QPrompt-R1: Real-Time Reasoning for Domain-Generalized Semantic Segmentation via Group-Relative Query Alignment
Authors: Fengyuan Lu
Zixuan Duan
Xunzhi Xiang
Zhicheng Zhang
Wenbin Li
Yang Gao
Qi Fan
Venue: ICLR 2026 Poster (2026)
Keywords: Semantic Segmentation; real time; domain generalization
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Deploying semantic segmentation in driving and robotics requires both real-time inference and robustness to domain shifts, formalized as Real-Time Domain-Generalized Semantic Segmentation (RT-DGSS), which has not been fully addressed. Existing methods often treat real-time(RT) inference and domain generalization (DG) separately, with DG improving robustness but lacking real-time performance, and real-time models being brittle under distribution shifts. To address the RT-DGSS problem, we propose QPrompt-R1, a real-time Query-Prompt architecture built on a ViT backbone. QPrompt-R1 injects a small set of learnable queries only at the final transformer block, performing a single query–image alignment step and eliminating decoder overhead. To further enhance alignment without test-time cost, we introduce a Group Relative Query Alignment (GRQA) objective, which uses group-relative supervision within each group to align queries with features, improving domain generalization through group-relative rewards. QPrompt-R1 achieves 54 FPS, delivering strong performance in synthetic-to-real transfer, real-to-real generalization, and robustness under adverse conditions. Additionally, GRQA is plug-and-play, improving state-of-the-art DGSS methods like REIN (+1.2) and SoMA (+0.5) without inference-time overhead.

Published as a conference paper at ICLR 2026
QPROMPT-RI: REAL-TIME REASONING FOR Do- MAIN GENERALIZED SEMANTIC SEGMENTATION VIA GROUP-RELATIVE QUERY ALIGNMENT
Fengyuan Lu', Zixuan Duan', Xunzhi Xiang', Zhicheng Zhang", Wenbin Li', Yang Gao', Qi Fan
Naanjing University
Shenzhen International Graduate School, Tsinghua University fanqi n ju edu.cn cn
ABSTRACT
Deploying semantic segmentation in driving and robotics requires both real-time inference and robustness to domain shifts, formalized as Real-Time Domain- Generalized Semantic Segmentation (RT-DGSS), challenge not fully addressed. Existing methods treat real-time (RT) inference and domain generalization (DG) separately, with DG improving robustness but lacking real-time performance. To tackle the RT-DGSS problem, we identify that the bottleneck in DG is the pre- diction head, not the backhone. We introduce QPrompt-R1, real-time Query- Prompt architecture based on the powerful VFM backbone. QPrompt-RI inte- grates reasoning by injecting learnable queries into the final transformer block. leveraging contextual learning to enhance segmentation performance under do- main shifts while maintaining
inference. To further optimize reason- ing without extra inference cost, we introduce a Group Relative Query Aligument (GRQA) training objective, which strengthens the relationship between queries and image tokens through group-relative advantage supervision, unlocking the do- main generalization potential VFMs. QPrompt-RI achieves 54 FPS, delivering strong performance in synthetic-to-real transfer, real-to-real generalization, and robustness under adverse conditions. GRQA functions as a allg-and-play mod- ule, improving DGSS methods such REIN (+1.2) and SoMA (+0.6) without introducing inference-tine -imen overhead. The code available at
INTRODUCTION
Semantic segmentation in autonomous driving and robotics requires both real-time inference and robustness distribution shifts. Real-time segmentation supports safety-critical tasks like obstacle avoidance in autonomous vehicles (Grigorescu et al.) 2020 Holder Shafique 2022) and robot navigation (Guan al. (002).. Robustness ensures generalization across environments, handling weather, lighting, and terrain variations (Gao et al. 2024).
Recent research has treated real-time performance and robustness under distribution shifts as sep- arate optimization goals. Current DGSS approaches (Wei et al. 2024 Yun et al.) 2025 Bi et al., 2024 Zhang & Robby T., (2025) mainly focus on exploit VFMs to improve robustness. contrast, RTSS methods focus on designing novel architecture to optimize the accuracy-
trade-off for high-speed performance. Despite their advances, DGSS methods face high computational costs, hin- dering real-time deployment, while RTSS methods sacrifice domain adaptability due to fixed class embeddings. This trade-off underscores a critical gap in current research: Why do existing methods fail to effectively combine RT and DGSS?
State-of-the-ard DGSS methods (Wei et al. 2024: Yun al. 2025 Bi al. 2024 Zhang & Robby T.
2025 Liu al. 2025) leverage VFMs to enhance domain robustness. However, identify that the speed bottleneck lies not the VFMs backbone, but in the sophisticated segmentation head. Most DGSS models, based on query-based head, rely complex segmentation head with pixel encoder and transformer decoder, using the VFM only in the backbone as fig. Th. Replacing
"Corespponiigg author.

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Published as a conference paper at ICLR 2026
pred
Miuser
SuMA+OOux 86
SoMA
Heavy Head
FADA REIN+OUrS
Oprompt-RI
REIN
Tell (latersction ever Laleo)
A Mask2Tormer
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DINO<2 -LP X
S-MA
(a) Prior Archi
PADA
BEIN
GROA
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Lest layar
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VFM
Quary Prompt
Real-time
GCNet-L
25
28
40
155
Oprompt- (1
(Frames Second)
(c)
Figure I: (a) Prior DGSS architectures rely on heavy segmentation heads. (b) QPrompt-RI inte- grates query prompts the final VFM layer with train-only GRQA, enabling efficient alignment and generalization. (c) Speed-accuracy trade-off under GTAV- Citys, BDD, Map). QPrompt-R1 achieves optimal balance, while GRQA remains scalable and further boosts performance.
the query-based head with simpler MLP head, commonly used in RTSS methods, results in significant speed boost (FPS), as shown in Fig [c). highlighting the query-based head as the time bottleneck of DGSS. On the other hand, most RTSS methods (Xu et al. 2022 2023 Yang et al. 2025) rely on CNN architectures, which cannot effectively leverage the generalization power of VFMs. Moreover, they lack the ability for in-context adaptive learning, they learn fixed set of class embeddings, while the query-based approach can fuse with image token context for adaptive learning, effectively mitigating the effects of domain shifts. Therefore, we need a novel architecture for RT-DGSS problem.
Recent advances in reinforcement learning (RL) have introduced new paradigms for post-training large models, enabling alignment with complex objectives beyond supervised fine-tuning. In lan- guage models, Matt introduced Group Relative Policy Optimization (GRPO) (Shao et al.)
2024). which replaces the critic with group-relative baselines, pushing the boundaries of mathemat- ical reasoning. GRPO has also been applied to vision tasks (Pan et al. 2025
Huang et al., 2025 Yu
et al. 2025). enhancing the capabilities of vision models.
