Leveraging Depth and Language for Open-Vocabulary Domain-Generalized Semantic Segmentation
Authors: Siyu Chen
Ting Han
Chengzheng Fu
Changshe Zhang
Chaolei Wang
Jinhe Su
Guorong Cai
Meiliu Wu
Venue: NeurIPS 2025 poster ()
Keywords: Domain Generalized
Open-Vocabulary
Depth Aware
Semantic Segmentation
Attention optimization
Primary area: applications

Abstract: Open-Vocabulary semantic segmentation (OVSS) and domain generalization in semantic segmentation (DGSS) highlight a subtle complementarity that motivates Open-Vocabulary Domain-Generalized Semantic Segmentation (OV-DGSS). OV-DGSS aims to generate pixel-level masks for unseen categories while maintaining robustness across unseen domains, a critical capability for real-world scenarios such as autonomous driving in adverse conditions. We introduce Vireo, a novel single-stage framework for OV-DGSS that unifies the strengths of OVSS and DGSS for the first time. Vireo builds upon the frozen Visual Foundation Models (VFMs) and incorporates scene geometry via Depth VFMs to extract domain-invariant structural features. To bridge the gap between visual and textual modalities under domain shift, we propose three key components: (1) GeoText Query, which align geometric features with language cues and progressively refine VFM encoder representations; (2) Coarse Mask Prior Embedding (CMPE) for enhancing gradient flow for faster convergence and stronger textual influence; and (3) the Domain-Open-Vocabulary Vector Embedding Head (DOV-VEH), which fuses refined structural and semantic features for robust prediction. Comprehensive evaluation on these components demonstrates the effectiveness of our designs. Our proposed Vireo achieves the state-of-the-art performance and surpasses existing methods by a large margin in both domain generalization and open-vocabulary recognition, offering a unified and scalable solution for robust visual understanding in diverse and dynamic environments. Code is available at https://github.com/SY-Ch/Vireo.

Leveraging Depth and Language for Open- Vonn-ooaaullay Domain-Generalized Semantic Segmentation
Siyu Chen
1.2 1.2f
Ting 23
Chengzheng Fu+
Changshe Zhang Meiliu Wu?
Chaolei Wang"
Jinhe Su' Guorong Cai
Jimei University, 2 University of Glasgow, Sun Yat-sen University,
Nanjing University of Aeronautics and Astronautics, Xidian University,
Abstract
neen-Vocabulary semantic segmentation (OVSS) and domain generalization in semantic segmentation (DGSS) highlight subtle complementarity that motivates Open-Vocabulayy
Semantic Segmentation (OV-DGSS), OV- DGSS aims to generate pixel-level masks for unseen categories while maintaining robustness across unseen domains, critical capability for real-world scenarios such as autonomous driving in adverse conditions. We introduce Vireo, a novel single-stage framework for OV-DGSS that unifies the strengths of OVSS and DGSS for the first time. Vireo builds upon the frozen Visual Foundation Models (VFMs) and incorporates scene geometry via Depth VFMs to extract domain-invariant structural features. To bridge the gap between visual and textual modalities under domain shift, propose three key components:
(1) GeoText Query, which align geometric features with language cues and progressively refine VFM encoder representations; (2) Coarse Mask Prior Embedding (CMPE) for enhancing gradient flow for faster convergence and stronger textual influence; and (3) the Domain- Voccb-layy Vector Embedding Head (DOV-VEH), which fuses refined structural and semantic features for robust prediction. Comprehensive evaluation on these components demonstrates the effectiveness of our designs. Our proposed Vireo achieves the state-of-the-as performance and surpasses existing methods by large margin in both domain generalization and open- open-vocabulary recognition, offering unified and scalable solution for robust visual understanding in diverse and dynamic environments. Code is available at /https://github.com. cww.:tthhu.ccc....... Ch/Virea
1 Introduction
Open-Vocce Sonn-ooabullryy Domain-Generalizer Semantic Segmentation (OV-DGSS) denotes the joint exe- cution of open-vocabulary semantic segmentation (OVSS) 3 4 and domain generalization in semantic segmentation (DGSS) 6 g tasks. It involves training model that, without access to target-domain samples annotations for novel categories, can generate pixel-wise segmentation for unseen classes while sustaining high performance across previously unseen domains (such as different cities, lighting environments, or climatic conditions). For instance, when an autonomous vehicle receives query like "Can I park next to that bollard?", its perception system must comprehend the linguistic input and accurately segment the referenced object the pixel level-even under adverse conditions such as dim lighting, rain-streakes lenses, or region-specific visual appearances.
OVSS and DGSS share notable similarities and can both be implemented using either multi- single-stage strategies. In multi-stage OVSS [1. candidate regions or coarse masks are generated and then classified by approach, while multi-stage DGSS first aligns domains through adversarial alignment or style transfer and then trains the segmentation approach on the aligned features.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Sat
CLIP
CLEPP
adic
Hoaa
Haad
STATEE
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(I) Performance Comparison
(II) Prediction & Attention Visualization
(II) Architecture Differences
Figure 1: Overview of Vireo and effectiveness. (I) Performance comparison under various adverse conditions across ACDC and DELIVER datasets, showing that Vireo consistently outperforms existing methods. (II) Qualitative visualization of segmentation predictions (left) and attention maps (right) under extreme night scenes, illustrating Vireo's 's robustness and precise alignment with semantic cues. (III) Architectural comparison: (a) Traditional OVSS and (b) DGSS pipelines freeze or fine-tune VFM separately without cross-moda integration; (c) Our proposed Vireo introduces GeoText Query and Depth- VFM integration to enhance both semantic alignment and domain robustness.
In single-stage OVSS [OO I1 the segmentation head is dynamically conditioned text prompts to directly produce masks for each class. In contrast, single-stage DGSS [1] [3] incorporates domain-invariant modules within the backbone segmentation head, enabling the model to jointly learn segmentation and generalization in a unified forward pass without requiring distinct stages. The key distinction lies in their focus: OVSS requires integration of visual features with textual semantics to accurately identify unseen classes, whereas DGSS emphasizes robustness to domain shifts.
Consequently, integrating open-vocabulary recognition of novel classes with domain robustness into a unified framework presents two primary challenges: (1) Text-vision alignment modules often degrade outside the source domain, leading to significant performance drops even for previously seen classes. (2) Domain-invariant strategies may suppress fine-grained semantic cues, hindering the model's ability precisely respond detailed textual queries.
Recent single-stage DGSS studies have increasingly adopted strategies that fine-tune learnable tokens across the layers of frozen Visual Foundation Model (VFM) [4 feature representations. In OVSS, the VFM encoder is typically fully frozen, with efforts focused on designing the decoder to endow the model with open- voccaulayy recognition capabilities. This reveals a subtle complementarity between the two paradigms: DGSS emphasizes the encoder by leveraging the VFM's strong feature generalization to learn cross-domain representations, whereas OVSS freezes the encoder and emphasizes the decoder to enable open-vocabulary recognition.
15 16,
17 18 to adapt its
Moreover, in cross dross-domain scenarios, depth and geometric cues are largely insensitive to variations in illumination and texture 19. 20). They supply reliable spatial constraints, easing the distribution shift of RGB features and sharpening boundary localization. Recent studies such as DepthForge (2 have shown that injecting depth query into frozen VFM boosts domain generalization. Motivated by these findings, adopt DepthAnything V2 [22] our Depth VFM: its diverse pre-training delivers consistent depth estimation across domains, and keeping it fully frozen incurs minimal training cost while preserving real-time inference speed.
In this paper, we propose a VFM-based single-stage pipeline for OV-DGSS, termed Vireo. Specif- ically, in the encoder, both the VFM and DepthAnything modules are kept frozen. The VFM is leveraged to robustly capture cross-domain visual features, while DepthAnything extracts the scene's intrinsic geometric structure. On this basis, we introduce Geometric- Textt Query (Geo'Text Query) to fuse the extracted structural features with manually provided textual cues, progressively refining the feature maps between the frozen VFM layers. To mitigate the slow convergence caused by sparse gradients propagating through the frozen encoder, we introduce a Coarse Mask Prior Embedding (CMPE) at the beginning of the decoder to inject denser gradient signals. This design accelerates the convergence of mask supervision and further strengthens the influence of textual priors. Subsequently, we design the Domain- Open- -ae----ppaa-ulttee Vocabulary Vector Embedding Head IDOVVVER the synergistic integration of structural and textual modalities, ensuring that both cross cross-domain structural features and open-vocabulary cues learned by the GeoText Query are fully utilized in the final prediction.
strengthen
For challenge 1: We find that GeoText Query not only capture structural and semantic cues within the frozen VFM encoder but also guide the progressive refinement of its feature representations. For challenge 2: The CMPE enhances gradient pack-propagation into the encoder, while the

