Target-Oriented Single Domain Generalization

## Abstract

Abstract Deep models trained on a single source domain often fail catastrophically
under distribution shifts, a critical challenge in Single Domain Generalization (SDG). While existing methods focus on augmenting source data or learning invariant features, they neglect a readily available resource: textual descriptions of the target deployment
environment. We propose Target-Oriented Single Domain Generalization (TO-SDG), a novel problem setup that leverages the textual description of the target domain, without requiring any target data, to guide model generalization.
To address TO-SDG, we introduce S pectral TAR get Alignment (STAR),
a lightweight module that injects target semantics into source features by exploiting visual-language models (VLMs) such as CLIP.
STAR uses a target-anchored subspace derived from
the text embedding of the target description to re-center image
features toward the deployment domain, then utilizes spectral projection to retain directions aligned with target cues while discarding source-specific noise. Moreover, we use a vision-language distillation to align backbone features with
VLM’s
semantic geometry. STAR further employs feature-space Mixup to ensure smooth transitions between source and target-oriented representations.
Experiments across various image
classification and object detection benchmarks demonstrate STAR’s superiority.
This work establishes that minimal textual metadata, which is a practical and often overlooked resource, significantly enhances generalization under severe data constraints, opening new avenues for deploying robust models in target environments with unseen data.

## 1 Introduction

Deep models trained on data drawn from a single source domain often suffer performance drops when deployed in novel environments that violate their training-distribution assumptions volpi2018generalizing; qiao2020learning. In computer vision, for example, classifiers tuned to well-lit daytime photos can misfire on low-light, foggy, or sensor-noisy images that lie just outside their original support. This vulnerability is especially acute in Single-Domain Generalization (SDG), where all training samples originate from one domain and no auxiliary target data are available. Lacking the diversity that multi-domain corpora provide, SDG methods must combat source-specific biases with only limited inductive cues, a challenge that has so far restricted progress relative to classical domain-generalization settings gulrajani2020search.

Rigid SDG protocol overlooks a readily available and inexpensive signal, high-level knowledge of the deployment domain. Even when target images are inaccessible, e.g., for privacy, security, or data-collection cost reasons, practitioners can usually articulate the target domain in words (“radiographs of feline thoraxes”, “night-time driving in snow”, etc.). Such descriptions encode the semantic factors that drive domain shift but are ignored by traditional SDG pipelines.

Meanwhile, large Vision–Language Models (VLMs) such as CLIP radford2021learning, ALIGN jia2021scaling, and BLIP li2022blip embed both images and natural-language phrases into a shared latent space. Prompt learning zhou2022learning, conditional prompting zhou2022conditional, and adapter layers gao2024clip; zhang2022tip show that these embeddings are readily steerable toward new tasks with minimal supervision. Yet all prior CLIP-based robustness methods ultimately rely on target images, for prompt ensembling, adapter tuning, or test-time refinement, and thus violate the SDG constraint.

We introduce the new problem of Target-Oriented Single-Domain Generalization (TO-SDG), a protocol that augments the classic SDG setting with one additional asset: a textual specification describing the target environment. TO-SDG explores
whether such lightweight metadata can compensate for the absence of target images and, if so, how to inject it into the training loop. To answer this question in image domains,
we present Spectral Target Alignment (STAR), a method that infuses target semantics into backbone image features
based on VLMs such as CLIP.
STAR addresses the core challenge of TO-SGD by integrating target semantics through a unified spectral
target orientation
process: the natural-language description of the target domain is first encoded into a compact vector via CLIP’s text encoder, establishing a semantic anchor in the vision-language space. This vector guides a two-fold transformation of source features, recentering their distribution to align with the target’s centroid (mitigating source-specific bias) and projecting them onto a low-rank subspace spanned by the directions most aligned with the target embedding, effectively filtering out noisy, domain-specific variations while preserving discriminative structure. To further harmonize the backbone’s geometry with CLIP’s semantically rich embedding space, a lightweight distillation loss regresses features toward their CLIP image embedding counterparts, transferring cross-modal invariances without updating the frozen VLM. Finally, feature-space Mixup interpolates between original and
target-oriented
representations, enforcing smooth decision boundaries that bridge source and target manifolds.
The main contributions of the paper are three-fold:

- •
We introduce the new problem setting of Target-Oriented Single Domain Generalization (TO-SDG),
which extends standard SDG by incorporating textual descriptions of the target domain, an accessible and practical source of prior knowledge in real-world deployments.
- •
We propose Spectral Target Alignment (STAR), a lightweight framework for multiple tasks
that injects target semantics via spectral target orientation,
vision-language distillation, and feature-space mixup, enabling effective text-guided generalization in TO-SDG.
- •
Experiments on diverse image classification and object detection benchmarks show that STAR reliably outperforms existing SDG methods, confirming that incorporating freely available natural language descriptions based on
various VLMs can significantly improve generalization performance.

## 2 Related Works

### 2.1 Single Domain Generalization

Single Domain Generalization (SDG) addresses the formidable challenge of training models that generalize to unseen domains using supervision from only a single source domain, in the absence of any information about target domain distributions. In contrast to conventional domain generalization paradigms that exploit multiple source domains to enhance model robustness, SDG imposes stricter constraints, requiring generalization solely from intra-domain cues. As a result, SDG presents significantly greater difficulty than its multi-source counterpart.

##### Classification

Existing approaches to SDG in image classification task primarily focus on enhancing the diversity and expressiveness of the source domain through data augmentation, which can be broadly categorized into three methodological streams.
The first stream comprises conventional augmentation techniques aimed at increasing within-domain variability to promote out-of-domain robustness. Techniques such as Improved Regularization via Data Augmentation devries2017improved, AugMix hendrycks2019augmix, and AutoAugment Cubuk_2020_CVPR_Workshops exemplify this line of work by introducing stochastic or learned perturbations to source samples. Geometric augmentation strategies have been further advanced by Lian et al. lian2021geometry, while ACVC Cugu_2022_CVPR constructs pseudo-domains by applying structured corruptions and aligning attention maps across clean and perturbed images.
The second stream adopts adversarial augmentation techniques that generate challenging variants of training samples either in the input or feature space. Early efforts by Volpi et al. volpi2018generalizing and Zhao et al. Long2020Maximum explore perturbations in the pixel space to induce hard domain shifts. More recent developments, such as Adversarial AutoAugment zhang2019adversarial and the work of Zhang et al. zhang2023adversarial, explore learned augmentation policies and perturbation of feature statistics, respectively. While these approaches introduce greater diversity, sustaining semantically coherent yet domain-divergent augmentations remains an open challenge.
The third stream leverages generative modeling to synthesize novel data distributions. Approaches such as those proposed by Qiao et al. qiao2020learning, Wang et al. wang2021learning, and Li et al. li2021progressive employ GANs and VAEs to generate stylized variants of source data. AdvSTzheng2024advst treats data augmentations as learnable semantic transformations and uses them adversarially to generate diverse source samples.