To address the RT-DGSS problem, we introduce the QPrompt(Fig. TD), a lightweight, single-layer prompting mechanism integrated at the final Transformer layer in VFM, inspired by prior query- based model and prompt/token tuning for vision models (Jia et al. 2022; Kerssies et al.] 2025). Concretely, QPrompt injects a small set of learnable queries only into the final transformer block, ap- proximating query-based decoding without a multi-layer decoder stack. QPrompt retains the adap- tive nature of query-based methods, with minimal computational overhead, adding only K extra tokens in the final transformer layer. However, a single layer interaction between image tokens and queries is insufficient for robust performance. Inspired by GRPO (Shao et al. 2024), propose Group-Relative Query Alignment (GRQA) to enhance query-promppe soning. Our key insight train all queries within class-specific group, allowing multiple queries to acquire segmentation competence and mitigate failures under domain shifts. Unlike Hungarian matching, which assigns single query to each ground-trun -trnt mask GRQA leverages group-relatie advantages to enable mutual supervision and jointly optimize all queries. This method is fully super- vised during training, with all auxiliary components disabled at test time, ensuring no inference-time overhead. As a plug-and-play module, GRQA can be easily integrated with existing DGSS methods like REIN and SoMA, providing performance improvements without increasing inference-tine cost.
domain geaiinn-gnrrrlizeed rea-
Building upon this, we propose QPrompt-R1. real-time, domain-generalle generalized semantic segmenta- tion model (QPrompt) enhanced with Group-Relative Query Alignment (GRQA) optimization for improved reasoning. QPrompt-R1 achieves sustained inference speed of 54 FPS, demonstrating strong synthetic-to ro-rell transfer, real-to-real generalization under adverse conditions. QPrompt- RI performance pushes the frontier of real-time systems while narrowing the performance gap to domain -enarlleenerrlizee semantic segmentation (DGSS) methods.
We make the following contributions:
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Published as a conference paper at ICLR 2026
We highlight Real-Time Generalized Semantic Segmentation (RT-DGSS) an important and practical research challenge, addressing both robustness to domain shifts and real-time inference efficiency.
We propose QPrompt-R1, a real-time and robust semantic segmentation model, along with plug-and-pln GRQA training strategy designed to enhance the model's generalization. QPrompt-RI achieves a balanced trade-off between performance and efficiency.
.
Group-Reen Relative Query Alignment (GRQA) is generalizable approach that can be com- bined with existing DGSS methods to push the limit of domain generalization.
2 RELATED WORKS
Domain Generalized Semantic Segmentation. Romain-generalle semantic segmentation (DGSS) aims to maintain high accuracy under distribution shifts from diverse urban layouts, weather, and lighting conditions. Early methods used style transfer, feature normalization, and ad- versarial alignment (e.g., (Zhou et al. 2022c), (Chattopadhyay et al.) 2023), (Kim et al. 2022). (Cho et al. 2023). Pann et al., 2018) to learn domain-invariant representations. Recently, vi- sion foundation models (VFMs) have become powerful backbones for DGSS, with methods like REIN Wei et al. 2024). FADA (Bi et al. 2024). and SoMA (Yun et al. 2025) refining VFMs through parameter-effiiiet tuning, frequency-domal approaches, such as MFuser (Zhang Robby T. 2025), combine VFMs with vision-language models (VLMs) to exploit multimodal priors. Despite these advances, most DGSS methods fo- cus on robustness, neglecting real-time applicability, which is crucial for safety-critical tasks like autonomous driving and roboties. We contend that both robustness and efficiency must be jointly addressed. While previous methods implicitly touch upon these aspects, we establish Real- Time Nomain-Generann Semantic Segmentation (RT-DGSS) evaluate the trade-off between inference speed and domain generalization.
adaptation, low-rank adjustments. Other
distinct research setting to rigorously
Real-Time Semantic Segmentation. Real-time semantic segmentation is crucial for applications such as autonomous driving and robotics, where fast, reliable pixel-level prediction is required. Early work mainly relies on lightweight CNN designs to balance accuracy and efficiency, e.g., BiSeNet (Yu et al., 2018) decouples spatial detail and context with dual-path architecture, and PIDNet (Xu et al. 2022) introduces a three-branch structure to explicitly model boundary cues. More recently. transformer/hybrid designs have also achieved strong accuracy-latency trade-offs. RTFormer (Wang et al., 2022) proposes an efficient dual-reet resolution transformer with GPU-friendll attention for real-time seg segmentation. SeaFormer (Wan al. 2023) develops mobile-friendly axial- transformer backbones coupled with lightweight segmentation heads, targeting low-latency deploy- ment on edge devices. Next-ViT (Li al. 2022) introduces an efficient deployment-orientee back- bone that offers a strong latency-accuracy trade-off for dense prediction. Despite these advances, prior RTSS methods primarily optimize latency and in-domain accuracy, leaving robustness under domain shifts largely unexplored. In contrast, we target Real- Time Domain Generalized Semantic Segmentation (RT-DGSS), which preserves efficiency while improving cross-domain generalization.
RL-bbased Post-training and GRPO. Reinforcement learning (RL))u et al., 2022) Schul- man et al. 2017) et al. 2025) has become essential for post-training large models, aligning them with objectives beyond supervised fine-tuning. In language models, DeepSeekMath intro- duced Group Relative Policy Optimization (GRPO) (Shao et al. 2024), replacing the critic with group-relative baselines to enhance mathematical reasoning. Similar approaches were applied to vision: (Pan et al.) 2025) proposed Group Relative Query Optimization (GRQO) for denser query supervision in vision transformers, (Yu et al. (025) applied GRPO to multimodal tasks, and Vision- RI (Huang et al. 2025) demonstrated improvements in multimodal reasoning. However, group- relative optimization for dense prediction tasks like semantic segmentation remains underexplored. We address this gap with Group Relative Query Alignment (GRQA), which adapts GRPO-style rewards for query-image alignment in segmentation transformers without additional inference cost.
3 METHODOLOGY
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MAP
Seg loss (eft)
Ing tokene
Prototype
Img align loss
Predict Head
Updete
Prototype
Mox
Querys
Learnable Queries
1) layers
VET
Sinllanity Mitrrcc
Reward
BRQA
loss
EMA
update
DowlTollTwell
Refer
Imput
model
Qprompt
GRQA(training only)
Figure 2: Overview of QPrompt-RI, Left: QPrompt employs VIT backbone, injecting learnable queries only in the final layer to capture context, followed by prediction head. Right: The GRQA module builds a prototype bank and computes group-relative advantages to optimize queries, en- hancing domain -emaiin-generllzeed reasoning. GRQA is training-only and incurs no inference overhead.