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Overview
VFM
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Coarse Mask Prior Embedding
CL.IP
Vireo
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Domain- Vocabulary Head
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Depth-VFR
(b) Tunable Vireo
(c) Coarse Mask Prior Embedding
Layer I
Loyo I.
Layer Las!
Geofer Qurr
Loger len
fart
Lager Lust
Depth Anarenoss
Attration Optinization
Gos Querry
Domain-Open Pornaal-ppen-oocabullryy Head
Dot Product
Addition
Trunsformc Docoddrr
Sepaoouaation
Prediiction
Concatenate
Sigmoid
Pinel Deroder
softmax
Lajer
Position cabooddin
Figure 2: Overview of the proposed Vireo framework for OV-DGSS. (a) Framework Overview.
Geometric- Text Query permeate our model: they re injected in Tunable Vireo to align domain priors, reused in CMPE to guide multi-scale feature fusion, and employed DOV-VEH queries for open-vocabulary segmentation, forming a unified end-to-end loop. (b) Tunable Vireo. GeoText Query are used to inject structural-textual priors across multiple layers. Depth-aware fusion and attention optimization are applied between intermediate frozen layers to progressively refine rep- resentations. (c) Coarse Mask Prior Embedding (CMPE). Multi-scale features from VFM are combined with Geo'Tet Query to provide dense supervision and gradient signals for downstream modules. (d) Domain-Openn-oecal Vocabulary Head (DOV-VEH), Multi-level features are processed by Pixel Decoder and Transformer Decoder to produce final predictions guided by open-vocabulary text queries.
redesigned DOV-VEH deepens the fusion of visual and textual priors. These components form unified framework that simultaneously achieves domain robustness and strong open-vocabulary generalization. Our main contributions are summarized as follows:
(1) We propose Vireo, novel VFM-based single-stage framework for OV-DGSS.
(2) We introduce GeoText Query to progressively refine frozen VFM features by injecting geometric cues from AnythAn and aligning them with textual semantics, enabling structural-semantic fusion across encoder layers.
(3) We design two complementary modules-CMPE for enhancing gradient flow and DOV-VEH for fusing visual and textual priete-togetter boosting segmentation performance under domain shifts and unseen classes.
2 Related Works
Bven-Vocabulary Semantic Segmentation. Open-Vocabulary Semantic Segmentation (OVSS) aims to segment objects based arbitrary textual descriptions, moving beyond fixed, pre-defined categories. key enabler for OVSS 23 24 25 has been vision-language models (VLMs),, particularly CLIP [16), which aligns visual and textual representations.

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Dumain-Generalinn Semantic Segmentation. Domain Generalization in Semantic Segmentation (DGSS) addresses the performance degradation when models encounter unseen target domains due to domain shift in data distributions (e.g.. lighting, weather). Data augmentation [26 27
and learning commain-invarian representations [28 [29] are two key strategies to enhance model robustness. This detailed discussion in the appendix further contextualizes our specific contributions within the broader landscape of these research areas.
3 Methodology Viiroo
We formulate the open- vocabulary DGSS problem as predicting a pixel-wise segmentation mask from an input image and set of text labels. Given an image I and set of open- open-vocabulary class names C, our goal is to produce a mask M where each pixel is classified according to one of the classes in C. As illustrated in Figure 2 our framework Vireo tackles this by leveraging frozen vision and text encoders with additional trainable prompt and depth modules for improved generalization, consists of three key modules tailored for the OV-DGSS: (1) Tunable Vireo with Geo'Text Query: Introduces Geo Query to inject and refine both geometric and textual information across layers of frozen VFM. (2) Coarse Mask Prior Embedding (CMPE): Generates coarse prior masks to guide segmentation and reinforce gradient flow from the decoder back to the frozen encoder. (3) Domain-Opent Vocabulary Vector Embedding Head (DOV-VEH): Integrates visual, geometric, and semantic features to produce final OV-DGSS predictions.
3.1 Overview
The input RGB image I E Hxxx33 is duplicated and independently fed into two frozen encoders: a VFM encoder FV and a DepthAnything encoder FD. These backbones extract a series of multi-scale features fl and fi respectively, where / denotes the encoder layer index in [1, L]. In parallel, a set of class labels C (cI,...,Ck] is transformed into natural language prompts T= (pproompt .... prompt ). which are encoded using frozen CLIP text encoder J to obtain textual embeddings ls E Rdd These features serve as shared semantic priors across the entire framework and are precomputed once at initialization. In the Tunable Vireo module, each layer receives a tuple (fl' .f,,ttt tx)), and applies the GeoText Query Pi to align and refine the visual features. This is achieved by computing cross-modal attention maps Ar = APtm(P;:3 fl' fP fx) followed by feature fusion and projection. The refined output fVV is then forwarded back to update the VFM's layer-wise activations.
We select the refined visual features ( ] from the VFM encoder at layers la 16, and 1 24 for downstream decoding. In the Coarse Mask Prior Embedding (CMPE) module, these features are first upsampled to a unified spatial resolution and passed through channel- spatial attention gating function ((-). The gated outputs are then fused to form a global coarse feature fM E _dd The fM is projected and matched with the text embeddings t4 E R to produce a coarse class probability map M E l x xK where M serves both as a weak supervision signal and as a prior to construct the query embeddings Q. The query prior is added to the GeoText Query and forwarded the final segmentation head. In the Nomain-Open--Ve Vocabulaay Vector Embedding Head (DOV-VEH) the multi-scale features M are passed through a pixel decoder D,(() to enhance spatial representation, followed by Transformer decoder D-r(-) that leverages positional embedding. The Geo'Text Query serve as learnable queries and interact with both the decoded features and the text embeddings, producing pixel-level mask embeddings Emack (I. y) E Rd and classification embeddings Eas(() E Rd. The final prediction is M(x, y, k) E xx provides the pixel-wise semantic prediction with both fine-grained detail and open- -pccbblcayy generalization capability.
8, l2 12,
3.2 Tunable Vireo with Geo'eex Query
To improve efficiency, we first precompute and share the textual prompt embeddings using a frozen CLIP text encoder Fr, Specifically, a set of class labels C {cI...., .K) } is transformed into language prompts = (prompt).... promptx: which are encoded as 4 = J= (prompt,)) where l E Rd These embeddings are reused across all GeoText Prompt layers, CMPE, and DOV-VEH modules to avoid redundant computation.

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During inference, the imput image E IR xx3 is simultaneously processed by frozen visual encoder FV and a frozen depth encoder FD (e.g.. DepthAnythhnn)). For each selected layer (1...., I., we obtain the visual feature map S = FF (T) and the depth feature map s = (T). Each layer of the Tunable Vireo module receives the tuple (f)' .fP {tx }) along with a layer-specific GeoText Prompt P E IR Nxd, where N is the number of learnable queries.
The prompt Pt first interacts with the textual embeddings via fusion block, then attends to both fM and fP through cross-attention mechanisms:
A = CrossAttm (P. f SP, {tx)).
The attention outputs are fused via weighted summation, then passed through an MLP projection layer and multiplied element- wise with P.. A residual connection adds this result to the original feature map fl yielding a refined visual representation fY. Finally, another MLP transforms fY into the input for the next VFM layer, and the updated Gex Prompt Pi+1 is passed forward.
This progressive refinement continues across all selected layers, enabling the model to inject and align geometric and semantic information at multiple scales, thereby enhancing cross-domas robustness and open-vocabulary generalization.
3.3 Coarse Mask Prior Embedding (CMPE)
We select the refined visual features { from the VFM encoder at layers 4 = 8, 12 = 12, 13 = 16, and 4 = 24, respectively. Each feature map is upsampled common spatial resolution (H x W) via bilinear interpolation, and then passed through an Adaptive Attention Gate (AAG) g()), which enhances informative channels and spatial regions. Specifically, AAG applies
x 1 convolutions followed by ReLU and Sigmoid for channel attention, and a 3 x 3 convolution followed by Sigmoid for spatial attention.
The attended features are concatenated along the channel axis and fused via a x 1 convolution to restore the embedding dimension d, yielding a fused feature representation: f = Fuse(G
We apply a residual addition between fM and the final layer output fY, to obtain the updated mask feature: fM fM + JY. This fused feature fM (x. Rd is projected to the same dimension as the text embeddings l IRd and compared via Einstein summation generate a coarse semantic probability map M(x, y, k)) = { (Ir, y), 1x) where M E IR Bx x xW This coarse mask is supervised with segmentation loss enhance gradient flow through the frozen encoder.
To generate query priors for the downstream segmentation head, we first normalize M across the spatial domain to derive attention weights:
exp(Mix,, v. k))
an (z, v) = E Eve exp(M(x', (e,,.','kk)) k))
(2)
Then, compute the class class-specific aggregated feature by spatially weighting fM: (x( M (M,
pelass
Each felass is projected into the embedding space eqlass Rd, and combined with set of learnable
queries vectors {q, ) to produce the query priors:
K
prior
Softmax((g,;
ok class)) ek
class
The final query priors (eprior No are added to the corresponding GeoText Query and forwarded into 1ーー
the Domain- Open- apen--oocabllryy Vector Embedding Head (DOV-VEH).
3.4 Domain-Open-
Domain-Open-Vocaaullryy Vocabulary Vector Embedding Head (DOV-VEH)
The DOV-VEH module receives the refined multi-scale features (fA ) from the VFM encoder,
where 4 8, l2 12, la 16, and 4 24, along with the updated Geo Query (Pt). These
5