##### Object Detection

Traditional object detectors such as Faster R-CNN ren2015faster form the backbone for most domain generalization studies, but they exhibit sharp performance degradation under domain shifts due to their reliance on source-specific statistics.
Early efforts to improve SDG for object detection
focused on architectural modifications to enhance representation invariance.
IterNorm huang2019iterative introduces a whitening-based normalization layer to reduce domain-specific covariance, while IBN-Net pan2018two and Switchable Whitening (SW) pan2019switchable leverage a combination of instance and batch normalization to decouple style and content features. These approaches demonstrated moderate gains but remained limited by their inability to model semantic shifts effectively.
More recent work such as ISW choi2021robustnet employs instance style whitening during training to improve robustness, whereas S-DGOD wu2022single proposes a specialized architecture tailored to single-source domain generalization by decoupling domain-variant and domain-invariant features. Notably, CLIP-Gap vidit2023clip incorporates pretrained VLMs
to bridge the semantic gap between source and target domains, demonstrating the potential of aligning high-level semantic features with textual priors in detection tasks.
These methods highlight the progression from normalization-based heuristics to semantically guided feature adaptation, marking a significant shift in SDG strategies for object detection.

### 2.2 Exploitation of Vision-Language Models

The advent of foundational VLMs
such as CLIP radford2021learning has redefined the landscape of representation learning, offering pre-trained multimodal encoders that exhibit strong transfer capabilities across a variety of downstream tasks. Notably, the text–image alignment learned by CLIP provides a rich semantic embedding space that remains stable across moderate domain shifts, making it a natural candidate for domain generalization (DG).
Recent efforts have explored integrating CLIP within DG pipelines to harness its robustness. CoCoOp zhou2022conditional introduces a prompt-tuning mechanism conditioned on visual features, showing that dynamically adapting textual prompts can significantly improve zero-shot generalization. CLIP-Adapter gao2024clip and Tip-Adapter zhang2022tip extend this line by proposing lightweight residual pathways that adapt CLIP to novel distributions while retaining its pretrained alignment. In parallel, Fort et al. fort2021exploring demonstrate that CLIP embeddings serve as effective detectors of distributional shift, outperforming dedicated uncertainty estimation techniques in out-of-distribution (OOD) detection.
VLMs have been employed to synthesize or hallucinate domain-variant supervision. UniDG zhang2023towards leverages CLIP to generate text-guided augmentations that simulate domain shifts, while recent retrieval-based methods shu2023clipood use textual descriptions to anchor unseen domains in the shared embedding space. Collectively, these works suggest that vision–language models not only encode invariant semantics but also offer actionable priors that downstream models can exploit to remain robust under distribution shift.
While CLIP remains the dominant vision–language model in domain generalization research, owing to its open accessibility and robust zero-shot transfer, emerging work has begun to explore alternatives. Addepalli et al. addepalli2024leveraging propose VL2V-SD and VL2V-ADiP, incorporating BLIP-2 li2022blip into self-distillation pipelines to improve out-of-distribution generalization in image classification. Despite their promise, such models remain underutilized in DG frameworks, largely due to limited public availability or incompatibilities with standard prompt-based architectures, which are tightly coupled with CLIP’s dual encoder design.

Figure: Figure 1: An illustration of the STAR framework with three components: Spectral Target Orientation (STO), Vision-Language Distillation (VLD), and Feature-Space Mixup (FSM). Given source images and a target text description as input, image features are extracted via a backbone network $f$, while the target description is mapped to a domain embedding $\phi^{t}$ using a frozen VLM text encoder $\mathcal{M}$. In STO, backbone features are re-centered around $\phi^{t}$ and projected onto a target-aligned subspace produced via SVD decomposition. The resulting features are fused with the original representations for supervised loss $\mathcal{L}_{\text{sup}}$ calculation. In VLD, backbone features are aligned with the image embeddings produced with a frozen VLM image encoder $\mathcal{M}^{\prime}$, providing semantic guidance through the distillation loss $\mathcal{L}_{\text{distill}}$. In FSM, feature space mixup is conducted to encourage smooth semantic transition across domains with the mixup loss $\mathcal{L}_{\text{mix}}$.
Refer to caption: x1.png

## 3 Method

### 3.1 Problem Setup

We propose Target-Oriented Single Domain Generalization (TO-SDG),
a novel setting where the goal is to leverage source domain data alongside a high-level specification of the target domain. Formally, we are given a labeled dataset $\mathcal{D}^{s}=\{(\mathbf{x}_{i},\mathbf{y}_{i})\}_{i=1}^{N}$ from a single source domain, where $\mathbf{x}_{i}\in\mathcal{X}$ and $\mathbf{y}_{i}\in\mathcal{Y}$. In addition, a textual description of the unseen target domain is provided, denoted by $T$.
The objective is to learn a predictive model $(h\circ f):\mathcal{X}\rightarrow\mathcal{Y}$ that generalizes to
the specified target domain, whose data remain unavailable during training.
The model is composed of a feature extractor $f_{\theta}:\mathcal{X}\rightarrow\mathcal{Z}$ parameterized by $\theta$,
and a predictor
$h_{\psi}:\mathcal{Z}\rightarrow\mathcal{Y}$ parameterized by $\psi$. The key challenge lies in utilizing the auxiliary target description $T$
to guide the learning process and promote generalization under domain shift.

### 3.2 Proposed Method

To tackle TO-SDG,
we propose STAR, a framework that injects target-specific semantic priors into the backbone feature space via a vision–language model such as CLIP and refines those representations through spectral editing and regularization.
STAR consists of three key components.
First, Spectral Target
Orientation (STO)
uses a frozen CLIP text encoder to extract a target domain embedding from the textual description.
This embedding anchors the backbone feature orientation through
first-order translation and spectral projection onto a low-rank, target-aligned subspace,
promoting generalizability towards
the target domain.
Second, Vision-Language Distillation (VLD) transfers semantic structure from CLIP image embeddings into the backbone features, aligning the representation geometry with that of a semantically rich, pretrained VLM.
Third, Feature-Space Mixup (FSM) interpolates between original and
target-oriented
features to smooth transitions across domains. The overall framework is illustrated in Figure [1](https://arxiv.org/html/2509.00351v1#S2.F1).

#### 3.2.1 Spectral Target Orientation

Robust generalisation from a single observed domain demands that the backbone
feature extractor $f_{\theta}$ acquires a representation that is anchored to the semantics of the target deployment environment while simultaneously remaining faithful to the discriminative structure of the source data.
We operationalize this intuition through Spectral Target Orientation (STO)
a two-stage procedure that (i) re-centres batch statistics around a language-derived target anchor and (ii) projects the resulting features onto a low-rank subspace whose directions are maximally aligned with that anchor.