3.1 PRELIMINARIES:
POLICY OPTIMIZATION (GRPO)
Relative Policy Optimization (GRPO) (Shao et al. 2024 is simplified policy-optimization framework that replaces the value function with a group-relative advantage. Given a group of outputs, each output's advantage is computed by subtracting the group mean reward: A; = σ Ej The policy is updated by maximizing PPO-style objective augmented with KL regularization:
COROO E min (prA,, 1
DKI. Meerl
Te/ where Ty denotes the policy probability, = - Tref the importance ratio between the current and reference policies, and the KL divergence term encourages conservative updates by keeping the learned policy close to reference model. These designs contain two central ideas ideas-grous gooupp relative advantages and conservative updates, which form the basis of GRQA objective.
3.2 QPROMPT FOR SEMANTIC SEGMENTATION
Since VFMs are pretrained on large-scale datasets and demonstrate strong generalization (Oquab) al. 2023 Kirillov et al. 2023), we aim to exploit their inherent strengths with simple architecture and training-onle strategy rather than heavily relying complex segmentation heads. Motivated by query-based heads (Cheng et al. 2022), which allow queries to adaptively interact with image tokens for in-context learning, introduce real-time architecture, QPrompt (as illustrated in Fig. 2). to preserve the advantages of muery-based approaches while reducing computational overhead. Formally, define the VFM backbone sequence of L > 2 Transformer blocks, [BI,... B3, )-
Let Ig E N xx denote the token sequence after block f. An input image is first processed by the backbone through the initial L 1 blocks, producing the intermediate tokens TT-1- 1. At the final block, K learnable queries Q E R xxxd are concatenated with the (L 1)-th output to form the augmented tokens:
Ør.- = [Q,
which are then fed into the last Transformer block (-)()) The block outputs the refined queries and updated image tokens:
[1,, (r.....
Following standard query-based decoding, each refined query Qr. predicts class logits and generates per-pixel predictions by attending back to image tokens æy. To recover fine-grained boundaries from patchified VFM features, QPrompt employs lightweight upsampling head, consisting of two learnalle transposed transposed-convoluteee convolution layers. In this way, queries serve as adaptive class embeddings that directly produce the segmentation map without requiring additional decoder stack.
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By injecting queries the last block, QPrompt approximates the role of both pixel encoder and transformer decoder in conventional query-based methods within a single layer interaction. Previous query-based model (Cheng et al. 2022) employs pixel encoder followed by a multi-layer decoder, with complexity O(M(N + k (dd), M is the number of decoder layers. QPrompt reduces to a single Transformer block over N K tokens (O((N + K))d)), retaining properties of query-based methods and improve inference speed. Unlike EoMT (Kerssies et al. 2025), whose complex mask attention and annealing introduce detrimental train-test process discrepancy, QPrompt is simpler, consistent architecture that ensures train-test parity for stable generalization.
3.3 GROUP RELATIVE QUERY ALIGNMENT
While QPrompt leverages query-based methods to reduce computational overhead, a single-layer interaction between image tokens and queries may be insufficient for robust generalization, particu- larly for handling domain shifts. To address this, we propose the Group Relative Query Alignment (GRQA) strategy, which enhances query interactions without increasing inference cost. During training. Hungarian matching (Cheng et al., (021) assigns only one query per class, relegating the others to the background. This results in only one query per class being supervised, preventing the training of multiple alternative queries to handle domain shift issues (Wen & Li 2024). To enable efficient and stable query optimization, we adopt a momentum-updatted prototype bank as standard class anchors widely used in prior segmentation works (Tang et al. 2025 Zhou et al. 2022b).
Prototype Bank. To provide stable, class-specific references for query learning, we maintain a momentum-updated Prototype Bank, Specifically P {Pe] where Pe E IR denotes the pro- totype for class c. For each training image. we compute per-image prototype fe by avgpooling fg-normalized pixel embeddings within the ground-truth region of class The Prototype Bank P. is updated via exponential moving average:
Pe f norm(aP. + (1 - f.)),,
(4)
where a controls the update rate. To further reduce the intra-class feature variance, we explicitly enforce consistency between fa and Pa with
Liny =
Σ oEG.
life
Pell2
(5)
C.
where G. is the set of classes present in the current batch. This regularization encourages per-image prototypes to be close to global anchors, stabilizing training and enhancing feature consistency.
Alignment Reward. Directly optimizing image-prototy: regularization loss stabilizes training, but query-based segmentation depends on query-image token alignment and interaction, so opti- mizing only one does not markedly improve segmentation ability. To enable each query focus on the most relevant class-specific information while avoiding query collapse, we define an alignment reward. Let the refined queries be denoted Qr. E KR For simplicity, we assume that Qn has already been normalized, and we denote the resulting queries as Q nQrm(Q2). Given the mo- mentum Prototype Bank P C. (Sec. 3.3). the query- protooype similarity matrix S R kxC is computed as:
S = QPT,
St,, P3),
(6)
where St,j represents the cosine similarity between query and prototype P,. For each query i, we select the 1 class as the most similar class:
C
r4 ===60 S
Here, C is the index of the most similar class for query i, and r, is the corresponding similarity score, which serves our alignment reward.
The inner product = QP is used to compute the query-prototype similarity, consistent with the core principle of query-based methods, where each query's prediction is based on its similarity to class prototypes. By using the similarity score r; as the reward, we encourage alignment between each query and its most relevant class prototype, ensuring that all queries effectively trained.
Group-Relative Advantage. To foster more efficient query- quere-image tokens interactions, we intro- duce the Group-relative Advantage approach, inspired by the group-relative advantage concept in
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GRPO (Shao al. 2024). By comparing each query's performance relative to others in its group, we ensure that queries effectively fuse with their most relevant class prototypes, improving segmen- tation accuracy. We partition the K queries into G groups [ where each group G consists of queries that share the same most similar prototype c₁ as computed in Sec 3.3
For each group gg. we compute baseline defined by the mean and standard deviation of the rewards r, for the queries in that group:
9
Σ iEG
Tt
0g
[9
Σ (nr - Mg) iEG
(8)
Here, Mg is the mean reward, and is the standard deviation, with e ensuring numerical stability. Next, we define the group-relative advantage for each query i 9g as:
r; Pg
0g +
The advantage A, measures how much query i's reward deviates from the group's baseline. If A, > 0, query i outperforms its group, indicating successful fusion with the most relevant prototype and deserving reward. If A, < 0, the query underperforms and should be penalized to encourage improvement. This group-relative advantage motivates queries exceeding the baseline to fuse with the most relevant prototypes and enhance their reasoning ability.