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Table Performance comparison between our Vireo and existing OVSS and DGSS methods under Citys. - ACDC + BDD. + Map., and GTAS. Citys. BDD. Map. generalization settings. Top three results are highlighted best second and third respectively. (%)
Mediod
Proc Yoar
Traincd Citysagee
Thirred on CITAS
Nigls-ACDC
Fog-ACDC
Rai-ACDC
Saow ACCDC
BED 1016.
Mapullary
GTAS
Cliyscages
BDD
Magillary
OVSS Method
PC-CLIP 50
NeadPS2022
80.8
64.4
63.2
61.5
55,92
66.12
47.32
53.54
51.41
55.60
LBSex B1
CVPR2024
27:7
56.5
5188
50.1
43.91
63.40
42.61
44.80
40.59
56.28
CNT Sez L0
CVPR2024
58.3
45.6
49.0
68.25
54.74
45. 15.18
43.52
44.25
50.85
SED
CVPR2024
38.7
69.0
56.4
00.2
$3.30
64.32
48.93
47.45
48.16
57.38
DGSS Method
Kosher bazed:
IDN [20
BCCV2018
21.2
50.4
49.5
57.06
RobustNet
CVP922031
24.3
56.0
49.8
50.75
58.64
45.00
35.56
35.20
WildNet
53
CVPR2002
12:7
41.2
34.2
28.4
50,94
58.79
47.01
44.62
39,42
40.09
Thanffomer bazel
[ICrormer
K0
CVPR2023
52.7
69.9
72.0
69.6
53.40
66.90
$1.30
CMiammer
15
AAAD302
33.7
77.8
67.6
64.3
59.27
71.10
58.11
55.31
49.91
60.09
VFM Iusedh
REIN
X3
CVPR2024
55.9
79.5
72.5
70.6
63.54
74.03
62.41
66.40
60.89
66.10
PADA
3M
NeutPS20024
57.4
80.2
75.0
73.5
65.13
75.86
63.78
68.25
61,94
68.09
OV-DGSS Method
Vireo (Ours)
60.6
82.3
76.3
76.2
66.73
75.99
67.86
70.69
62.91
69.63
features first processed by pixel decoder Dp(.). which leverages multi-scale cross-attention extract rich spatial context: feix D, (J%) The fused feature fri E IR H xd then compressed via 1 X 1 convolution and enriched with sinusoidal positional encoding to preserve spatial structure.
The enhanced features fed into Transformer Decoder Dr-()), where the GeoText Query act as learnable queries. Through stacked layers of self-ateention and cross-attention with f the model captures fine-grained visual-semantic alignment, yielding set of high-resolution mask features Emask (z. y) E R2d at each spatial position.
Simultaneously, the Geo'eet Query (P) are passed through a two-layer MIP and then interact with the text embeddings } (precomputed from the CLIP text encoder) to produce classification-leeel representations (k) E IRd. The final segmentation prediction M E # is generated via an Einstein summation over the embeddings:
D M(I, y, k. = Σ Eman En v. Ek d),
(5)
where D is the feature embedding dimension. This design enables DOV-VEH to generate pixel-level segmentation masks that are both spatially accurate and semantically aligned with open-vocabulary textual queries.
4
Experiments
4.1
Datasets Evaluation Protocols
We evaluate Vireo on six real-world datasets (Cityscapes [38] BDD100K (3)] Mapillary 440]
ACDC 41
ADE150 42), and ADE847 42 and two synthetic datasets (GTA5 4 and DELIVER
Cityscapes (City.) is an iitonomous-driving dataset with 2,975 training images and 500 validation images, each at a resolution of 2048 x 1024. BDD100K (BDD.) and Mapillary (Map.) provide 1,000 and 2,000 validation images, respectively, resolutions of 1 1280 x 720 and 1920 x 1080. ACDC offers 406 validation images captured under extreme conditions (night, snow, fog, and rain), each at 1920 1080. GTAS is synthetic dataset that contains 24,966 labeled images obtained from a video game. DELIVER a multimodal synthctic dataset comprising 3,983 training images, 2,005 validation images, and 1,897 test images across five weather conditions (cloudy, foggy. night, rainy, and sunny): : every image is 1042 x 1042 and there are 25 classes. ADE150 and ADES47 refer to subsets of the ADE20K dataset 42), each containing 2,000 validation images of variable resolution sourced from diverse scenes such as SUN and Places, covering 150 and 847 semantic categories, respectively.
44
Following the existing DGSS evaluation protocol, we train on one dataset as the source domain and validate on multiple unseen target domains. The three standard evaluation setups are: (1) Cityscapes - ACDC; (2) GTA5 - Cityscapes, BDD100K, Mapillary; (3) Cityscapes - BDD100K, Mapillary, GTA5. To assess proposed OV-DGSS approaches and compare its open-vocabulary capability

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Nahh
Rain
Emagee
NolbustNet
HGFeemer
REIN
Ours
Ground Truth
Figure 3: Key segmentation examples of existing DGSS methods and Vireo under the Citys. ACDC unseen target domains under Night, Snow, Rain, and Fog conditions.
Table Performance comparison between our Vireo and existing OVSS methods under Citys, DELIVER ADE150 ADE847 generalization setting. Top three results are highlighted as best
second and third respectively. (%)
Method
Proc: Year Sun-DELIVER Nigh-DELIVER Cloud-DELIVER
Rain-DELIVER
Fog-DELIVER
ADEI50
ADE347
Train on Cityscapes
OVSS Method:
NeurIP52023
EBSeg 31
CVPR2024
26.41
15.50
22.62
20.35
22.00
12:75
3.75
CAT-Seg 10
PN224024
28.21
20.56
26.22
26.53
24,80
20.19
6,95
SED
3
CVPR2024
27.14
22.79
24.40
25.18
25.25
18.86
5.45
OV-DGSV Method:
Vireo (Ours)
35.73
27.51
32.34
31,80
32.72
21.37
7.31
Train GTA
OVSS Method:
FC-CLIP
NeurIPS2023
22.24
18.58
18.50
16.59
19.12
15.47
5.73
EBSeg 31
CVPR2021
32.32
20.05
26.19
26.19
28.69
11.87
1.19
CAT-Sog
CVPR2024
28.59
23.49
27.31
27.94
27.66
20.45
7.18
SED
CVPR2024
OV-DGSS Menhod:
Vireo (Ours)
38.49
29.89
33.89
33.46
35.80
21.23
7.68
against OVSS approaches, we additionally introduce two more configurations: (4) Cityscapes > DELIVER, ADEIS0, ADE847; (5) GTA5 DELIVER, ADE150, ADE847. The evaluation metric mean Intersection over Union (mloU).
4.2 Deployment Details & Parameter Settings
Our implementation is built upon the MMSegmentation optimizer with an initial learning rate of le-4, weight decay 0.05, epsilon set to 1e-8, and beta parameters of (0.9, 0.999). The total number of training iterations is 40,000, matching REIN, and adopt a polynomial learning-rate decay schedule that reduces the learning rate to zero over 40,000 iterations with decay power of 0.9 and epoch-based warmup. Data augmentation comprises multi-scale resizing, random cropping (with fixed crop size and category-ratio constraint), random horizontal flipping, and photometric distortion. All experiments are conducted on NVIDIA RTX A6000 GPU with a batch size of 8, taking approximately 14 hours to train and peaking at around 45 GB of GPU memory usage.
45
codebase. We employ the AdamW
4.3 Performance Comparison
Domain Generalization Ability: Table summarizes the evaluation results of various state-of- the-art open-vocabulary semantic segmentation (OVSS) and Nomain-generalizer semantic segmen- tation (DGSS) methods under two cross-domain settings (Cityscapes > ACDC, BDD100K, Map- illary, GTA5 and GTA5 - Cityscapes, BDD100k, Mapillary). The results demonstrate that our approach achieves outstanding performance across target datasets, significantly outperforming other OVSS/DGSS methods. Furthermore, the visualizations in Figure shows that Vireo delivers

