios, assessing the effects of cross-modal collaboration and the contributions of each proposed module.

The ablation settings are defined as follows:

w/o L. removes the visual encoder adapter while retaining the text encoder adapter, to analyze the effect of cross-modal feature collaboration under a single-sided condition and assess the necessity of dual-sided modeling.

w/o V. removes the text encoder adapter while retaining the visual encoder adapter, similarly evaluating cross-modal collaboration under a single-sided condition

w/o GF (Direct Sum). removes the gated fusion module and fuses universal and task-specific features via simple element-wise summation, to verify the role of gating in feature fusion and adaptive weighting.

w/o All Proposed Modules. removes the feature decoupling, cross-modal interaction, and gated fusion modules, retaining only the text and visual encoder adapters, to evaluate the overall contribution of the proposed method compared with the baseline adapters.

The experimental results, as illustrated in Figure 5, show that the full model consistently outperforms all ablation conditions, validating the effectiveness and synergistic benefits of the proposed modules.

Figure 5. Ablation experiment comparison.

4.7. Parameter Sensitivity Experiments

We conducted a sensitivity experiment on the shared layer dimension in the cross-modal semantic interaction module to analyze its impact on model performance. As shown in Figure 6 (left),the accuracy of the Base class gradually increases with the increase in the dimension, from 82.27 at a smaller dimension to 84.18 at a larger dimension, indicating that a larger dimension helps improve the model’s ability to fit the seven categories, but the accuracy of the Novel class begins to decline after the dimension reaches 32, indicating that too large a dimension may introduce more trainable parameters, resulting in overfitting on new classes with less data. Considering the performance of both Base and Novel classes, the harmonic mean reaches the best when the dimension is 16. Therefore, choosing a moderate shared layer dimension helps to improve the model’s expressiveness while taking into account the generalization performance of new classes.

Figure 6. Effect of shared layer dimension (left) and scaling factor (right).

In this experiment, we analyzed the impact of the scaling factor $\alpha$ on the model, and the results are shown in Figure 6 (right). Overall, the model performs best when the scaling factor is 0.001, and the model reaches 83.43, 75.23, and 79.12 in Base, Novel, and HM, respectively. As the scaling factor increases, the accuracy of the model on Base gradually improves, but the accuracy on Novel decreases significantly, and overfitting to the base class occurs. When the scaling factor of the model is small, the model’s insufficient ability to interact with modal features leads to poor performance in the Base class; when the scaling factor is moderate, the model achieves good feature alignment between different modalities, while improving the performance of the Base class, it also maintains the generalization ability of the Novel class, thus achieving the best balance; and when the scaling factor continues to increase, the model overfits the Base class features, resulting in a decrease in the generalization ability of the Novel class, and the overall HM no longer improves. The above results show that when the scaling factor is 0.001, the optimal performance balance between the seen category and the new category can be achieved.

As shown in the line charts on the left side of Figure 7, the model exhibits a similar trend on the EuroSAT and UCF101 datasets: as the proportion of universal feature subspaces increases, the performance on base classes gradually declines, while the generalization ability does not show a corresponding improvement. This indicates that increasing the proportion of universal features does not necessarily enhance generalization performance. A possible reason is that the decoupling between universal and task-specific features is not yet fully achieved, leading to mutual interference between the two types of representations. Since complete feature independence has not been realized in the current framework, we adopt the partition ratio that yields the optimal overall HM performance across all datasets. Future work will further explore more effective and robust feature disentanglement mechanisms to improve the generalization and stability of the model in multi-task scenarios.

Figure 7. Effect of the proportion of universal feature subspaces on model performance. In the right-side bar chart, the red color represents the best performance.

As shown in Table 5, although our method introduces more trainable parameters compared with previous prompt-learning approaches, the overall parameter scale remains small relative to the CLIP backbone, accounting for only about 5.48% of its total parameters. This moderate increase in parameters leads to a significant performance improvement, with our method achieving the highest HM value (79.12%) among all compared methods. In future work, we will further explore lightweight optimization strategies to reduce the number of trainable parameters and inference latency while maintaining performance improvements.

Table 5. Comparison of model parameters and performance among different prompt-learning methods.

Methods	CLIP	CoOp	CoCoOp	Maple	Ours
Parameters	124,324,281	2048	35,360	3,555,072	6,815,744
HM	71.70	71.66	75.83	78.55	79.12

4.8. Feature Visualization

As shown in Figure 8, to verify the spatial distinguishability between general and task-specific features of images and text in the pre-trained model, this study conducts an analysis using a multi-class cat image dataset. First, the image features extracted by CLIP are L2-normalized and then separated according to their eigenvalues. For each patch (excluding the CLS token), the activation intensities of both general and task-specific features are computed, and the activations of task-specific features are superimposed onto the original image for visualization. The results show that, even among different cat breeds, the general features are mainly concentrated in key regions such as the nose, back, and ears, while the task-specific features focus on fine-grained details such as paws, fur, and spots. This indicates a clear spatial distinction between general and task-specific features. However, some overlapping activations between the two types of features are observed in certain regions (e.g., paws or body parts), especially in images with complex backgrounds. These irrelevant activations should be removed from the general features to reduce noise interference. Based on these observations, future work will focus on optimizing the processing of general features within the current decomposition module and exploring more effective decomposition strategies to enhance the separation of task-specific features, thereby further improving the transferability and robustness of the model in downstream tasks.

Figure 8. Visualization of General and Task-Specific Features.

5. Conclusions

In this study, we proposed a dual-subspace driven cross-modal semantic decoupling and fusion framework to enhance the generalization of visual–language models across diverse downstream tasks. By separating task-specific and general knowledge subspaces and leveraging cross-modal interaction and dynamic semantic fusion, the framework effectively balances task adaptability and generalization in few-sample scenarios. Nevertheless, limitations remain: manual separation of general and task-specific features may be inflexible, and overlaps between these features, especially in complex backgrounds, can introduce noise. Future work will focus on optimizing general feature processing and exploring alternative decomposition strategies to improve task-specific feature separation, further enhancing transfer performance, robustness, and adaptability of visual–language models in diverse downstream applications.