GRPO-style Clipping with Reference KL Stabilization. While group-relative advantages provide dense supervision, they exhibit high variance and occasionally induce overly large updates. To keep query updates conservative and stable, we adopt GRPO/PPO-style clipped objective together with a KL regularization term an EMA reference model. We maintain reference model Oret, which is an exponential moving average (EMA) of the current model parameters 6, This reference model serves
stable guide for the current model by providing a reference distribution for com- parison. Given the query query-prototype similarity matrices So = QP and Sret = Quea them into per-query class distributions via softmax:
we convert
exp(S (So (i.
exp(Srer(i..))
(i.j))
(ee) = E, exp((See((,,j)))
(10)
ES exp
For each query i, we define the importance ratio as:
Ta(i,c))
Pj Toof (1, c4)
(11)
Following the principles of Proximal Policy Optimization (PPO) and GRPO, we define our group- relative clipped objective as:
CGR
Σ Σ min (piAi, clip(e;, 1 - 1 + ())
(12)
K
gml
where the clipping function limits the importance ratio P. from deviating excessively from 1, thereby preventing unstable updates. This mechanism ensures that the model's alignment improves gradu- ally, promoting stable learning. Additionally, regularize the current model's distribution with respect to the reference distribution using forward KL divergence:
Tee (i, log Te(i, ci)
Trer(l.Ca) To((,c;))
DKL [[T]|
(13)
K
This KL divergence term prevents abrupt shifts in the model's behavior while allowing for gradual and stable improvements. Finally, the overall GRQA alignment loss is defined as:
LGRQA
1
Σ
min (p:A,, clip(es, - 1 + c) A4) + [DKu(mol|ned),
(14)
where > 0 is a small constant. The GRQA loss captures the alignment between queries and prototypes, while ensuring stable training through the regularization and clipping mechanisms.

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Published as a conference paper at ICLR 2026
Overall training objective. Finally, the total training loss combines the standard segmentation loss, the prototype alignment loss, and the GRQA alignment loss:
Luaal Lueg + Aing Ling + Agnya LORQA.
(15)
where Lrey is the supervised segmentation loss, Limg aligns per-image prototypes with the bank, and LORQA is the group-relativy alignment objective. Xing and Agauu trade-off weights.
4 EXPERIMENTS
Datasets. We evaluate QPrompt-RI on real and synthetic scene datasets, reporting segmentation
accuracy and efficiency. The Cityscapes (Citys) dataset (Cordts et al. 2016) includes 2,975 training and 500 validation images at 2048 x 1024. We also use BDDI00K (BDD) (Yu et al. 2020) and
Mapillary (Map) (Neuhold et al., 2017) as out-of-domain benchmarks, with 1,000 and 2, 2,000 valida-
tion images at 1280 x 720 and 1902 x 1080, respectively. We use GTAV (Richter et al. 2016) with 24,966 labeled frames from an open- world simulator for synthetic data.. We also evaluate on four ACDC splits (Sakaridis et al. ) for adverse conditions: Fog, Night, Rain, and Snow.
Evaluation setting. We use three evaluation protocols: (i) GTAV-RRea trained on GTAV, tested on Citys, BDD, and Map: (ii) Real- trained on Citys, tested on BDD and Map (iii) Real-ACDC: trained on Citys, evaluated on ACDC's adverse-condition splits. (iv) Clean-> Cor- ruptions: trained on Citys, evaluated on Cityscapes-C. Segmentation accuracy is measured by mloU, and efficiency by FPS. Inference speed reported on a single NVIDIA RTX 4090 GPU with batch size of 1. Inference is conducted at a resolution of 512 x 1024, with real-time baselines evaluated at their official resolutions.
Implementation details. We DINOv2 (Oquab et al. 2023) the ViT backbone. To obtain module. Each layer upsamples the logits by a factor x producing an overall x4 upsampling. During training, Leeg is used for the first two-thirds of epochs to train base model, which is then initialized for the GRQA phase. In the final third, GRQA training performed, with EMA updating the reference model. Images are cropped into 512 x 512 patches using sliding window.
fine-grained details in predictions, we use a two-layer transposed-convol
Baselines.
We compare against both domain generalization and real-tine cell-time segmentation methods. For DG baselines, we include Mask2Former (Cheng et al.) (022)) with D2no (Oquab et al.) (023). REIN (Wei et al. 2024), FADA (Bi et al., 2024), SoMA (Yun et al., 2025). and MFuser (Zhang &
Robby 2025), tollowing their reported training settings and input resolutions. For real-time seg-
mentation, we evaluate Next-ViT ViT (Li et al. 2022), RTFormer (Wang et al., 2022), SeaFormer (Wan)
(Xu et al. 2023 GCNet-L (Yang ce al., 2025). and EoMT (Kerssies et al. 2025), representing strong and efficient variants. For (iv)
et al., 2023), PIDNet-L (Xu et al.) 2022), SCTNet-B-Segl
Clean Corruptions setting, we compare with SegFormer (Xie et al.) 2021), FAN (Zhou et al.
2022a), TAPADL (Guo et al. (023) and REIN. All baselines are tested at their recommended infer- resolutions for fair comparison.
4.1
QUANTITATIVE RESULTS
As shown in Table 2 our method achieves the best balance between accuracy and efficiency, con- sistently delivering real-time inference at 54 FPS while maintaining strong segmentation accuracy.
GTAV source (GTAV - Real). On GTAV-to-real adaptation, our method reaches 64.1 1 mIoU, surpassing the best real-time baseline (EoMT) by 3.1 mloU. Its accuracy is comparable to advanced DGSS methods such as REIN, yet our model runs over x5 faster, ensuring real-time applicability.
Cityscapes source (Real + Real). Across real-world datasets, our method achieves 67.8 mloU, exceeding the best real-time baseline by 1.7 mloU. Performance is competitive with strong DGSS models such M2F, while maintaining real-time speed for superior efficiency-accuraal trade-off.
Cityscapes source (Real - ACDC). Under adverse weather conditions, ours attains 69.4 mIoU, improving EoMT by 3.0 mloU and showing greater robustness in challenging scenarios. Ac- curacy approaches leading DGSS methods, while running faster for practical deployment.
Cityscapes source (Clean Corruptions). Across all corruptions, our method achieves the best robustness with 69.8 mloU, showing clear gains especially under Noise and Blur. It also runs at 54 FPS, offering strong resilience while remaining suitable for real-time use.
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Table 1: Comparison between domain generalization semantic segmentation (DGSS) and real-time semantic segmentation (RTSS) methods on GTAV->Real, Real-> and Real->CCDC bench- marks. "M2F" denotes Mask2Former, "Seaf" denotes SeaFormer, "Next" denotes Next-Vit "RTF" denotes RTFormer; indicates our re-implementation using official source code with default set- tings, results are reported in mloU (%) and inference speed FPS.