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Table 3: Comparison of Seen and Unseen Category mloU Across Weather Scenarios and Methods,
Chead
NgM
Sun
Den
smn CAT PC CUP
Duo
Smh
PCOP
Das
STD
Cordey PCCLUPP
Den
sm
CAT
CUP
SED
CAT
FC CCP magg
Siea
36.35
19-96
19.55
15
11:63
N0W
1458
31.0
15:15
15
9
31:95
1166
1531
3650
00
Uranee
12
5.70
1.6
486
290
12:15
8.9
3.51
633
4:88
945
121
330
0.0
3.08
1333
581
168
5:57
11:45
815
6.1
473
611
Skaa
33.00
3440
2622
11:00
22:22
12000
23.22
24.0
17.29
2110
23.90
2570
2026
1453
12.00
2346
23.3
2035
16.00
3133
3040
2134
2631
1633
21:4
Cloud
Rain
Night
Sun
Image
FC-CLIP
E.BSeg
CAT-Seg
SED
Ground Truth
Figure 4: Key segmentation examples of existing OV-DGSS methods comparison unseen classes and cross domain on Cityscapes + DELIVER.
satisfactory predictions in both extreme weather conditions and scenes with dense pedestrian and vehicular traffic.
Open- Veca-aocayylary Capability: Table 2 presents a comparison between Vireo and other open- vocabulary semantic segmentation (OVSS) methods under the Cityscapes DELIVER (sun, rain, night, cloud, fog), ADE150, and ADE847 configurations. The results indicate that conventional OVSS approaches suffer sharp performance drop in extreme scenarios (c.g., night), whereas our model--ehhaneed by depth-based geometric features maintains robust performance, outperforming the strongest OVSS baseline by least 5%. Furthermore, shown in Figure 4 the coexistence of new open classes and extreme weather conditions in the DELIVER dataset leads OVSS approaches exhibit significant false positives and false negatives.
Table compares Vireo trained on Cityscapes with other open- open-vocabulary semantic-segmennttii methods on the DELIVER dataset across five weather conditions for both Seen and Unseen cate- gories. Vireo achieves the highest mloU for both groups: it outperforms the second-best method by roughly 7-10 percentage points on Seen classes and by 2-3 points on Unseen ones. Although all methods register lower mloU on Unseen calegories-underseee underscoring the difficulty of opea-vocyyulary segmentation--Vee Vireo substantially reduces this gap. confirming that its depth-geometry guidance and cross-domain alignment improve recognition of new classes. Overall, Vireo remains consis- tently superior across weather scenarios and category types, demonstrating stronger out-of-domain generalization.
4.4 Ablation Study
Robust Performance Gains. Table we compare parameter overhead and mloU of lightweight tuning methods on EVA02-L arge and DINOv2-Large under the GTAS-xCitsscape lary transfer. With only Rs 3.8 M trainable parameters, Vireo leads both: 66. 0% on EVA02 (+1.1% over FADA) and 67.7% on DINOv2 (+1.6%), Other schemes (REIN, LoRA, VPT, AdaptFormer)
BDD Mapil-

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Table 4: Performance comparison of the proposed Vireo against other DGSS methods under the
GTA5 - Citys.+BDD. Map.. setting.
EVA02 (Large)
46
47
DINOv2 (Large)
54
Trainable Params
Trainable Params
Fine-tune Method
mloU
Avg.
Fine-tune Method
mloU
Avg.
Citys.
BDD.
Map.
Citys.
BDD.
Map.
Full
304,24M
62.1
56.2
64.6
60.9
Full
304.20M
63.7
57.4
64.2
61.7
AdvStyle
304.24M
63.1
56.4
64.0
61.2
+AdvStyle
304.20M
60.8
58.0
62.5
60.4
PASTA
304.24M
61.8
57.1
63.6
60.8
+PASTA 49
304.20M
62.5
57.2
64.7
61.5
GTR--LTR(50
304.24M
57.4
63.2
60.1
+GTR-LTR
304.20M
62.7
57.4
61.6
Freeze
0.00M
56.5
53.6
58.6
56.2
Freeze
0.00M
63.3
56.1
63.9
61.1
+AdyStyle 48
0.00M
51.4
51.6
56.5
53.2
+AdvStylc 48
0.00M
61.5
55.1
63.9
60.1
+PASTA[49
0.00M
57.8
52.3
58.5
56.2
+PASTA 49
0.00M
62.1
57.2
64.5
61.3
+GTR-LTR 50
0.00M
52.5
52.8
57.1
54.1
+GTR-LTR 50
0.00M
60.2
57.7
62.2
60.0
+LoRA[51
1.18M
55.5
52.7
58.3
55.5
+LoRA
0.79M
65.2
58.3
64.6
62.7
+AdaptFormer
3.17M
63.7
59.9
64.2
62.6
+AdaptFormeri
3.17M
64.9
59.0
64.2
62.7
+VPT [XX]
3.69M
62.2
57.7
62.5
60.8
+VPT
3.69M
65.2
59.4
65.5
63.3
+REIN
2.99M
65.3
60.5
64.9
63.6
+REIN(36
2.99M
66.4
60.4
66.1
64.3
+FADA 37
11.65M
66,7
61.9
66.1
64.9
+FADA R7
11.65M
68 2
62.0
68.1
66.1
+ +Vireo (Ours)
3.78M
68.5
62.1
67.4
66.0
+Vireo (Ours)
3.78M
70.7
62.9
69.6
67.7
Imsge/GT
wil GTQ
wil CMPE
Affinity
Image/GT
GTQ
CMPE
Affinity
Figure 5: Visualizations of attention maps and affinity maps under differnet scenes, where CMPE denotes the Coarse Mask Prior Embedding, GTQ represents the GeoText Query, respectively.
improve a frozen backbone but fall short of Vireo's accuracy-efficiency balance. Versus full fine- tuning, Vireo slashes training cost and further improves mloU, showing its depth-geometry queries generalize across VFMs.
Table repoors the parameter overhead and mloU of Virco and several lightweight fine-tuning schemes four backbones (CLIP-L, SAM-H, EVA02-L, and DINOv2-L). Compared with the prompt-based REIN baseline, Vireo adds only about 0.79 M extra parameters yet achieves the highest average mloU on every backbone: the gain is most pronounced on the parameter-on parameter-constrranee constrained CLIP-L, and Vireo still surpasses heavier adapters such as FADA by 1-2 mloU on the larger EVA02-L and DINOv2- models, underscoring its parameter efficiency and scalability across backbones.
In T using Depth Anything V2 alone yields approximate 0.6% mloU gain across all six scenarios, and adding depth augmentation with attention optimization (DA AO) delivers further s 1% improvement. The DOV-VEH and CMPE modules each add 0.5%-0.8% validating mask vectors and dense gradient embedding. GeoText Query its provides a substantial z 4.4% boost, underscoring the complementary benefits of fusing semantic and geometric cues.
Figure 7: Comparison of Train Loss for Baseline and CMPE Models.
Pronounced Attention Focus. Figure 5 further confirm that GeoText
Query steer the model toward geometry regions, while CMPE strengthens gradients

---

Ennligaations
Snow
Night
Fog
Rain
BDDD.
Map
REIN 26
70.6
55.9
79.5
72.5
63.3
74.0
concal
70.8
56.1
79.4
72.8
63.6
74.2
Prompt Depth Anything
20.4
55.5
79.7
72.0
63.8
73.9
Depth Anything
71.5
56.7
80.5
73.3
74.5
DA +. AO
72.2
57.4
80.9
74.2
65.1
75.0
DOV-VEH
CMPE
71.6
56.9
80.2
7344
64.1
74,5
GeoTed Query
74,0
58.4
81.1
74.8
65.3
75.3
Table Ablation studies on component con- figurations of the proposed Vireo under the Citys. ACDC with Snow, Night, Fog, Rain BDD. + Map. generalization setting. Top three results highlighted as best second and third (%).
Build.
Person
Light
Sky
Vege.
Car
Terrain
Sidd.
Pole
Figure 6: t-SNE embeddings of the feature space. Left: original source-domain dataset. Right: our Vireo after adaptation on Cityseapes - ACDC + BDD 100k + Mapillary. Each point coloured by its semantic class.
CLIP (VIT-L)
55
SAM (Huge) 56
Trainable
mIoU
Trainable
mloU
Fine-tune
Avg.
Fine-tune
Avg.
Params
Citys
BDD
Map
Params
Citys
BDD
Map
Full
304.15M
51.3
47.6
54.3
51.1
Full
632. 18M
57.6
51.7
61.5
56.9
Freeze
0.00M
53.7
48.7
55.0
52.4
Freeze
0.00M
57.0
47.1
58.4
54.2
REIN
2.99M
57.1
54.7
60.5
57.4
REIN
4.51M
59.6
52.0
62.1
57.9
FADA
11,65M
58.7
55.8
62.1
58.9
FADA
16.59M
61.0
53.2
63.4
60.0
Vireo (Ours)
3.78M
60.5
57.5
64.1
60.7
Vireo (Ours)
5.30M
64.5
59.0
66.0
63.2
EV002 (Large) 44
47
DINOv2 (Large)
54
Trainahle
mIoU
Trainable
mloU
Fine-tur tune
Avg.
Fine-tune
Avg.
Params*
Citys
BDD
Map
Params*
Citys
BDD
Map
Full
304.24M
62.1
56.2
64.6
60.9
Full
304.20M
63.7
57.4
64.2
61.7
Freeze
0.00M
56.5
53.6
58.6
56.2
Freeze
0.00M
63.3
56.1
63.9
REIN
2.99M
65.3
60.5
64.9
63.6
REIN
2.99M
66.4
60.4
66.1
64.3
FADA
11.65M
66.7
61.9
66.1
64.9
FADA
11.65M
68.2
62.0
68.1
66.1
Vireo (Ours)
3.78M
68.5
62.1
67.4
66.0
Vireo (Ours)
3.78M
70.7
62.9
69.6
67.7
Table 6:
Performance comparison of Vireo across multiple VFMs under the GTAS
Citys. + BDD. Map. setting. 'Trainable parameters in backbone. Top three results are best second, third (%)
foreground masks; compared with baselines, Vireo's focus is tighter on scene structure and semantic boundaries, explaining its consistent advantage in cross-domain and open-vocabulary settings.
t-SNE Visualization of DGSS Features. The feature distributions of the original dataset and our method are visualized in Figure revealing the superiority of our learned features in forming well-separated semantic clusters. This demonstrates the effectiveness of our domain-generalized visual-textual alignment structuring the open-vocabulary semantic space.
5 Conclusion
This study introduces Vireo, the first single-stage framework that unifies open- open-vocabulary recognition and domain-gent comaiingeneralised semantic segmentation. By integrating frozen visual foundation models, depth-aware geometry and three core modules-Gan GeoText Query, Coarse Mask Prior Embedding and the DOV Vector Embedding Head-it converges faster, concentrates attention on scene structure and outperforms state-of -iite-of-tie of-the-art methods across multiple benchmarks, demonstrating the power of combining textual cues with geometric priors for robust pixel-level perception.
Limitations Vireo assumes reliable RGB camera, but in rare cases (e.g., occlusion, glare, hard- ware failure), the stream can be lost, impairing perception. Future work will explore multi-source setups-autone automatically switching to lidar, radar, event-based cameras when RGB fails to keep segmentation robust in all conditions.
10