##### Target Anchor Guided Translation

The procedure begins with a free-form textual description $T$ of the target environment.
A frozen text encoder $\mathcal{M}$ from a vision-language model, specifically CLIP, maps this description into the multimodal latent space, producing a target embedding

$$ $\phi^{t}=\mathcal{M}(T),\qquad\phi^{t}\in\mathbb{R}^{1\times d},$ (1) $$

where $d$ denotes the embedding dimension shared with the image encoder. The vector $\phi^{t}$ serves as an external semantic prior for the target domain: it encapsulates those high-level attributes that are expected to shift between source and target, and it does so without requiring any target images.

Given a mini-batch $X_{b}=\{\mathbf{x}_{i}\}_{i=1}^{n_{b}}$ of source samples, the backbone produces the feature matrix $Z=f_{\theta}(X_{b})$. Let $\mathbf{z}_{i}$ denote the $i$-th row of $Z$ and $\mathbf{\mu}^{z}=\tfrac{1}{n_{b}}\sum_{i=1}^{n_{b}}\mathbf{z}_{i}$ be the batch mean. Classical style-transfer huang2017arbitrary shows that first-order statistics largely account for appearance discrepancies; we therefore translate the batch so its centre of mass coincides with the target anchor:

$$ $\Omega^{t}_{i}=(\mathbf{z}_{i}-\mu^{z})+\phi^{t}.$ (2) $$

where $\Omega^{t}_{i}$ is the $i$-th row of the translated feature matrix $\Omega^{t}$. This affine shift removes source-specific bias while injecting target semantics. Crucially, because $\phi^{t}$ originates from the same CLIP space as the image embeddings, the shift operates in a modality-aligned coordinate system, avoiding the semantic drift that often plagues purely heuristic normalization schemes.

##### Spectral Target-Oriented Projection

Although the mean shift corrects first-order statistics, the features may still encode nuisances correlated with the source domain. To disentangle semantically meaningful factors from such noise, we perform singular value decomposition (SVD) on the recentered features:

$$ $\Omega^{t}=U^{t}\,\Sigma^{t}\,(V^{t})^{\!\top},$ (3) $$

where $U^{t}\in\mathbb{R}^{n_{b}\times n_{b}}$ and $V^{t}\in\mathbb{R}^{d\times d}$ are orthonormal and $\Sigma^{t}$ is
a diagonal-like matrix with non-negative singular values. Each column of $V^{t}$ provides an orthogonal direction in feature space, ranked by how strongly it varies after re-centring around $\phi^{t}$. Retaining only the top $k=\eta\cdot d$ directions, where $0<\eta<1$, we construct the truncated basis $V^{t}_{k}\in\mathbb{R}^{d\times k}$ that spans a target-aligned subspace, discarding low-variance noise while preserving transferable semantics. We map the backbone features into this subspace using the following projection matrix:

$$ $P^{t}=V^{t}_{k}(V^{t}_{k})^{\!\top},$ (4) $$

thereby isolating a low-rank subspace that captures target-aligned variance while suppressing low-energy directions dominated by source-domain noise. Projecting the original feature batch,
renormalizing and rescaling each sample gives target oriented features

$$ $\tilde{Z}=\frac{ZP^{t}}{\lVert ZP^{t}\rVert_{2}}\,\lVert Z\rVert_{2},$ (5) $$

where $\|\cdot\|_{2}$ denotes the $\ell_{2}$ norm. The final rescaling preserves the norm of each batch,
preventing the downstream predictor
from the impact of varied amplitudes.

Generalization can further benefit from stochastic feature-space augmentation.
Specifically, we apply standard weak image transformation on $X_{b}$ to obtain $\bar{X}_{b}$, which is
then processed identically as $X_{b}$ using
Eq ([2](https://arxiv.org/html/2509.00351v1#S3.E2))–Eq ([5](https://arxiv.org/html/2509.00351v1#S3.E5))
to produce $\bar{Z}$.
Averaging the two views,

$$ $\tilde{Z}_{\text{avg}}=(\tilde{Z}+\bar{Z})/2,$ (6) $$

smooths high-frequency artefacts introduced by augmentation and encourages the model to remain invariant along the geodesic connecting them in representation space.

Finally, we blend the target-oriented
features with the original representations,

$$ $\hat{Z}=(1-\alpha)\,Z+\alpha\,\tilde{Z}_{\text{avg}},$ (7) $$

with $\alpha\in[0,1]$ modulating the strength of orientation.
For convenience, the entire STO transformation via Eq ([2](https://arxiv.org/html/2509.00351v1#S3.E2))–Eq ([7](https://arxiv.org/html/2509.00351v1#S3.E7))
on a single sample $\mathbf{x}$ within its batch
can be written compactly as

$$ $\hat{\mathbf{z}}=g\bigl{(}f_{\theta}(\mathbf{x}),\phi^{t}\bigr{)},$ (8) $$

where $g(\cdot,\cdot)$ denotes the STO operator.
This operation modulates the feature representation by integrating semantic guidance from the target domain embedding $\phi^{t}$, and the target-oriented features are then used to minimize
the supervised empirical loss:

$$ $\mathcal{L}_{\text{sup}}(\theta,\psi)=\mathbb{E}_{(\mathbf{x},\mathbf{y})\in\mathcal{D}^{s}}\!\Bigl{[}\,\ell_{\text{sup}}\bigl{(}h_{\psi}(g(f_{\theta}(\mathbf{x}),\phi^{t})),\mathbf{y}\bigr{)}\Bigr{]}$ (9) $$

where $\ell_{\text{sup}}$ denotes the standard supervised loss—e.g., a cross-entropy loss for the classification task.

STO imposes a structured combination of operation steps on the source data features: a first-order
translation guided by the target embedding $\phi^{t}$, a spectral projection step that selectively eliminates source-specific variance, and a controlled blending of oriented and original features.
Together with supervised learning in the source domain,
these operation steps produce representations that reside on a submanifold optimized for maintaining discriminative performance on the source domain while effectively aligning with the semantics of the unseen target domain. Consequently, STO provides a principled mechanism for achieving robust generalization in the challenging TO-SDG setting.

#### 3.2.2 Vision–Language Distillation

While STAR’s spectral target orientation
steers features toward the direction of the target domain,
it does not by itself guarantee that the resulting representation retains the
semantic structure captured by
vision-language models–an essential property for meaningful and effective spectral target orientation.
We therefore introduce an auxiliary vision-language distillation term that transfers high-level semantics
from the fixed image encoder $\mathcal{M}^{\prime}$ of the adopted vision-language model, CLIP.
For every source image $\mathbf{x}$, we regress the backbone feature output $f_{\theta}(\mathbf{x})$
onto its CLIP image embedding $\mathcal{M}^{\prime}(\mathbf{x})$
via a squared distillation loss

$$ $\mathcal{L}_{\text{distill}}(\theta)=\mathbb{E}_{\mathbf{x}\in\mathcal{D}_{S}}\!\left\|\,f_{\theta}(\mathbf{x})\;-\;\mathcal{M}^{\prime}(\mathbf{x})\,\right\|_{2}^{\!2}.$ (10) $$

This coupling encourages the backbone representation to inherit CLIP’s global semantic geometry,
such as intra-class attributes and inter-class structures, without updating CLIP or accessing any target images. Empirically, the distillation term synergizes with STAR’s domain-aware editing, yielding representations that are simultaneously target-oriented and semantically well calibrated, thereby boosting
generalizability to the unseen target domain.