GTAV + Real
Real + Real
Real + ACDC
Method
Refer
FPS
Citys BDD Map
Avg
BDD
Map
Avg
Fog
Night
Rain Snow
Avg
M2F
CVPR22
63.7
57.4
64.2
61.7
63.7
70.4
67.1
78.4
51.9
70.5
68.9
67.4
11
REIN
CVPR24
66.4
60.4
66.1
64.3
65.0
72.3
68.7
79.5
55.9
72.5
70.6
69.6
10
DGSS
FADA
NeurIPS24
68.2
61.9
68.1
66.1
65.1
75.8
70.5
80.2
57.4
75.0
73.5
71.5
8
SoMA
CVPR25
71.8
61.3
71.6
68.2
67.0
76.5
71.8
74.7
61.7
77.8
77.3
74.4
10
MFuser
CVPR25
70.2
63.1
71.3
68.2
65.8
77.9
71.8
82.3
57.9
78.6
74.9
73.5
3
Next"
Arxiv22
50.1
30.4
40.2
40.2
52.8
60.9
56.9
71.1
20.1
54.3
49.2
51.1
57
RTF
NeurlPS22
45.3
26.2
38.6
36.7
43.2
56.3
49.8
69.4
16.4
49.1
43.3
44.6
94
Seaf"
ICLR23
46.9
27.4
33.1
35.8
40.4
51.7
46.1
65.8
17.2
47.7
40.5
42.8
70
PIDNet"
CVPR23
45,7
28.1
35.9
36.6
43.4
54.5
48.9
66.9
15.2
48.7
48.1
44.7
46
RTSS
SCTNet*
AAA124
43.3
23.7
39.0
35.3
34.1
51.1
42.6
59.6
16.0
44.8
37.5
39.5
131
GCNet"
CVPR25
25.7
20.9
26.9
24.5
38.0
50.8
44.4
63.0
11.1
42.4
33.1
37.4
53
EoMT
CVPR25
62.1
57.2
63.7
61.0
62.6
69.7
66.1
77.8
52.7
69.7
65.4
66.4
52
Ours
66.1
59.0
67.1
64.1
63.8
71.7
67.8
79.5
53.1
74.2
70.6
69.4
54
Table 2: Results on Cityscapes + Cityscapes-C (level-5) datasets. In Cityscapes-C, level cor-
responds to the most severe corruption intensity. "** indicates our re-implementation using official source code with default setting.
Cityscpes
CityscaessC (level-S)
Method
Blur Glass
Noise Impul Shot Speek
Digital Contr
Weather
Arg
FPS
Motion
Defoe
Gauss
Gauss
Bright
Satur
JPEG
Snow
Spatt
Fog
Frost
SegFormer'
57.6
54.5
46.5
48.0
17.6
21.1
22.0
56.5
76.6
65.6
72.5
40.8
36.8
57.4
71.6
35.2
48.8
FAN*
59.4
55.1
56.3
52.5
19.3
26.3
30.1
56.1
77.4
68.0
74,3
45.7
47.9
65.6
77.3
TAPADL
60.1
55.5
51.4
51.2
22.3
27.1
3244
57.9
80.6
67.3
77.9
49.2
48.7
69.0
76.9
37.3
54.1
REIN
68.5
71.7
69.7
68.7
6.2
23.0
13.1
63.7
81.5
78.9
80.6
68.8
63.8
73.6
79.5
47.9
60.0
Qurs
68.7
74.5
71.1
75.0
60.4
60.9
66.7
75.1
79.4
74.8
78.4
70.1
65.5
72.5
76.0
48.0
69.8
4.2
QUALITATIVE RESULTS
Segmentation Results.
Exemplar segmentation results are presented in Fig. comparing the per- formance across the GTAV - Citys, BDD, and Map settings. Our method achieves better pixel-wise wise predictions than real-time methods, including PIDNet, GCNet, SCTNet, and EoMT.
Impact of GRQA on Queries. We train on GTAV and compare two setups: SFT-base (without GRQA post-training) and GRQA post-training. First, we compute the average similarity between queries and class embeddings Citys, BDD, and Map test sets. As shown in Fig. 4 GRQA increases the similarity between each query and its most relevant class embedding, enhancing query uti- lization. We also examine query activation rates, defined as queries assigned to the background class. Across the dataset, higher number of activated queries indicates that more queries participate in segmentation, reflecting higher query utilization. As shown Fig. (S) activated queries increased by 45%, 48%, and 52% under GRQA, demonstrating improved query utilization and stronger segmentation of relevant objects.
Antive Querka
24
Buw GRDA
Ran DADA
Rani OR0A
Cityx
8D0
Map
Figure 5: GRQA boosts query activation for segmentation.
Table 6: Qprompt with different re- wards (trained on GTAV).
4.3 SCALABILITY OF GRQA
Plug-and-Play Gains for State-of-the-Art DGSS. To further assess GRQA versatility, we apply GRQA as a plug-and-play training strategy to two state-of- the-art DGSS methods, REIN and SoMA, under the Real setting. As shown in Table GRQA en- hances their average performance by +1.2 and +0.6, re-
Trained on GTAV
Reward
Citys
BDD
Map
Avg
w/o reward
63.6
$7.7
65.6
62.3
w/ DINO-RI
64.5
58.2
66.1
62.9
w/ GRQA
66.1
59.0
67.1
64.1
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Published as a conference paper at ICLR 2026
Input
PIDNet
GCNet
SCTNet
EDMT
Ours
Cityccap
Figure 3: Exemplar segmentation results Giyy BDD, and Map. Compared with real- time baselines, including PIDNet, GCNet, SCTNet, and EoMT, our method delivers noticeably more accurate pixel-wise predictions, highlighting stronger cross-domain generalization ability.
150
Figure 4: Query-protee prototype correlations the Base (SFT) and GRQA models, showing GRQA enhances query- fusion. For clarity, S R Cis split into five blocks.
spectively, without introducing any inference-time overhead. These results indicate that GRQA is a general and effective enhancement for DGSS frameworks, extending its benefits beyond our own model.