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Acknowledgements
This work was supported in part by the Natural Science Foundation of Xiamen, China, under Grants 35022202373036 and 35022202371019 1019; in part by the National Natural Science Foundation of China under Grant 42371457; in part by the Natural Science Foundation of Fujian Province, China, under Grants 2025J01345 and 2023J01803, 803; by the 2025 University of Glasgow Early Career Reward for Excellence on "Developing Vision-Language Models Enhanced by Geospatial Intelligence" and the 2025 University of Glasgow Early Career Mobility Scheme (ECMS) Award; and by the Program of China Scholarship Council under Grant No. 202506380088.
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Reviews:
[{"review_id": "9cpWim3Mob", "reviewer": "Reviewer_sPRg", "summary": "This paper proposes Vireo, a single-stage framework for Open-Vocabulary Domain-Generalized Semantic Segmentation. It leverages frozen Visual Foundation Models and integrates scene geometry via DepthAnything. Three key modules are introduced: (1) GeoText Prompts for aligning geometry and textual semantics; (2) Coarse Mask Prior Embedding for faster convergence and improved gradient flow; and (3) Domain-Open-Vocabulary Vector Embedding Head for fusing semantic and geometric cues. Extensive experiments show Vireo significantly outperforms previous OVSS and DGSS methods on several challenging benchmarks.", "questions": "(1) Could you provide ablation results showing Vireo’s performance when using fewer GeoText prompt layers (e.g., only the deepest two layers or just one layer)? Additionally, how does reducing the number of prompt layers affect compute time, memory usage, and segmentation accuracy?\n(2) Can you provide an analysis of how DepthAnything behaves across different environments—especially in low-light or weather-degraded scenarios—and discuss any methods you considered (or could integrate) for estimating depth confidence or reducing the impact of noisy depth inputs on segmentation quality?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. The methodology is well-described, with clear diagrams and ablations highlighting the contribution of each module.\n\n2. The paper is among the first to unify open-vocabulary segmentation with domain generalization in a single-stage framework, providing a sound insight to bridge the OVSS and DGSS.\n\nWeaknesses:\n\n1. Lack of discussion of limitations. One key limitation of Vireo lies in its reliance on depth information from the DepthAnything module. While depth cues can enhance structure-awareness, they may become unreliable under challenging conditions like nighttime, heavy rain, or fog. If depth estimation degrades in such domains, it risks introducing noisy geometric priors that misguide the GeoText Prompt mechanism and ultimately degrade segmentation performance. The paper currently lacks a quantitative evaluation of depth fidelity under these adverse conditions and does not propose mitigation strategies when depth estimates are poor.\n\nQuestion:\n\n(1) Could you provide ablation results showing Vireo’s performance when using fewer GeoText prompt layers (e.g., only the deepest two layers or just one layer)? Additionally, how does reducing the number of prompt layers affect compute time, memory usage, and segmentation accuracy?\n(2) Can you provide an analysis of how DepthAnything behaves across different environments—especially in low-light or weather-degraded scenarios—and discuss any methods you considered (or could integrate) for estimating depth confidence or reducing the impact of noisy depth inputs on segmentation quality?", "was_revised": true, "final_justification": "Thanks for the authors' efforts in providing the rebuttal, most of my concerns have been addressed, and I'd keep my positive rating and recommend acceptance.", "rebuttal": "## Response to Reviewer sPRg\n\n**Question 1**: Could you provide ablation results showing Vireo’s performance when using fewer GeoText prompt layers (e.g., only the deepest two layers or just one layer)? Additionally, how does reducing the number of prompt layers affect compute time, memory usage, and segmentation accuracy? \n\nAnswer 1: Reducing the number of GeoText prompt layers leads to a significant drop in segmentation accuracy. From our ablation in Table 1, using only the single deepest layer yields mIoU ≈65.6 – far below 73.9 mIoU with all 24 layers. We attribute this to the rich multi-level spatial context provided by the GeoText layers. Each GeoText layer captures features at different scales; with fewer layers, the model cannot fully express complex geographic features and boundaries, resulting in substantially lower accuracy. This is not merely a minor accuracy loss, but a fundamental loss of the model’s ability to capture fine details in geographic shapes. \n\nNote that adding more Vireo (GeoText) layers does not increase the model size or memory footprint, because all Vireo layers share parameters. As shown in Table 1, the total parameter count remains ~3.78M regardless of layer count. We also observe minimal change in inference speed when varying the number of layers. For example, using 4 layers vs. 24 layers yields nearly the same per-frame inference time on an RTX 3090 (about 998ms vs. 1079ms). Given these trade-offs, reducing the GeoText layers provides little benefit in speed or memory, but significantly harms accuracy. Thus, we conclude that the cost of removing layers outweighs any potential gains.\n\nTabel.1 A comparison of how different numbers of GeoText layers affect model accuracy, inference speed on 3090, and parameter increase.\n|Vireo Layers Number|Parameters|Inference Time(ms)|mIoU|\n|:-:|:-:|:-:|:-:|\n|Deepest 1 Layer|3.78M|979.02|65.59|\n|Deepest 2 Layer|3.78M|987.86|67.09|\n|Deepest 3 Layer|3.78M|992.85|68.93|\n|Deepest 4 Layer|3.78M|997.92|69.69|\n|All 24 Layer|3.78M|1078.86|73.85|\n\n\n\n**Question 2**: Can you provide an analysis of how DepthAnything behaves across different environments—especially in low-light or weather-degraded scenarios—and discuss any methods you considered (or could integrate) for estimating depth confidence or reducing the impact of noisy depth inputs on segmentation quality?\n\nAnswer 2: Thank you very much for your attention to this matter. First, as shown by our ablations (Table 4 in the paper), incorporating DepthAnything’s depth cues markedly improves segmentation performance under extreme weather conditions. DepthAnything has demonstrated strong robustness in low-light and weather-degraded environments. For instance, under nighttime or foggy conditions, using depth cues from DepthAnything substantially improves mIoU compared to using only RGB features. This indicates that even in harsh scenarios, the depth module provides useful structural information that benefits segmentation. \n\nSecond, to mitigate noisy depth inputs, we explicitly designed Vireo to handle varying depth quality. In our architecture, a learnable weighting mechanism adaptively adjusts the contribution of depth features during feature fusion. In environments where depth estimates are less reliable (e.g., night or heavy rain), this mechanism automatically down-weights the depth modality. By reducing the influence of noisy depth cues on the final prediction, the model maintains robust performance. In summary, even when depth is imperfect, Vireo can rely more on other cues (like RGB and text) as needed, thereby minimizing the impact of noisy depth input on segmentation quality.\n\nTable.4 in main text: Ablation studies on component configurations of the proposed **Vireo** under the *Citys. → ACDC with Snow, Night, Fog, Rain + BDD. + Map.* generalization setting.\n\n| Configurations|Snow|Night|Fog|Rain|BDD.|Map.|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n| REIN|70.6|55.9|79.5|72.5|63.5|74.0|\n| + concat depth feature |70.8|56.1|79.4|72.8|63.6|74.2|\n| + Depth Anything V2|71.5|56.7|80.5|73.3|64.4|74.5|\n| + DA + AO|72.2| 57.4|80.9|74.2|65.1| 75.0|\n| + DOV-VEH|70.9| 56.2|79.8|72.8|63.7| 74.2|\n| + CMPE|71.6|56.9|80.2|73.4|64.1|74.6|\n| + GeoText Prompts|74.0|58.4|81.1|74.8|65.3|75.3|\n| **Vireo**|76.2|60.6|82.3|76.3|66.7|76.0|\n\n\n\n**Weaknesses 1**: Lack of discussion of limitations. One key limitation of Vireo lies in its reliance on depth information from the DepthAnything module. While depth cues can enhance structure-awareness, they may become unreliable under challenging conditions like nighttime, heavy rain, or fog. If depth estimation degrades in such domains, it risks introducing noisy geometric priors that misguide the GeoText Prompt mechanism and ultimately degrade segmentation performance. The paper currently lacks a quantitative evaluation of depth fidelity under these adverse conditions and does not propose mitigation strategies when depth estimates are poor.\n\nAnswer 3: Thank you for your valuable feedback. We take your concern about Vireo’s reliance on the DepthAnything module for depth information and the potential issues it may cause in extreme environments seriously. During the model design process, we indeed considered this challenge and took appropriate measures to address it.\n\nFirstly, during training, we introduced a diverse set of environmental data, including scenarios with low light, fog, and heavy rain, to enhance the model's robustness. In the model architecture design, we implemented an adaptive depth fusion mechanism, which combines learnable weights to adjust the importance of depth information in feature fusion based on environmental changes. This strategy allows the model to automatically reduce the weight of depth information in low-light or extreme weather conditions, thereby minimizing noise interference on segmentation results.\n\nSecondly, we conducted preliminary experiments to compare the errors in depth maps generated by DepthAnything under extreme conditions (such as nighttime, heavy rain, and fog). The results (see Table.1) indicate that the depth maps generated by DepthAnything do not experience significant distortion in these scenarios. It is important to note that even in low-light environments like nighttime, pure RGB-based segmentation methods face the issue of missing depth information, which impacts segmentation accuracy. In contrast, by incorporating DepthAnything, we observed a significant improvement in segmentation accuracy in these environments, demonstrating that depth information remains crucial for performance in complex scenarios.\n\nTabel.1 DepthAnything predicts the similarity of depth maps on the cityscapes dataset under extreme environments and sunny conditions.\n|Metric|Foggy|Rain|Night|\n|:-:|:-:|:-:|:-:|\n|SSIM|0.990552|0.978066|0.975610|"}, {"review_id": "7Bay6rrxjR", "reviewer": "Reviewer_Mrmp", "summary": "The paper introduces Vireo, a novel single-stage framework for Open-Vocabulary Domain-Generalized Semantic Segmentation (OV-DGSS). This framework is designed to generate pixel-level masks for unseen categories while maintaining robustness across various unseen domains, which is crucial for real-world applications like autonomous driving in challenging conditions.\nSpecifically, the key contributions of the proposed method lie in three aspects: GeoText Prompts for aligning geometric and language features, Coarse Mask Prior Embedding (CMPE) to enhance gradient flow, and the Domain-Open-Vocabulary Vector Embedding Head (DOV-VEH) for robust prediction. \nExperiments show that Vireo achieves state-of-the-art performance, significantly outperforming existing methods in both domain generalization and open-vocabulary recognition across 6 real-world datasets, including Cityscapes, BDD100K, Mapillary, ACDC, ADE150, ADE847, and two synthetic datasets GTA5 and DELIVER.", "questions": "Please see my comments in the weakness section.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. The paper proposes a unified framework for open-vocabulary and domain-generalized semantic segmentation. \n2. The proposed method aligns depth with language. \n3. Good experimental results across multiple datasets. \n\n\nWeaknesses:\n1. My major concern lies in the clarity issues within the paper. First, it should start with an overall problem formulation, including, input, output and parameters to be learned, and then explain key components of the pipeline. Second, in Figure 2, it is difficult to understand the flowchart and relate the mathematical symbols defined in Sec.3 with different parts of the figure. Third, the training objective is missing. \n2. Is Table 4 listing the contribution of each module or accumulated result? More explanation is needed.", "was_revised": true, "final_justification": "I support borderline accept this paper, conditioned on the promised paper rewriting and presenting clear ablation results.", "rebuttal": "## Response to Reviewer Mrmp\n\n**Question 1**: My major concern lies in the clarity issues within the paper. First, it should start with an overall problem formulation, including, input, output and parameters to be learned, and then explain key components of the pipeline. Second, in Figure 2, it is difficult to understand the flowchart and relate the mathematical symbols defined in Sec.3 with different parts of the figure. Third, the training objective is missing.