#### 3.2.3 Feature-Space Mixup

To enhance generalizability over the prediction model
and promote locally linear behaviour as features traverse from source-biased to target-aligned regions of the representation manifold, we adopt a feature–space Mixup strategy that operates after STAR’s spectral target orientation.
Concretely, for each batch we
apply a random permutation to the target-oriented samples
to obtain a re-indexed batch
$\{\hat{\mathbf{z}}^{\prime}_{i}\}_{i=1}^{n_{b}}$ along with their corresponding one-hot labels $\{\mathbf{y}^{\prime}_{i}\}_{i=1}^{n_{b}}$,
then form convex combinations

$$ $\mathbf{z}^{\text{mix}}_{i}=\beta\,\mathbf{z}_{i}+(1-\beta)\,\hat{\mathbf{z}}^{\prime}_{i},\qquad\mathbf{y}^{\text{mix}}_{i}=\beta\,\mathbf{y}_{i}+(1-\beta)\,\mathbf{y}^{\prime}_{i},$ (11) $$

where $\beta$, sampled from a Beta distribution, controls the interpolation strength.
Mixing in latent space, rather than pixels, couples the original source representation $\mathbf{z}_{i}=f_{\theta}(\mathbf{x}_{i})$ with
the target-oriented representation
$\hat{\mathbf{z}}^{\prime}_{i}=g(f_{\theta}(\mathbf{x}^{\prime}_{i}),\phi^{t})$.
The resulted mixup set $\mathcal{D}^{\text{mix}}=\{(\mathbf{z}^{\text{mix}}_{i},\mathbf{y}^{\text{mix}}_{i})\}_{i=1}^{n_{b}}$
feeds a standard supervised objective

$$ $\mathcal{L}_{\text{mix}}(\theta,\psi)=\mathbb{E}_{(\mathbf{z}^{\text{mix}},\mathbf{y}^{\text{mix}})\in\mathcal{D}^{\text{mix}}}\bigl{[}\ell_{\text{sup}}\!\left(h_{\psi}(\mathbf{z}^{\text{mix}}),\mathbf{y}^{\text{mix}}\right)\bigr{]},$ (12) $$

which synergizes with the primary supervised loss
and distillation term to
compel the decision surface to vary smoothly along semantic trajectories
that interpolate between source and target cues
and to foster representations that extrapolate gracefully to the unseen target domain.

#### 3.2.4 Overall Objective

The complete training objective integrates supervised and regularization terms to jointly optimize discriminative performance and semantic alignment:

$$ $\mathcal{L}_{\text{tr}}=\mathcal{L}_{\text{sup}}+\lambda_{\text{mix}}\mathcal{L}_{\text{mix}}+\lambda_{\text{distill}}\mathcal{L}_{\text{distill}},$ (13) $$

where $\lambda_{\text{mix}}$ and $\lambda_{\text{distill}}$ are trade-off hyperparameters
that control the influence of the feature-space Mixup supervision and vision–language distillation losses,
respectively. This composite objective enforces that the model learns features that are both robust to distributional shifts and semantically consistent with the target domain.

**Table 1: Classification accuracy (%) and standard deviation on the PACS dataset using "Photo" as the source domain. Each row corresponds to a different target domain.**
| Target | MixUp | CutOut | ADA | ME-ADA | AugMix | RandAug | ACVC | L2D | PR-C | Ours |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Art | 52.8 | 59.8 | 58.0 | 60.7 | 63.9 | 67.8 | 67.8 | 67.6 | - | $\mathbf{75.3}_{(0.4)}$ |
| Cartoon | 17.0 | 21.6 | 25.3 | 28.5 | 27.7 | 28.9 | 30.3 | 42.6 | - | $\mathbf{52.5}_{(0.3)}$ |
| Sketch | 23.2 | 28.8 | 30.1 | 29.6 | 30.9 | 37.0 | 46.4 | 47.1 | - | $\mathbf{53.1}_{(0.4)}$ |
| Avg. | 31.0 | 36.7 | 37.8 | 39.6 | 40.8 | 44.6 | 48.2 | 52.5 | 57.1 | 60.3 |

**Table 2: Classification accuracy and standard deviation(%) on the DomainNet dataset using "Real" as the source domain. Each column corresponds to a different target domain.**
| Method | Painting | Infograph | Clipart | Sketch | Quickdraw | Avg. |
| --- | --- | --- | --- | --- | --- | --- |
| MixUp zhang2018mixup | 38.6 | 13.9 | 38.0 | 26.0 | 3.7 | 24.0 |
| CutOut devries2017improved | 38.3 | 13.7 | 38.4 | 26.2 | 3.7 | 24.1 |
| CutMix yun2019cutmix | 38.3 | 13.5 | 38.7 | 26.9 | 3.6 | 24.2 |
| ADA volpi2018generalizing | 38.2 | 13.8 | 40.2 | 24.8 | 4.3 | 24.3 |
| ME-ADA Long2020Maximum | 39.0 | 14.0 | 41.0 | 25.3 | 4.3 | 24.7 |
| RandAug cubuk2020randaugment | 41.3 | 13.6 | 41.1 | 30.4 | 5.3 | 26.3 |
| AugMixhendrycks2019augmix | 40.8 | 13.9 | 41.7 | 29.8 | 6.3 | 26.5 |
| ACVC Cugu_2022_CVPR | 41.3 | 12.9 | 42.8 | 30.9 | 6.6 | 26.9 |
| AdvST zheng2024advst | $42.3_{(0.1)}$ | $14.8_{(0.2)}$ | $43.5_{(0.4)}$ | $30.8_{(0.3)}$ | $5.9_{(0.2)}$ | 27.1 |
| STAR (Ours) | $\mathbf{45.9_{(0.1)}}$ | $\mathbf{17.1_{(0.3)}}$ | $\mathbf{45.7_{(0.6)}}$ | $\mathbf{35.2_{(0.5)}}$ | $\mathbf{9.2_{(0.3)}}$ | $\mathbf{30.0}$ |

**Table 3: Mean Average Precision (mAP) results on the Diverse-Weather dataset with "Day Clear" as the source domain. Each column represents performance on a different target domain.**
| Method | Day Clear | Night Clear | Dusk Rainy | Night Rainy | Day Foggy |
| --- | --- | --- | --- | --- | --- |
| Faster-RCNN ren2015faster | 48.1 | 34.4 | 26.0 | 12.4 | 32.0 |
| IterNorm huang2019iterative | 43.9 | 29.6 | 22.8 | 12.6 | 28.4 |
| SW pan2019switchable | 50.6 | 33.4 | 26.3 | 13.7 | 30.8 |
| IBN-Net pan2018two | 49.7 | 32.1 | 26.1 | 14.3 | 29.6 |
| ISW choi2021robustnet | 51.3 | 33.2 | 25.9 | 14.1 | 31.8 |
| S-DGOD wu2022single | 56.1 | 36.6 | 28.2 | 16.6 | 33.5 |
| CLIP-Gap vidit2023clip | 51.3 | 36.9 | 32.3 | 18.7 | 38.5 |
| STAR (Ours) | 58.3 | 39.5 | 35.2 | 21.0 | 40.4 |