4.4 ABLATION STUDIES
Ablation Study Performance and Effi- ciency Trade-offs. We perform an ablation study starting with Mask2Former and pro- gressively removing components, evaluated on GTAS Cityscapes. In Table Fask2Former performs well but has low FPS (11). Remov- ing Pixel Decoder boosts FPS but slightly re-
Table 7: Ablation for Performance and Efficiency. Evaluated on setting GTAV to Citys
Method
mloU FPS Param(Infer)
Mask2Former
63.7
11
325 M
Pixel Dec
62.9
25
320 M
w/o muti-scale
62.8
27
320 M
duces performance. Removing multi-scale has minimal impact on performance and increas- ing speed. Replacing the Transformer Decoder with an MLP-Head drops performance but greatly improves FPS. QPrompt restores performance, maintains high FPS, offering performance- efficiency balance. These results show heavy decoders are bottleneck, and QPrompt boosts effi- ciency without compromising performance.
w/o Transformer Dec
61.3
55
QPrompt
63.6
54
315 M
QPrompt-RI
66.1
54
315 M
Variants of QPrompt-R1. We conduct ablation studies under the G7AV-GGiy++BDD+Mar setting. As shown in Table 4 the baseline (MLP-Head) achieves an aver- age score of 59.9, Introducing QPrompt raises the perfor- mance to 62.3, yielding clear gain of +2.4 and confirming the advantage of our query prompting design over simple MLP head. Building upon this, image alignment brings modest improvement +0.3. The reward mechanism pro- vides larger boost of +1.1, underscoring its effectiveness in guiding query optimization. Finally, adding KL diver- gence not only stabilizes training but also brings further gains, achieving the best performance 64.1. These re- sults highlight the cumulative benefits of GRQA, where
Table 8: Performance of QPrompt-1 on different VFM backbones.
Backbone
Method
Cirys
A
MLP--eaa
61.3
DINOv2-L
+Qprompt
63.6
+2.3
+GRQA
66.1 +2.5
MI.P-Head
50.4
CLIP-L
+Qprompt
51.7 +1.3
+GRQA
53.2 +1.5
MLP-Head
54.7
SAM-H
+Qprompt
55.8 +1.1
+GRQA
57.2 +1.7

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Published as a conference paper at ICLR 2026
Table Performance of GRQA transferred to SOTA DGSS.
Table 4: Roles of individual Table Hyperparameter abla- tion for GRQA.
parts in QPrompt-R1.
Trained on GTAV
Method
Trained on GTAV
Method
Citys
BDD
Map
Avg
B
Citys
Any
App
Citys
Citys
BDD
Map
Avg
MePd
61.3
55.7
62.7
59.9
0.05
0.001
65.6
1
64.0
REIN
60.4
QPrompe
63.6
57.7
+2.4
0.1
0.001
66.1
5
64.5
+GRQA
67.4
61.0
68.1
65.5
+1.
+img align
63.9
58.2
65.8
62.6
40.3
0.15
0.001
65.7
SoMA
71.8
61.3
71.6
68.2
+Reward
65.8
58.6
66.8
63.7
+1.1
0.1
0.01
65.5
10
66.1
+ORQA
72.0
62.5
7188
68.8
+0.6
+KI.
66.1
59.0
67.1
64.1
+0.4
0.1
0.0001
65.9
10
65.8
each component contributes positively, and the full configuration delivers the strongest and most stable generalization.
Hyperparameter Ablation for GRQA. We conduct an ablation study analyze the impact of hy- perparameters on GRQA, as shown in Table 55] On the Citys dataset, the optimal configuration = 0.1 and 3 0.001, yielding 66.1 mIoU. The results show that has minimal impact, while 3 strongly affects performance, highlighting the importance of KL divergence regularization for stabi- lizing training and ensuring robust query alignment. Additionally, the best performance is achieved with Amng = 10 and Agaqu = Increasing Aimg consistently improves performance, confirming its key role in stabilizing training. In contrast, Agmpa requires careful tuning, extreme values cause degradation. This indicates that GRQA is more sensitive to Agrap than Ximg. underscoring the need for balanced integration of the two loss terms.
Ablation for different rewards. To further assess the impact of different rewards on our method, we also validated the DINO-RI (Pan al. (2255) reward formulation on QPrompt. Since DINO-RI is designed for object detection, we made necessary adaptations to the method while maintaining the core reward structure as outlined in the original work. Teble show that applying DINO-RI's reward in QPrompt does lead to some improvements. Our proposed GRQA reward still outperforms DINO-RI terms of performance.
Qprompt-R1 on various VFMs. To investigate whether our method generalizes across different architectures, we evaluate both Qprompt and GRQA under the GTAV-+Citys setting on diverse backbones, including DINOv2-L, CLIP-L.(Radfor et al. [021]. and SAM-HKirlllov et al. (2023). As shown in Table 8. both variants consistently improve over the standard MLP-Head baseline. Specifically, Qprompt yields steady gains of +2.3, +1.3, and +1.1, while GRQA further enhances performance with additional improvements of +2.5, +1.5, and +1.7. These results highlight that our approach strengthens DGSS models from both architectural and training perspectives, delivering robust generalization across heterogeneous backbones.
Parameter, Performance and Speed. We demonstrate the efficiency and speed benefits of our solution in Ta- b QPrompt achieves a balance between domain gen- cralization, inference speed, and parameter efficiency, It reduces parameters and improves speed while maintain- ing competitive performance compared to models like Mask2Former, REIN, and FADA. For QPrompt-R1, de- spite requiring an additional reference model during train- ing, its inference-time parameters remain the same as QPrompt (315M), ensuring efficient speed and count.
Table 9: Performance of QPrompt-RI on different VFM backbones.
Method
mIoU
FPS
Total Param
NixI-ViT Mask2Former
50.1
57
62M
63.7
11
325M
REIN
66.4
10
328M
FADA
68.2
8
338M
QPrompt
63.6
54
315M
QPrompt-RI
66.1
54
315M
CONCLUSION
We introduced QPrompt-R1, method that simultaneously achieves real-time efficiency and ro- bustness to distribution shifts. Through QPrompt, we inject learnable queries only at the final transformer block, enabling efficient query-image alignment with minimal overhead. Furthermore, Query Alignment (GRQA) enhances cross-domain robustness via cooperative our iopervision-addieee no inference cost and integrating seamlessly with existing DGSS mod- performance, establishing
query els. QPrompt-RI achieves 54 FPS while maintaining strong speed-accuracy frontier for semantic segmentation in autonomous driving and robotics.
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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work is supported in part by the National Natural Science Foundation China (62192783, 62276128, 62406140), Young Elite Scientists Sponsorship Program by China Association for Science and Technology (20230NRC01), the Key Research and Development Program of Jiangsu Province under Grant (BE2023019) and Jiangsu Natural Science Foundation under Grant (BK20221441, BK20241200), The authors would like to thank Huawei Ascend Cloud Ecological Development Project for the support of Ascend 910 processors.
ETHICS STATEMENT
All authors have read and agree to abide by the ICLR Code Ethics. This work does not involve interventions with human participants or personally identifiable information. We use only publicly available datasets under their original licenses and follow the terms of use. Potential risks and our mitigations are summarized below:
Privacy Security. We do not collect or release any personal data. When showing quali- from public datasets; any sensitive content is filtered. Bias & Fairness. We report results on multiple benchmarks and provide detailed settings to facilitate external auditing. We acknowledge possible dataset biases and encourage follow- up evaluation broader demographics and domains.
tative examples, all images/videos
Dual Use Misuse. The method could be misused to enable undesired large-scale labeling surveillance. To reduce misuse, we release only research artifacts (code/configs) and exclude any tools for scraping or re-identifying individuals.