\n\nAnswer 1: We acknowledge the clarity issues and will restructure Section 3 (Methodology) in the final version. Specifically, we will first begin Section 3 with a clear formulation of the open-vocabulary Domain Generalized Semantic Segmentation (DGSS) task – including the model inputs (an image and a set of category labels), the output (pixel-level mask), and the parameters to be learned. Next, we will present a step-by-step overview of our pipeline, introducing each module at a high level before diving into details. This reorganization will follow the logical flow of data through the model. In addition, we will revise Figure 2 by adding clear annotations for each variable and ensuring that each mathematical symbol in the text is labeled on the diagram, in order to help readers correlate the equations in Section 3 with the visual blocks in Figure 2. Lastly, we will explicitly describe the training objective and loss functions used, which were omitted in the current draft. \n\nTo demonstrate the improved clarity, here is a concise reformulation of our approach:\n\n*We formulate the open-vocabulary DGSS problem as predicting a pixel-wise segmentation mask from an input image and a set of text labels. Given an image $I$ and a set of open-vocabulary class names $C$, our goal is to produce a mask $M$ where each pixel is classified according to one of the classes in $C$. Our framework Vireo tackles this by leveraging frozen vision and text encoders with additional trainable prompt and depth modules for improved generalization. Vireo consists of three main components: (1) Tunable Vireo with GeoText Queries, (2) Coarse Mask Prior Embedding (CMPE), and (3) Domain-Open-Vocabulary Vector-Embedding Head (DOV-VEH).*\n\n*For visual encoding, we pass the image through two frozen encoders: a vision encoder $F_V$ (e.g., DINO) and a depth estimator $F_D$ (e.g., DepthAnything), extracting multi-scale visual features. In parallel, each label in $C$ is converted into a text prompt and encoded by a frozen CLIP text encoder $F_T$, yielding a set of textual embeddings (which we call GeoText Queries). These GeoText queries are injected at multiple stages of the vision encoder via cross-attention layers. At each chosen encoder layer, the GeoText Query interacts with the intermediate visual features, refining those features in a cross-modal manner. The refined features are then fed into subsequent layers, progressively aligning visual content with semantic cues.*\n\n*From selected encoder layers, the features are routed into the CMPE module, which fuses them into a coarse mask prior $f_M$. This provides a global spatial prior and additional supervision (even with the backbone frozen, CMPE’s output guides early layers). Finally, the DOV-VEH module combines the refined visual features and text queries in a transformer-based decoder to produce the high-resolution output mask $M$. DOV-VEH operates in a shared vision–language embedding space and uses dual attention to ensure the masks align with the textual queries. All parts of Vireo are trained end-to-end, supervised by standard segmentation loss on $M$ as well as intermediate losses on the coarse mask $f_M$.*\n\nThis reworked methodology will make the novelty and design of each module clear, and we will ensure the training objective is explicitly stated. \n\n\n\n**Question 2**: Is Table.4 listing the contribution of each module or accumulated result? More explanation is needed.\n\nAnswer 2: Thank you for your question. Table 4 in the paper is an ablation study that illustrates the incremental contribution of each module. Each row of Table 4 adds a specific component to a baseline model, allowing us to isolate its impact. In other words, the table is not showing fully separate configurations, but rather a cumulative build-up. First, the first row (“REIN”) is the base model; each subsequent row adds one module or feature (e.g., + concat depth feature, + Depth Anything V2, + DOV-VEH, etc.) on top of the previous ones; and the final row (“Vireo”) includes all proposed modules enabled, achieving the highest performance. \n\nFrom this table, one can see how each component improves the model under various conditions (Cityscapes → ACDC with Snow, Night, Fog, Rain; plus generalization to BDD and Mapillary domains). For example, adding depth features (+Depth) already yields a small improvement under most conditions, and adding our full GeoText prompting and other modules leads to the best results. We will clarify this explanation in the paper to avoid confusion. \n\nTable.4 in main text: Ablation studies on component configurations of the proposed **Vireo** under the *Citys. → ACDC with Snow, Night, Fog, Rain + BDD. + Map.* generalization setting.\n| Configurations|Snow|Night|Fog|Rain|BDD.|Map.|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n| REIN|70.6|55.9|79.5|72.5|63.5|74.0|\n| + concat depth feature |70.8|56.1|79.4|72.8|63.6|74.2|\n| + Depth Anything V2|71.5|56.7|80.5|73.3|64.4|74.5|\n| + DA + AO|72.2| 57.4|80.9|74.2|65.1| 75.0|\n| + DOV-VEH|70.9| 56.2|79.8|72.8|63.7| 74.2|\n| + CMPE|71.6|56.9|80.2|73.4|64.1|74.6|\n| + GeoText Prompts|74.0|58.4|81.1|74.8|65.3|75.3|\n| **Vireo**|76.2|60.6|82.3|76.3|66.7|76.0|"}, {"review_id": "PASSQA6z4B", "reviewer": "Reviewer_3YaR", "summary": "This paper introduces Open-Vocabulary Domain-Generalized Semantic Segmentation (OV-DGSS), a challenging task that combines the objectives of OVSS and DGSS. The goal is to create a model that can segment novel object categories while also being robust to shifts in the data domain. To address this, the authors propose Vire. Vireo is built upon a frozen Visual Foundation Model, which is DINOv2 and incorporates a Depth VFM, which is DepthAnything, to extract domain-invariant geometric features. The authors design three module to integrate the depth into the framework to provide domain-general guidance.", "questions": "Please elaborate on the claims between L69-74 regarding the proposed modules: GeoText Prompt, CMPE, and DOV-VEH. Please address each step from a high-level perspective. I strongly recommend revising the whole Sec.3 and Fig.2 to match the quality of the rest of the paper. In my view, the current level of writing clarity is not sufficient to be published on NeurIPS.", "limitations": "Yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n* It's a valid task, and the experiment shows improved results over regular OVSS methods. \n* The idea of using high-quality depth to guide the segmentation under various domains makes sense. DepthAnything demonstrates a strong ability in doing that. \n* The experiments and ablation studies are thorough. The authors also open-sourced the code. \n\nWeaknesses\n* Poor writing in Sec.3: Methodology. The introduction addresses the function of each module. These are not elaborated in the method section. The organization is chaotic. It is indeed rich in details, but the paragraphs are more towards a technical manual. It is great in the Supp to help understand the code, but not beneficial in understanding the novelty, design, and contribution of each proposed module. \n* The acronyms and method naming block the comprehension as well. GeoText Prompt is actually a learnable query instead of a prompt commonly referred to in LLMs.", "was_revised": true, "final_justification": "The rebuttal clarifies most of my questions regarding the manuscript. However, whether the final version can successfully deliver these information through promising writing is unknown. I keep my score and will not be against accepting it.", "rebuttal": "## Response to Reviewer 3YaR\n\n**Question 1**: Please elaborate on the claims between L69-74 regarding the proposed modules: GeoText Prompt, CMPE, and DOV-VEH. Please address each step from a high-level perspective. I strongly recommend revising the whole Sec.3 and Fig.2 to match the quality of the rest of the paper. In my view, the current level of writing clarity is not sufficient to be published on NeurIPS.\n\nAnswer 1: We agree with the reviewer and will substantially revise Section 3 and Figure 2 for clarity (see also our response to Reviewer Mrmp Q1, who raised a similar concern). Specifically, we will introduce a clear overall problem formulation and then describe each component of our approach in a logical sequence. Next, Figure 2 will be improved with proper labeling and visual clarity to complement the text. Lastly, we will ensure the writing in Section 3 is concise and reader-friendly rather than resembling a low-level technical manual. \n\n*(To avoid redundancy, we have provided a rewritten summary of Sec.3 under Reviewer Mrmp Q1 above, which equally applies here. We are committed to integrating those improvements to address the clarity issues.)*\n\n\n\n**Weaknesses 1**: Poor writing in Sec.3: Methodology. The introduction addresses the function of each module. These are not elaborated in the method section. The organization is chaotic. It is indeed rich in details, but the paragraphs are more towards a technical manual. It is great in the Supp to help understand the code, but not beneficial in understanding the novelty, design, and contribution of each proposed module.\n\nAnswer 2: We appreciate this feedback and will ensure the final paper emphasizes each module’s novelty and purpose. In summary, each of our modules targets a specific gap in prior work. \n\n1. GeoText Queries: Prior works inject language information only at the output stage. In contrast, we introduce learnable text queries at multiple encoder layers, progressively refining visual features via cross-attention throughout the network. This layer-wise textual integration improves visual–semantic alignment and robustness to domain shifts.\n2. CMPE (Coarse Mask Prior Embedding): This module provides a global weak-supervision signal in the form of a coarse mask. By embedding intermediate features into a coarse segmentation map, CMPE feeds global context and additional training supervision back into early layers. This design enhances gradient flow to the frozen backbone, improving object-level discrimination and spatial alignment even without fine-tuning the encoder.\n3. DOV-VEH (Domain-Open-Vocabulary Vector-Embedding Head): Unlike conventional decoders, DOV-VEH bridges pixel-level features and text queries in a shared embedding space. It uses a dual-attention transformer decoder to fuse visual and textual information, yielding final predictions that are both spatially precise and open-vocabulary. This addresses the semantic misalignment issue by ensuring textual class embeddings directly influence mask generation.\n\nTogether, these modules tackle the key limitations of previous methods: static one-time language injection, lack of strong supervision for the encoder, and inability to handle novel classes or domains. By combining GeoText Queries, CMPE, and DOV-VEH, Vireo achieves robust open-vocabulary segmentation that generalizes across domains. \n\n\n\n**Weaknesses 2**: The acronyms and method naming block the comprehension as well. GeoText Prompt is actually a learnable query instead of a prompt commonly referred to in LLMs.\n\nAnswer 3: We agree and will adjust our terminology to improve clarity. In particular, we will rename “GeoText Prompt” to “GeoText Query” in the paper, to better reflect that it is a learned query vector (not a free-form prompt). We will also review all acronyms and naming conventions to ensure they are intuitive. \n\n\n\n**Formatting Concerns 1**: I'm curious if adding various colors to the main paragraph text is allowed. I checked the instructions, but there's no specification regarding this. The default template definitely does not contain such a format.\n\nAnswer 4: We understand the reviewer’s concern about formatting. Using colored text for emphasis is not prohibited by the guidelines (we found no rule against it), but it is indeed unconventional. We will remove any distracting colored text in the revised submission to conform to the standard style. We believe the focus should remain on the clarity, novelty, and contributions of our work rather than on formatting choices, and we will ensure the final manuscript is polished in appearance."