**Table 4: Per-class Average Precision (AP) results for object detection on the Diverse-Weather dataset, using "Day Clear" as the source domain and "Dusk Rainy" as the target domain.**
| Method | bus | bike | car | motor | person | rider | truck | mAP |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Faster-RCNN ren2015faster | 28.5 | 20.3 | 58.2 | 6.5 | 23.4 | 11.3 | 33.9 | 26.0 |
| IterNorm huang2019iterative | 32.9 | 14.1 | 38.9 | 11.0 | 15.5 | 11.6 | 35.7 | 22.8 |
| SW pan2019switchable | 35.2 | 16.7 | 50.1 | 10.4 | 20.1 | 13.0 | 38.8 | 26.3 |
| IBN-Net pan2018two | 37.0 | 14.8 | 50.3 | 11.4 | 17.3 | 13.3 | 38.4 | 26.1 |
| ISW choi2021robustnet | 34.7 | 16.0 | 50.0 | 11.1 | 17.8 | 12.6 | 38.8 | 25.9 |
| S-DGOD wu2022single | 37.1 | 19.6 | 50.9 | 13.4 | 19.7 | 16.3 | 40.7 | 28.2 |
| CLIP-Gap vidit2023clip | 37.8 | 22.8 | 60.7 | 16.8 | 26.8 | 18.7 | 42.4 | 32.3 |
| Ours | 41.4 | 26.1 | 62.7 | 21.9 | 30.0 | 20.5 | 43.8 | 35.2 |

## 4 Experiments

### 4.1 Experimental Setup

##### Datasets

Our experimental evaluation utilizes three benchmark datasets.
Our evaluation is conducted on three diverse and challenging benchmarks designed to test generalization under significant domain shift.
PACS li2017deeper comprises four stylistically distinct domains: Art, Cartoon, Photo, and Sketch, sharing seven object categories. We follow the standard SDG protocol by using the Photo domain as the sole source and treating the remaining domains as unseen targets.
DomainNet peng2019moment, the most complex benchmark in our study, spans six highly heterogeneous domains: Real, Infograph, Clipart, Painting, Quickdraw, and Sketch, covering 345 object classes. We designate the Real domain as the source and evaluate generalization on the remaining five, which present substantial intra-class variability and domain shift.
Diverse-Weather wu2022single is an urban scene segmentation benchmark constructed to evaluate robustness under extreme weather variation. It includes five domains: Daytime Clear, Night Clear, Dusk Rainy, Night Rainy, and Daytime Foggy. We adopt Daytime Clear as the source domain.

##### Experimental Details

Our experiments follow the standard setup used in prior SDG studies for image classification and object detection zheng2024advst; wu2022single. Specifically for STAR, we use the pretrained CLIP model as the vision–language backbone. For each target domain, the corresponding domain name (e.g., "Art Painting", "Night Rainy", etc.), is used as the textual description $T$. We set the STO blending coefficient $\alpha=0.9$, the Mixup weight $\lambda_{\text{mix}}=0.5$, the vision–language alignment weight $\lambda_{\text{distill}}=0.01$, and the spectral truncation ratio $\eta=0.5$. Detailed configurations are provided in Appendix [B](https://arxiv.org/html/2509.00351v1#A2).

### 4.2 Comparison Results

We evaluate our method against a broad spectrum of baselines. For image classification, we compare with MixUp zhang2018mixup, CutOut devries2017improved, CutMix yun2019cutmix, AutoAugment cubuk2018autoaugment, RandAugment cubuk2020randaugment, and AugMix hendrycks2019augmix, ACVC Cugu_2022_CVPR, ERM koltchinskii2011oracle, CCSA motiian2017unified, JiGen carlucci2019domain, ADA volpi2018generalizing, ME-ADA Long2020Maximum, RSDA volpi2019addressing, L2D wang2021learning, PDEN li2021progressive, and AdvST zheng2024advst. All classification experiments are conducted using a ResNet-18 backbone for fair comparison.
For object detection, we evaluate our framework against established baselines and DG-specific variants. We include Faster R-CNN ren2015faster, IterNorm huang2019iterative, Switchable Whitening (SW) pan2019switchable, IBN-Net pan2018two, Instance Style Whitening (ISW) choi2021robustnet, S-DGOD wu2022single and CLIP-Gap vidit2023clip, all implemented with a ResNet-101 backbone.

Table [1](https://arxiv.org/html/2509.00351v1#S3.T1) presents the classification results on the PACS dataset using ResNet-18 as the backbone. Our proposed method, STAR, consistently outperforms prior approaches across all target domains, achieving the highest average accuracy of 60.3%, surpassing the next-best method PR-C by a notable margin of 3.2%. STAR sets a new state of the art in the "Art" domain with 75.3%, outperforming ACVC; achieves 52.5% on "Cartoon," exceeding L2D by 9.9%; and reaches 53.1% on "Sketch," outperforming L2D by 6.0%. These gains highlight STAR’s ability to robustly generalize under severe domain shifts.

Table [2](https://arxiv.org/html/2509.00351v1#S3.T2) reports performance on the DomainNet benchmark with ResNet-18 as the backbone. STAR achieves the highest average accuracy of 30.0%, outperforming the strongest prior method, AdvST, across all five target domains. The most pronounced gain is observed in the "Sketch" domain, where STAR improves accuracy by 4.4%, highlighting its robustness under severe domain shifts.

Table [3](https://arxiv.org/html/2509.00351v1#S3.T3) reports single-domain generalization performance for object detection on the Diverse-Weather benchmark, measured in mean Average Precision (mAP) across five unseen target domains. STAR outperforms all prior methods by a clear margin across all conditions, including severe low-light and adverse weather scenarios.
Compared to the strongest baseline, CLIP-Gap, which leverages vision–language pretraining to mitigate domain shift, STAR delivers consistent and sizable improvements. On "Day Foggy," STAR achieves 40.4 mAP, outperforming CLIP-Gap by 1.9 points; on the challenging "Night Rainy" setting, STAR reaches 21.0 mAP, a 2.3-point gain over CLIP-Gap. Notably, in "Dusk Rainy" and "Night Clear," STAR surpasses CLIP-Gap by 2.9 and 2.6 points respectively, reflecting superior robustness to both illumination variance and atmospheric degradation. On the source domain ("Day Clear"), STAR also outperforms all baselines, achieving 58.3 mAP, indicating that the proposed target-oriented conditioning does not compromise in-domain performance.