Legal Compliance. We comply with licenses of all third-party assets (code, models, and datasets) and cite their sources. Any additional third-party terms respected.
Research Integrity. We document preprocessing, training recipes, and evaluation proto- cols; random seeds and hyperparameters are provided to enable reproducibility.
Where applicable, institutional review information is withheld for double-blind review and can be provided after acceptance.
REPRODUCIBILITY STATEMENT
We include training and evaluation details in the main paper and Appendix. Concretely: (i) all hyperparameters, optimization settings, and compute budgets; (ii) full data preprocessing and splits; (iii) code structure with scripts to reproduce the main tables and figures; (iv) checkpoints and logs for the primary models.
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A
THE USE OF LLMS
We used ChatGPT-44 to polish our manuscript, using the following prompt:
I want you to act as expert in scientiric writing. I will provide you with some paragraphs in English and your task is to improve the spelling, grammar, clarity, conci seness, and overall readability of the text provided, while breaking down long sentences, reducing repetition and increasing logic. You should use artificial intelligence tools, such natural Language processing, rhetorical kncwledge, and your expertise in effective scientific writing techniques to reply. Provide the output as table in a readable mede. The first column is the original sentence, the second column is the sentence after editing, and the third column provides explanation of your edits and reasons. Please edit the following text in scienti fic tone:
B APPENDIX
B.1 ABLATION FOR BOUNDARY ACCURACY
To evaluate boundary prediction accuracy, we tested our model under the GTA5 - Cityscapes set- ting. We compared QPrompt-RI with the RTSS methods PIDNet, ViT, and the DGSS methods Mask2Former and REIN. The 1px B-mloU and 3px B-mloU metrics measure boundary mloU at and pixels from the ground-truth boundaries, respectively. As shown in Table TO our model performs similarly to Mask2Former and REIN, but without multi-scale features pixel encoders, instead using a lightweight transposed-conv upsampler. This results in speed improvement with higher FPS. These demonstrate that our approach effectively balances efficiency and boundary curacy, with no substantial loss segmentation quality.
Table 10: Performance about Boundary accuracy.
Method
1px B-mloU
3px B-mloU
mloU
FPS
PIDNet
25.4
29.3
45.7
46
Next-Vit VIT
17.1
31.6
50.1
57
Mask2Former
43.5
45.8
63.7
11
REIN
44.1
46.5
66.4
10
QPrompt-Ri
42.7
45.3
66.1
B.2 GRQA APPLY TO GENERAL SEGMENTATION
Since the GRQA algorithm leverages mutual supervision between queries for optimization, it can be applied to improve query-based models for general semantic segmentation. not just domain- generalized task. We conduct additional in-domain testing on the Citys->Citys setting, As shown in Table I1] we found that GRQA still provides performance improvement in general semantic segmentation.
B.3 PERFORMANCE COMPARISON PROMPTED SAM MODELS
We also compared model with prompt-based methods, specifically FastSAM Zhang et al. 2023) and Grounded SeM et al. 2024). Since SAM produces class-agnostic masks, we tested them under the open-vocabulary semantie segmentation setting, where used text prompts that
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Table 11: Performance comparison general semantic segmentation (City)++City))
Method
Citys Citys (mloU)
QPrompt
79.2
+GRQA
80.4
Mask2Former
82.4
+GRQA
83.1
include class labels to make the predicted masks class-specific. The experiments were conducted on Cityscapes, and as shown in Table [2] our model outperforms both FastSAM and Grounded SAM, achieving significantly higher mloU and FPS. Additionally, we observed that the speed bottleneck of FastSAM lies in mask classification, rather than in the "everything" mode used for class-agnostic mask prediction.
Table 12: Performance Comparison with Prompted SAM Models.
Method
mloU (Citys)
FPS
FastSAM
32.6
< 1
Grounded SAM
36.7
< 1
Ours
66.1 1
54
16


Reviews:
[{"review_id": "rZsa7Qkt0D", "reviewer": "Reviewer_Zb67", "summary": "The paper proposes QPrompt-R1, a method addressing the novel and practical problem of Real-Time Domain-Generalized Semantic Segmentation (RT-DGSS). The core idea is to inject a small set of learnable queries into the final layer of a Vision Foundation Model (VFM) backbone (QPrompt) and enhance their training with a novel Group-Relative Query Alignment (GRQA) objective. This approach aims to retain the domain generalization benefits of query-based methods while achieving real-time speeds by avoiding heavy decoder stacks. The results are impressive, showing a strong balance of speed and robust cross-domain performance.", "questions": "Baseline Comparison: Could you include a comparison with a prompted version of SAM or other fast, promptable foundation models to better contextualize the performance of your QPrompt architecture?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Generalizability of GRQA: The demonstration that GRQA is a \"plug-and-play\" module that can be added to other SOTA DGSS methods to improve their performance is a major strength.\n2. Motivation: The paper is well-motivated, identifying a critical and practical problem.", "weaknesses": "1. Overclaiming of the \"First\" RT-DGSS Problem: While this paper is the first to formally name and define the problem, the concept of achieving real-time and robust segmentation is an implicit goal in the field. RTSS papers also evaluate cross-domain performance and DG papers evaluate the computational efficiency, even if it's not their primary focus.\n2. Novelty of QPrompt: The idea of injecting tokens/queries is not new. The prototype bank, which is claimed to be a key component of the GRQA strategy, has already been widely applied. The specific application to create a lightweight, single-layer \"prompting\" mechanism for segmentation might be novel in this context, but the framing should be more precise to acknowledge the inspiration.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jRcj5rcebm", "reviewer": "Reviewer_9SAk", "summary": "This paper investigates the problem of real-time domain-generalizing semantic segmentation. The authors propose to depart from standard, heavy decoders which are used for this dense classification task on top of vision foundation model encoders, and replace these decoders with a single, light-weight and efficient transformer decoder layer. To enrich the mask-level queries which are involved in this decoder layer, they propose a novel group-relative query alignment module which produces a reinforcement-learning-type reward, optimized in a standard supervised fashion. The authors validate their model on several central domain generalization benchmarks, achieving very high efficiency while staying competitive in terms of segmentation performance to state-of-the-art generalizable segmentation methods.", "questions": "1. Can the authors provide an \"end-to-end\" ablation starting from a baseline, heavy network with pixel and transformer decoder heads on top of the VFM backbone, and then first remove these heads and subsequently add the proposed components one by one, finally arriving at the fully-fledged proposed network? Such an ablation should include both performance figures and efficiency figures, importantly latency and amount of computation, and potentially also parameter count and total space complexity at inference. This would greatly help in supporting the motivation that efficiency can be substantially improved by removing the decoder heads.\n\n2. Can the authors provide clarifications on the GRQA part of their method? (cf. Weaknesses)\n\n3. Can the authors provide a comparison in terms of boundary accuracy of the competing segmentation methods against their method? This would help to understand whether the proposed efficiency improvement preserves fine-grained details in the segmentation output despite working with lower-resolution features.