}, {"review_id": "FBLWY5gDL4", "reviewer": "Reviewer_8Cmf", "summary": "This paper proposes Vireo, a single stage method that solves both Open-Vocabulary semantic segmentation (OVSS) and domain generalization in semantic segmentation (DGSS) together. Vireo unifies depth features from frozen visual foundation models (VFMs) and language embeddings (CLIP) in a single segmentation framework. Main contributions include GeoText Prompts for the integration of depth and text features, a coarse mask prior embedding (CMPE) to augment gradient signals, and a Domain-Open-Vocabulary Head (DOV-VEH) for end segmentation. Large-scale evaluations demonstrate substantial performance improvements on various datasets and adverse conditions over previous OVSS and DGSS approaches.", "questions": "1. How do you precisely address open-vocabulary classes in inference, and can you give us better examples or measurements for how well you segment truly unseen classes?\n\n2. Can you also explain the unique contribution of depth features? For instance, can you perform an ablation without depth cues altogether or examine cases where depth plays a substantial role?\n\n3. You need to define clearly in your paper the meaning of \"AO\" in your ablation study table.\n\n4. Had you ensured that baselines (particularly OVSS methods) were compared in a fair manner with the same training augmentations or configurations? It would make your comparisons stronger by making this clear.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper addresses a challenging and practically interesting task: merging open-vocabulary segmentation with domain generalization, particularly for autonomous driving environments.\n\n2. The approach innovatively blends depth and linguistic information, making efficient use of powerful pretraining models.\n\n3. Experiments are comprehensive, with evident and persistent gains over several baselines under challenging conditions (night, fog, rain, etc.).\n\n4. Ablation experiments clearly demonstrate the worth of single components.\n\nWeaknesses\n\n1. Clarity can be improved: some terminology, including GeoText Prompts and Attention Optimization (AO), is not well defined. A simple description or some explanatory examples would be beneficial.\n\n2. The benefit due specifically to depth information alone is not entirely elucidated; the performance gain due to depth may be delineated more clearly.\n\n3. The comparison may not be entirely fair, as they are mostly with methods not specifically tailored for the combined OV-DGSS environment.\n\n4. It is unclear how well Vireo splits unseen novel classes during inference time. More explanation or outcomes for zero-shot classes would be desirable.\n\n5. The approach is very memory-intensive (several big models, much GPU memory usage), with practical deployment implications.", "was_revised": true, "final_justification": "Thanks for addressing my questions, keep the positive score.", "rebuttal": "## Response to Reviewer 8Cmf\n\n\n**Question 1 / Weaknesses 4**: How do you precisely address open-vocabulary classes in inference, and can you give us better examples or measurements for how well you segment truly unseen classes?\n\nAnswer 1: Thank you for the reviewer’s thoughtful question. First, Vireo is explicitly designed to generalize to novel “unseen” classes by leveraging both depth cues and textual information. Our GeoText Query mechanism injects semantic guidance at multiple network layers, which helps the model recognize classes it has never seen in training. In practice, given a new class label (text) at inference, Vireo’s pipeline does the following: a frozen visual foundation model (for RGB and depth) extracts features, and the class label is encoded into an embedding (the GeoText query). Through cross-attention, this text embedding guides the intermediate layers of the vision model, refining feature representations to be sensitive to that class’s characteristics. At the segmentation head (decoder), we further refine the textual and visual features together, which enables the model to output a mask for the new class if it is present. In summary, by fusing information from three modalities, our model can infer categories not present in the training set. \n\nNext, we validated this capability on the DELIVER benchmark, which evaluates open-vocabulary segmentation under various weather conditions. Our method shows substantial gains for unseen classes compared to prior approaches. For example, under foggy conditions, Vireo achieves an IoU of 12.15 on unseen classes, whereas the next best methods (SED and FC-CLIP) reach only 8.40 and 4.32, respectively. Similar trends hold under rainy and night conditions – Vireo consistently outperforms existing methods on the “Unseen” category while also maintaining strong results on seen classes. These results demonstrate that Vireo not only accurately segments seen classes but also effectively handles entirely new classes in challenging scenarios. This provides strong evidence of the model’s open-vocabulary capabilities and its value for advancing open-set segmentation research.\n\nTable.1 Performance Comparison of OVSS Algorithms on the DELIVER Dataset under Different Weather Conditions\n\nCloud\n|**Class**|**Ours**|**SED**|**CAT-Seg**|**FC-CLIP**|**EBSeg**|\n|:-:|:-:|:-:|:-:|:-:|:-:|\n|**Seen**|**49.14**|36.72|43.92|28.07|38.10|\n|**Unseen**|**10.96**|8.74|3.69|4.05|2.92|\n|**Mean**|**33.89**|24.40|26.22|17.50|22.62|\n\nFog\n|**Class**|**Ours**|**SED**|**CAT-Seg**|**FC-CLIP**|**EBSeg**|\n|:-:|:-:|:-:|:-:|:-:|:-:|\n|**Seen**|**49.59**|38.28|41.43|26.06|35.76|\n|**Unseen**|**12.15**|8.40|3.54|4.32|4.48|\n|**Mean**|**35.80**|25.25|24.80|17.26|22.00|\n\nNight\n|**Class**|**Ours**|**SED**|**CAT-Seg**|**FC-CLIP**|**EBSeg**|\n|:-:|:-:|:-:|:-:|:-:|:-:|\n|**Seen**|**41.63**|34.99|34.89|23.03|26.03|\n|**Unseen**|**9.52**|7.27|2.30|4.09|2.09|\n|**Mean**|**29.89**|22.79|20.56|14.93|15.50|\n\nRain\n|**Class**|**Ours**|**SED**|**CAT-Seg**|**FC-CLIP**|**EBSeg**|\n|:-:|:-:|:-:|:-:|:-:|-----------|\n|**Seen**|**47.15**|37.35|41.43|25.59|31.96|\n|**Unseen**|**12.27**|8.95|5.81|4.60|5.57|\n|**Mean**|**33.46**|25.18|26.53|16.59|20.35|\n\nSun\n|**Class**|**Ours**|**SED**|**CAT-Seg**|**FC-CLIP**|**EBSeg**|\n|:-:|:-:|:-:|:-:|:-:|:-:|\n|**Seen**|**55.16**|41.68|45.10|26.52|42.63|\n|**Unseen**|**11.46**|8.12|4.91|4.73|6.14|\n|**Mean**|**38.49**|27.14|28.21|16.93|26.41|\n\n**Question 2 / Weaknesses 2**: Can you also explain the unique contribution of depth features? For instance, can you perform an ablation without depth cues altogether or examine cases where depth plays a substantial role?\n\nAnswer 2: Thank you for your question. Depth features are a crucial contributor to Vireo’s performance, and our ablation studies confirm this. In Table 4 of the main text, we compare model variants with and without incorporating depth. The results show that adding depth cues consistently improves accuracy across different environments. For example, on the Cityscapes→ACDC generalization task, the baseline without depth achieves 72.5 mIoU (Rain scenario), whereas integrating depth information raises it to 72.8, and further refinements of our depth module yield even higher gains. Across all tested weather conditions and datasets, the inclusion of DepthAnything’s features yielded improved mean IoU. This demonstrates that the depth module provides complementary structural information (e.g. scene geometry) which pure RGB vision lacks, thereby enhancing segmentation quality. We will make sure to highlight the depth ablation results clearly in the paper (and additional qualitative examples in the supplement) to underline how and where depth cues make a difference. \n\nTable.4 in main text: Ablation studies on component configurations of the proposed **Vireo** under the *Citys. → ACDC with Snow, Night, Fog, Rain + BDD. + Map.* generalization setting.\n| Configurations|Snow|Night|Fog|Rain|BDD.|Map.|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n| REIN|70.6|55.9|79.5|72.5|63.5|74.0|\n| + concat depth feature |70.8|56.1|79.4|72.8|63.6|74.2|\n| + Depth Anything V2|71.5|56.7|80.5|73.3|64.4|74.5|\n| + DA + AO|72.2| 57.4|80.9|74.2|65.1| 75.0|\n| + DOV-VEH|70.9| 56.2|79.8|72.8|63.7| 74.2|\n| + CMPE|71.6|56.9|80.2|73.4|64.1|74.6|\n| + GeoText Prompts|74.0|58.4|81.1|74.8|65.3|75.3|\n| **Vireo**|76.2|60.6|82.3|76.3|66.7|76.0|\n\n\n**Question 3 / Weaknesses 1**: You need to define clearly in your paper the meaning of \"AO\" in your ablation study table. \n\nAnswer 3:  “AO” stands for Attention Optimization. This refers to the mechanism within our Tunable Vireo module that optimizes cross-attention between vision and text features. In simpler terms, AO is the process by which the model aligns visual features with textual queries through learned attention operations. We will clarify this terminology in the paper (particularly in Figure 2’s caption and the text), so that readers understand that AO = Attention Optimization in the context of our model. \n\n**Question 4**: Had you ensured that baselines (particularly OVSS methods) were compared in a fair manner with the same training augmentations or configurations? It would make your comparisons stronger by making this clear.\n\nAnswer 4: Yes, we took care to ensure fair comparisons with all baseline methods. We trained the OVSS baselines under their recommended settings on the same hardware (a single NVIDIA A6000 GPU) and for the same number of iterations as our method. Wherever possible, we used each method’s official code and followed their suggested training recipes (including data augmentations and hyperparameters). In cases where we adjusted data augmentations for consistency, we did so in a minimal way that does not give our model any undue advantage. In summary, all methods were evaluated in a uniform framework, and no baseline was disadvantaged in terms of training conditions. We will add a statement in the paper to make this fairness clear. \n\nWe will incorporate brief, reader-friendly explanations of both terms (and any other unclear terminology) along with possibly a diagram or example in the supplement to ensure these concepts are easily understood.\n\n\n**Weaknesses 3**: The comparison may not be entirely fair, as they are mostly with methods not specifically tailored for the combined OV-DGSS environment.\n\nAnswer 6: This is a valid point. Since our work is the first to tackle the joint Open-Vocabulary + Domain-Generalized Segmentation problem, there were no perfectly matched baselines available. We had to compare against methods from related areas (OVSS and DGSS separately) using our combined evaluation settings. We acknowledge that those methods were not originally designed for the exact scenario we consider; however, we still believe the comparison is informative. It demonstrates how state-of-the-art OVSS methods perform in a DGSS context, and it highlights the progress made by our approach. In fact, our results show that by bridging OVSS and DGSS, Vireo achieves significantly better generalization than either type of baseline could on this combined task. We will clarify this context in the paper to assure readers that while the baselines were the closest available choices, the superior performance of Vireo underlines the effectiveness of our proposed ideas. \n\n\n**Weaknesses 5**: The approach is very memory-intensive (several big models, much GPU memory usage), with practical deployment implications.\n\nAnswer 6: We respectfully clarify that Vireo’s runtime memory requirements are manageable and on par with related methods. In our experiments, the model requires only about 4–5 GB of GPU memory during inference, which is not excessive for modern GPUs. For training, we used a single NVIDIA A6000 (48 GB), similar to other OVSS models’ requirements. We also note that there are straightforward ways to reduce memory consumption for deployment: for example, applying model quantization or pruning can substantially cut down the memory and compute footprint without significant loss in accuracy. In practice, one could also run the depth module on a CPU or a smaller network if needed, depending on deployment constraints. We will mention these points in the paper to address the concern. Overall, we do not anticipate our model’s memory usage to be a barrier to research or real-world adoption, especially given the available tools to optimize model size. We hope this explanation alleviates the reviewer’s concerns regarding practicality."}]