Table [4](https://arxiv.org/html/2509.00351v1#S3.T4) reports object detection performance on the Dusk Rainy scene, which presents a substantial domain shift from the source (Daytime Clear) due to the joint challenges of low visibility and rain-induced noise. While performance across most object categories remains competitive with prior methods, STAR demonstrates clear improvements in key dynamic object classes, achieving gains of 3.6% for "bus", 4.8% for "motor", and 3.2% for "person". These results highlight STAR’s capacity to enhance generalization under adverse visual conditions, particularly for mobile entities critical to downstream decision-making in real-world systems.
Additional per-class object detection results are presented in Appendix [C](https://arxiv.org/html/2509.00351v1#A3).

### 4.3 Ablation Studies

#### 4.3.1 Ablation on Vision–Language Models

**Table 5: Ablation studies classification on different VLM choice. Accuracy and standard deviation(%) comparison on the PACS dataset.**
| Target | $-\text{w BLIP }$ | $-\text{w LLaVA }$ | - w CLIP (Ours) |
| --- | --- | --- | --- |
| Art | $74.1_{(0.6)}$ | $73.9_{(0.8)}$ | ${75.3}_{(0.4)}$ |
| Cartoon | $51.3_{(0.4)}$ | $52.0_{(0.7)}$ | ${51.5}_{(0.3)}$ |
| Sketch | $51.6_{(0.6)}$ | $51.1_{(0.7)}$ | ${52.7}_{(0.4)}$ |
| Avg. | 59.0 | 59.0 | 59.8 |

We conduct an ablation study to assess the effect of different target text encoders on STAR’s performance. Specifically, we compare three categories of pretrained models for deriving the domain embedding $\phi^{t}$: BLIP li2022blip, a vision–language model optimized for image–text alignment; LLaVA liu2023visual, a multimodal large language model (MLLM) tuned for visual instruction-following; and CLIP, our default choice, which is pretrained with contrastive supervision over image–text pairs at scale.
Table [5](https://arxiv.org/html/2509.00351v1#S4.T5) summarizes classification accuracy across target domains in PACS. While all variants perform competitively, CLIP consistently yields the highest average accuracy (59.8%), outperforming BLIP and LLaVA by 0.8% on average. Notably, CLIP excels in the Art and Sketch domains, suggesting its embeddings more faithfully preserve visual-domain semantics that are useful for feature alignment. Although LLaVA slightly outperforms other models on the Cartoon domain, it exhibits higher variance and underperforms on other shifts, likely due to its instruction-tuning objective being less aligned with low-level visual representation.

#### 4.3.2 Ablation on Different Components

Table [6](https://arxiv.org/html/2509.00351v1#S4.T6) presents a comprehensive ablation study assessing the contribution of each component in the STAR framework
on PACS dataset by using Resnet-18 as the backbone network. In particular, we compared the full model STAR with the following variants:
(1) "$-\text{w/o }\mathcal{L}_{\text{distill}}$" which drops the distillation loss;
(2) "$-\text{w/o }\mathcal{L}_{\text{sup}}$" which omits the primary classification objective;
(3) "$-\text{w/o }\mathcal{L}_{\text{mix}}$" which disables feature-space Mixup;
(4) "$-\text{w/o projection}$" which bypasses the spectral target-oriented, keeping re-centered features without projecting onto the target-aligned subspace;
(5) "$-\text{w bottom }k$" which replaces the top-$k$ projection with a projection onto the lowest-variance components, testing whether performance gains arise from dimensionality reduction alone or from semantically meaningful axes; and
(6) "$-\text{w/o Aug}$" which removes data augmentation.

Removing the classification loss $\mathcal{L}_{\text{cl}}$, which serves as the primary supervised signal, results in a dramatic degradation in performance, with an average accuracy of just 42.4%, confirming its foundational role in grounding the target-conditioned features with task-specific supervision. Excluding the vision–language distillation term $\mathcal{L}_{\text{distill}}$ leads to a 4-point drop in average accuracy (from 59.8% to 55.8%), indicating that semantic guidance from the CLIP teacher effectively regularizes the feature space and enhances cross-domain alignment.
The impact of spectral projection is also significant. When removed, accuracy drops to 51.1%, illustrating the importance of subspace filtering in eliminating source-domain noise and enhancing target-relevant directions. A control variant that projects onto the bottom-$k$ singular vectors further confirms this interpretation, yielding inferior performance (54.9%) compared to top-$k$ projection, consistent with the hypothesis that principal components encode the most informative semantic variation.
Disabling Mixup ($\mathcal{L}_{\text{mix}}$) causes a measurable decline across all domains, with average accuracy falling to 56.5%, underscoring the utility of interpolation-based regularization in bridging source and target representations. Finally, removing the image-space augmentation used in generating alternate views for spectral alignment results in a performance drop to 57.6%, indicating that even modest stochastic perturbations help stabilize the learned target-conditioned subspace.
Together, these results affirm the complementary nature of STAR’s components.

**Table 6: Ablation studies classification accuracy and standard deviation(%) comparison on the PACS dataset using "Photo" as the source domain. Each row corresponds to a different target domain.**
| Target | $-\text{w/o }\mathcal{L}_{\text{distill}}$ | $-\text{w/o }\mathcal{L}_{\text{sup}}$ | $-\text{w/o }\mathcal{L}_{\text{mix}}$ | $-\text{w/o projection}$ | $-\text{w bottom }k$ | $-\text{w/o Aug}$ | STAR (Ours) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Art | $69.7_{(0.5)}$ | $49.1_{(0.9)}$ | $70.1_{(0.5)}$ | $65.9_{(0.9)}$ | $65.9_{(0.9)}$ | $73.2_{(0.5)}$ | $\mathbf{75.3}_{(0.4)}$ |
| Cartoon | $48.3_{(0.4)}$ | $38.7_{(0.6)}$ | $49.0_{(0.5)}$ | $43.6_{(0.7)}$ | $49.0_{(0.8)}$ | $49.6_{(0.8)}$ | $\mathbf{52.5}_{(0.3)}$ |
| Sketch | $49.6_{(0.6)}$ | $39.6_{(0.5)}$ | $50.6_{(0.1)}$ | $44.0_{(0.6)}$ | $49.8_{(0.5)}$ | $50.1_{(0.7)}$ | $\mathbf{53.1}_{(0.4)}$ |
| Avg. | 55.8 | 42.4 | 56.5 | 51.1 | 54.9 | 57.6 | 60.3 |

## 5 Conclusion

In this paper, we introduce Target-Oriented Single Domain Generalization (TO-SDG),
a new problem setup that
augments the standard SDG problem with
a simple natural language text description of the target domain.
To address the challenges of TO-SDG,
we propose a novel approach, Spectral Target Alignment (STAR),
which aligns source features with target deployment-domain semantics
via text-guided spectral target orientation, vision-language
distillation, and feature-space Mixup augmentation,
effectively purging source-specific biases while preserving discriminative structure.
Experiments across image classification and object detection benchmarks demonstrate
STAR’s superiority over prior methods.
By harnessing freely available textual metadata,
STAR establishes a practical pathway toward robust
model deployment in unseen domains.