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The examined problem, albeit constituting a simple combination of two existing ones - i.e. real-time semantic segmentation and domain-generalizing semantic segmentation, is underexplored and yet very relevant for practical real-world applications of Computer Vision, including safety-critical settings that involve uncontrolled and unconstrained operational design domains and at the same time fast inference, such as autonomous vehicles and field robotics.\n\n2. The idea to depart from complex, MaskFormer-style decoders which are the current de-facto standard for semantic segmentation and to leverage the features which are already extracted by Vision Foundation Models in their image tokens is interesting and promising, even though the authors have not provided experimental evidence that the bottleneck of recent segmentation models in terms of latency are indeed these decoders.\n\n3. The proposed Group-Relative Query Alignment is a novel technique in the context of semantic segmentation, with a strong and rigorous theoretical formulation and a demonstrated clear performance benefit for diverse VFM-based segmentation networks (cf. Table 6). This novel module is relevant both in the context of real-time semantic segmentation and beyond that, as it delivers performance gains for heavier, more highly performant yet less efficient networks for domain-generalizing semantic segmentation (cf. Table 2).", "weaknesses": "1. Missing evidence for motivation on efficiency-level design decisions. The authors state in the very beginning of the paper in the Abstract that \"the bottleneck in Domain Generalization is the prediction head, not the backbone\". However, this statement is not supported by experimental evidence, e.g. by benchmarking a baseline network of similar architecture to the one proposed which however does include standard pixel and transformer decoders - a la Mask(2)Former - in terms of inference computation in order to break down the inference latency / FLOPS into backbone-level and head-/decoder-level portions. Combined with the fact that in the main experimental comparison (Table 1) the proposed model trails the performance of state-of-the-art, heavier methods, most of which feature the above decoders, the precise quality of the tradeoff between performance and efficiency is not clear.\n\n2. Omission of learned upsampling to handle fine-grained details in segmentations. The utilized VFMs, such as ViT, rely on patchification to tokenize the input image, which heavily reduces the resolution of their output image-token-based features. Yet, the proposed method operates directly on these heavily downsampled features to produce the final segmentation map. That is, the method does not involve any learned upsampling, exactly of the type that is performed in the pixel decoder module of standard mask-based segmentation architectures such as Mask(2)Former which is omitted in the presented architecture. Thus, the question arises on whether the resulting predictions are deteriorated in terms of preservation of fine-grained details in the ground-truth semantic boundaries.\n\n3. Issues with the presentation of the method. The overall presentation of the method is of fair quality, however, there are a few unclear points. The symbol $K$ is never properly defined and it is not clear whether it denotes the number of semantic classes in the set, as is usually the case in the notation of semantic segmentation papers. This potential relation is reinforced by the fact that in Fig. 4, the number of queries $K$ is 20, which differs from the number of class prototypes (19) only by 1. In Eq. (7) and (8), the parameter $\\epsilon$ appears twice; only a single addition of a positive constant is nonetheless required to avoid an ill-defined zero-valued denominator in (8). The core motivation behind the full GRQA objective introduced in p. 6 is not straightforward to me. Some background on this type of reinforcement learning could be useful for readers who are not familiar with the topic. In this context, L. 268-271 are unstructured and hard to parse.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7HQTbmABAD", "reviewer": "Reviewer_fRjv", "summary": "This work proposes QPrompt-R1, a method specifically designed for RT-DGSS (real-time domain generalized semantic segmentation). QPrompt-R1 consists of two components: the QPrompt architecture that ensures real-time performance and GRQA that enhances domain generalization. Based on the VFM backbone, QPrompt injects learnable queries into the last Transformer block to replace the traditional complex decoder. GRQA addresses the insufficient robustness of single-layer query-image interaction. Experiments demonstrate notable performance improvement.", "questions": "- I am curious about whether GRQA is capable of improving the performance of general semantic segmentation. Discussion or even a small experimental analysis would furthur improve this work.\n- As the proposed method uses prototye and prototye bank, it is suggested to cite CPT [r1] where category prototype and memory bank are also adopted.\n\n[r1] Quan Tang, et al. Rethinking Feature Reconstruction via Category Prototype in Semantic Segmentation,TIP 2025.\n\nI am more familiar with general semantic segmentation techniques, and follow only mainstream ones in domain transfer. Feel free to correct if there is any misunderstanding.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- It effectively applies GPRO to segmentation tasks, enhancing segmentation performance without introducing additional inference overhead.\n- GRQA can be effectively integrated into current domain generalization semantic segmentation methods to further improve their performance.\n- QPrompt-R1 strikes a good balance between domain generalization capability and inference speed.", "weaknesses": "- Q-Prompt is similar to EoMT. They both insert queries into the final layer, but the authors discuss no differences.\n- In the Evaluation Setting of Section 4.1, the Real→Real scenario should exclude ACDC’s adverse-condition splits.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FLN0mvqXw1", "reviewer": "Reviewer_dtcA", "summary": "This work proposed a unified framework to address real-time segmentation and domain generalized segmentation, which is relevant to real-world applications. The proposed is a plug-and-play solution, which can benefit existing methods without introducing latency overhead. Experiments and ablation studies demonstrate the effectiveness of the proposed method.", "questions": "- Would you consider making the source code publicly available to foster future research in this line?\n- As this work claimed to address real-time efficiency and domain generalization in a single framework, would you better justify the benefit of your solution for reducing the parameters of the model and improving the inference speed? This is critical and should be discussed in detail.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- This paper provided insights to address real-time segmentation and domain-generalized segmentation in a single framework.\n- The proposed GRQA is a plug-and-play module, which can enhance existing real-time segmentation models.", "weaknesses": "- As the proposed GRQA is a plug-and-play module, it is also necessary to evaluate atop high-quality, non-real-time segmentation models to verify whether it is consistently beneficial.\n- The related work only reviewed CNN-based real-time segmentation methods. More recent state-of-the-art real-time methods like SeaFormer, SeaFormer++ RTFormer, and Next-ViT should be reviewed and compared in the experiments. Moreover, the domain generalization performance under different corruptions should be evaluated on Cityscapes-C and relevant benchmarks and compared against existing methods like SegFormer, FAN, Robustifying Token Attention for Vision Transformers, and Trans4Trans.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)