Author rebuttal:

Meta-review: This paper proposes a single-stage framework, termed Vireo,  for Open-Vocabulary Domain-Generalized Semantic Segmentation. Vireo mainly consists of three components: GeoText Prompts for aligning geometry and textual semantics; Coarse Mask Prior Embedding for faster convergence and improved gradient flow; and Domain-Open-Vocabulary Vector Embedding Head for fusing semantic and geometric cues. Extensive experiments are conducted to show the effectiveness of the proposed method.

Four reviewers reviewed this paper with the overall ratings of 3 borderline accept and 1 borderline reject. The reviewers recognized the contributions, such as valid task, well-described methodology, the first framework on open-vocabulary segmentation with domain generalization, good experimental results, and thorough experiments and ablation studies. Meanwhile, they pointed out some suggestive comments and critical issues, such as lack of discussion of limitations, poor presentation, and memory-intensive. Most reviewers are concerned about the presentation and clarity. Thanks to the authors' rebuttal, most of the reviewers' concerns are addressed properly.  

This paper considers a joint setting of open-vocabulary semantic segmentation (OVSS) and domain generalization in semantic segmentation (DGSS), which is a challenging and practically interesting task. The authors designed Vireo with specific components to address the challenges and achieved superior performance as compared to be baselines.  The AC recommends accept and strongly suggests the authors revise this paper based on the detailed comments and improve the presentation carefully.
Decision: Accept (poster)