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## Appendix A Training Algorithm

The full training procedure of our proposed STAR approach is detailed in Algorithm [1](https://arxiv.org/html/2509.00351v1#alg1).

## Appendix B Experimental Details

In our experiments, following the previous studies zheng2024advst, for the PACS dataset, a pre-trained ResNet-18 on ImageNet was fine-tuned on the source domain with images resized to $224\times 224$. The setup included 50 epochs, batch size of 512, and a learning rate of 0.001 adjusted according to a cosine annealing scheduler. The same ResNet-18 backbone was used for the DomainNet dataset, with the experiments set to 200 epochs, and a batch size of 512, with the learning rate also following a cosine annealing pattern. All experiments were conducted with each setup replicated five times using different random seeds to ensure statistical reliability, and results were reported as the average accuracy with standard deviations.
Following standard practice in single-domain generalization for object detection, we adopt Faster R-CNN ren2015faster with a ResNet-101 backbone he2016deep as our detection model.
Our modification is limited to the classification head, where we replace the original classification loss with a cross-entropy loss computed on target-conditioned features produced by our STO module. In addition to this modified classification loss, we incorporate two auxiliary objectives: a vision–language distillation loss, and a feature-space Mixup loss. The detection pipeline, region proposal network, and bounding box regression components remain unchanged.
We train the model in 1000 iterations. The learning optimizer is SGD with a weight decay of 0.0005, and the learning rate is 0.001.
In all object detection experiments, we evaluate performance using the Mean Average Precision (mAP) metric. Specifically, following the protocol in wu2022single, we report mAP$@$0.5, which considers a prediction to be a true positive if it correctly matches the ground-truth class label and achieves an Intersection over Union (IoU) score greater than 0.5 with the corresponding ground-truth bounding box. This threshold provides a balanced evaluation of both localization accuracy and semantic correctness.

## Appendix C Object Detection Per-class Comparison Results

To better understand the strengths and limitations of each method across different semantic categories, we provide detailed per-class Average Precision (AP) results for object detection under various target weather conditions. These results allow for a finer-grained analysis of how well models generalize across challenging domain shifts, particularly for safety-critical object classes.

Table [7](https://arxiv.org/html/2509.00351v1#A0.T7) presents detection results on the Night Clear scene, a particularly challenging target domain characterized by the compounded effects of low illumination and adverse weather. This composite shift introduces a significant discrepancy from the source (Daytime Clear), severely degrading model performance due to both photometric and structural variations. Despite these conditions, STAR achieves a 3.0% improvement in overall mAP over the strongest competing method, with notable gains of 3.3% and 1.9% in the "person" and "rider" categories, respectively. These results underscore the robustness of STAR in scenarios where conventional models struggle, validating its effectiveness under severe domain shifts.

Table [8](https://arxiv.org/html/2509.00351v1#A0.T8) presents per-class detection performance when adapting from Daytime Clear to the highly challenging Night Rainy domain, which combines severe low-light degradation with weather-induced visual noise. This scenario introduces compounded appearance shifts that substantially hinder generalization. Despite this, STAR outperforms all baselines across every evaluated object category, achieving the highest overall mAP of 21.0%, a 2.3 point improvement over the strongest prior method, CLIP-Gap. Notably, STAR delivers substantial gains in safety-critical classes such as "motor" with 2.7%, "person" with 2.6%, and "rider" with 2.4% improvement, demonstrating its ability to recover object semantics under extreme visual distortion. These results highlight STAR’s robustness in high-stakes, low-visibility environments where conventional models experience pronounced failures.

Table [9](https://arxiv.org/html/2509.00351v1#A0.T9) reports per-class detection performance when transferring from the Daytime Clear source domain to the Daytime Foggy target domain within the Diverse-Weather benchmark. This setting simulates real-world visibility degradation due to atmospheric scattering, which blurs contours and attenuates contrast, which are factors known to degrade detector reliability. STAR achieves the highest mean Average Precision (mAP) of 40.4%, outperforming the strongest baseline, CLIP-Gap, by 1.9 points. Across all seven object categories, STAR attains state-of-the-art results, with particularly notable gains in "motor" with 2.2%, "person" with 2.2%, and "truck" with 2.6% improvement. These improvements highlight the effectiveness of STAR’s target-aware spectral conditioning in maintaining spatial and semantic precision under fog-induced ambiguity.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 2
presentation: 2
questions: 1- What is the difference between TO-SDG and SDG setting? Also, in this setting, don't we need to adapt to different domains separately? This is more like a domain adaptation setting than generalization? I believe addressing this point is essential, as it seems like the task is the main contribution of this paper. Specifically, each component of STAR method has been extensively studied for DA/DG domain. E.g., [7-8] leverage image-text transferability property in the clip space, AdaIN (as scited in the paper) uses batch statistics for augmentation and interpolation between samples and the knowledge distillation has been studied in many works such as studend-teacher framework. 

2- A comprehensive comparison against zero-shot DA methods is needed both in related works and experimental results.

3- Are image encoder backbone and clip text encoder in the same space? if not how would Eq2 and L239 hold?
soundness: 2
strengths: I appreciate the comprehensiveness of the experimental setup on multiple benchmarks, different types of domain shift types, backbones and different tasks. This helps to understand effectivness of generalization the proposed method in various settings.
summary: The paper studies the task of generalization on the target domain when only the source data and high-level information about the target domain (i.e., target-oriented Single-Domain Generalization). The paper claims that this is a novel and more practical setting as it lets you to utilize lightweight information regarding the target domain. To tackle TO-SDG, they propose the Spectral Target Alignment (STAR) method. STAR consists of three main components: (1) re-centers the features towards the target domain using CLIP encoded target's textual information, (2) Aligns image encoder features with CLIP's features via a distillation objective, and (3) boosts generalization by interpolating between source and target cues/features. 

The paper comprehensively compares STAR on TO-SDG with baselines such as open vocabulary object detection and single-domain generalization (SDG) methods, as well as classification methods. Experimental results show that STAR is more effective than these baselines on different types of benchmarks (e.g., adverse-weather generalization and artistic domain shift).
weaknesses: 1- The paper proposes a new task, Target-Oriented Single Domain Generalization (TO-SDG), where only high-level information from the target domain (text description) is available. However, this setting is essentially **very similar or identical to zero-shot domain adaptation (ZDA)**, which has been extensively studied. **The paper neither explains the differences from existing ZDA formulations nor compares with representative methods**. Some relevant works are [1–6]. The implementation details and how different backbones (e.g., different models or different resnets) are adopted needs to be better explained in the implementation details. 

2- Several parameters and model choices are unclear. For instance, what versions  CLIP or LLaVA variants are used? how the LLaVA language model is employed to encode textual descriptions?  Clearer descriptions are needed for reproducibility and understanding.

3- In Eq. (2) and L688, the image encoder appears to be ImageNet-pretrained, not CLIP-pretrained. If so, the image features and CLIP text embeddings lie in different spaces, and the claim in L239 ("because φt originates from the same CLIP space as the image embeddings, the shift operates in a modality-aligned coordinate system, avoiding the semantic drift that often plagues purely heuristic normalization schemes,") would not hold? I'd appreciate if this could be further clarified

4- Some statements are inaccurate or exaggerated. For example, L52:  "Yet all prior CLIP-based robustness methods ultimately rely on target images ...." is not accurate and may need to be adjusted. For instance, methods such as CLIP-the-Gap [7] and prompt-tuning approaches like CoOp and CoCoOp do not require target data. While CoOp/CoCoOp adapt prompts using labeled source data (e.g., ImageNet), they are evaluated on unseen target domains (e.g., ImageNet-V2, Sketch, A, R), demonstrating genuine generalization without target access.



[1] Unified Language-driven Zero-shot Domain Adaptation, CVPR 2024
[2]  PØDA: Prompt-driven Zero-shot Domain Adaptation, ICCV 2023
[3] Zero-Shot Deep Domain Adaptation, ECCV 2018
[4] Text-driven Zero-shot Domain Adaptation with Cross-modality Graph Motif Matching
[5] Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation, CVPR 2024
[6]  UPRE: Zero-Shot Domain Adaptation for Object Detection via Unified Prompt and Representation Enhancement, ICCV 2025
[7] CLIP the Gap: A Single Domain Generalization Approach for Object Detection, CVPR 2023 
[8] PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization

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confidence: 4
contribution: 3
initial_rating: 4
presentation: 2
questions: 1. What is the per-iteration overhead of STO (SVD + projection) relative to baseline training for classification and detection?

2. What happens if T is partially wrong or noisy? Can you sweep paraphrases/synonyms to test stability?
soundness: 3
strengths: 1.  The paper is well organized and easy to follow.

2. Consistent improvements in both classification (PACS, DomainNet) and detection (Diverse-Weather).
summary: This paper proposes Target-Oriented Single-Domain Generalization (TO-SDG), where a model trained on one labeled source domain leverages a text description of the unseen target domain. The authors introduce STAR (Spectral TARget Alignment), which aligns source features to a CLIP text anchor, projects them onto a target-oriented subspace via SVD, applies vision–language distillation, and performs feature-space Mixup. STAR achieves notable improvements on PACS, DomainNet, and Diverse-Weather benchmarks.
weaknesses: 1. For classification, baselines focus heavily on augmentation-style SDG methods with ResNet-18. It would be informative to see stronger modern backbones (e.g., ConvNeXt/Vit-B) and CLIP-tuned baselines.

2. The ablation removes “projection” entirely or uses bottom-k, but it would be helpful to plot performance vs k (η) across domains (beyond a single sensitivity curve) and report the standalone effect of the mean-shift without projection to disentangle the two parts of STO more cleanly.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 2
questions: Please refer to weaknesses.
soundness: 2
strengths: 1. The proposed method contributes a simple yet effective framework that incorporates target domain text descriptions.
summary: This paper introduces a Target-Oriented Single Domain Generalization (TO-SDG), which leverages textual descriptions from the target domain during training to improve generalization from a single source domain. To bridge the semantic gap between source and target domains, the authors propose a Spectral Target Alignment framework composed of three components: a spectral target orientation module, a vision-language distillation strategy, and a feature-space mixup technique. Extensive experiments across three standard benchmarks demonstrate the method's effectiveness and improvements over baseline approaches.
weaknesses: 1. The experimental section lacks comparisons with several state-of-the-art single domain generalization (SDG) approaches, such as [1-3], many of which demonstrate stronger performance. The omission of these baselines limits the confidence of the performance claims and weakens the empirical contribution of the work.
[1] Chen, Jin, et al. "Meta-causal learning for single domain generalization." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.
[2] Li, Deng, et al. "Prompt-driven dynamic object-centric learning for single domain generalization." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.
[3] Xu, Zhipeng, et al. "Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization." Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.

2. The technical contribution of this method is insufficient. The Vision-Language Distillation is very common, and has been proposed in many literatures, e.g.,[1].
[1] Yang, Chuanguang, et al. "Clip-kd: An empirical study of clip model distillation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.
The feature-level mixup that fuses source feature with target-oriented feature, which is similar to the domain-level mixup in literatures such as [2],
[2] Cao, Meng, and Songcan Chen. "Mixup-induced domain extrapolation for domain generalization." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 10. 2024.
Please show comparisons with these related works, and provide detailed analysis on how your motivation and method differ from these ones.

3. While the proposed TO-SDG task is conceptually novel, its real-world applicability is debatable. In practical scenarios, target domain styles (e.g., “art painting”) are often abstract, noisy, or multi-modal, making it difficult to summarize them with a single descriptive phrase. In this perspective, the proposed setting risks being seen as a promising research task with limited practicality. It’s recommended to discuss the limitations of TO-SDG more explicitly in your manuscript, and consider to conduct experiments on more existing settings.

4. The evaluation on PACS is only conducted using the ‘photo’ domain as the source, which is inconsistent with standard SDG protocols. Typically, performance is averaged over all possible single-source domain settings to ensure fairness and robustness. It’s recommended to extend your experiments to include all four PACS domains as source domains. 

5. SDG methods are mostly evaluated under multiple benchmarks for the image classification task. It’ll be better to provide results on Digits/DomainNet.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: The authors need to make an effort to show how much gain is obtained from their proposed main module. So far, many factors are involved, and not easy to see the pure gain from their novel approach. Please respond to the weaknesses above. 

Minor comments. Some tables are far from the corresponding sentences. Better to fix it.
soundness: 2
strengths: 1. The idea of injecting the target domain's meta information into the image encoder is interesting and sounds reasonable. 
2. The proposed approach seems to be strong in both image classification and object detection. 
3. Empirical results are strong, though I am not sure if the results support their claims enough.
summary: This paper proposes a new approach for single-domain generalization where they aim to inject the target domain's information, e.g., art or foggy, to let a model change its behavior depending on the target domain. Specifically, they employ the CLIP model to encode such information given as text and fuse the information into the image embedding. Additionally, they employ distillation loss from the CLIP model and a feature-level mixup approach. Empirically, their approach outperforms baseline methods with a large margin in image classification and object detection.
weaknesses: 1. Their main claim is about the effectiveness of the textual description of the target domain. However, their experiments and methods are not well-designed to support their claim. It is true that combining several techniques outperforms existing approaches, yet it is not clear whether the improvement comes from their main claim. For example, according to Table 6, distillation from the CLIP model appears to significantly improve performance, suggesting that the use of CLIP enhances performance. 

2. Related to 1, it is not clear if the comparison in results is fair in terms of backbone, e.g., Table 2. If they employ the CLIP model during training and other baselines do not have access to CLIP, the comparison might not be fair. 

3. Is the feature-level mix-up not explored in domain generalization? The technique was proposed a long time ago, and applying it to the DG problem sounds like a simple idea. Then, the use of feature mix-up is not very novel.


Decision: Reject