Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation
Authors: Yujia Cai
Boxuan Li
Chenghao Xu
Jiexi Yan
Venue: ICLR 2026 Poster (2026)
Keywords: Image Retrieval
Vision–Language Representation Models
Hypernetworks
Style Adaptation
Contrastive Learning
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Hystar dynamically adapts model weights per query style and uses StyleNCE to enable efficient, stable, and robust multi-style image retrieval.

Abstract: Query-based image retrieval (QBIR) requires retrieving relevant images given diverse and often stylistically heterogeneous queries, such as sketches, artworks, or low-resolution previews. While large-scale vision--language representation models (VLRMs) like CLIP offer strong zero-shot retrieval performance, they struggle with distribution shifts caused by unseen query styles. In this paper, we propose the Hypernetwork-driven Style-adaptive Retrieval (Hystar), a lightweight framework that dynamically adapts model weights to each query’s style. Hystar employs a hypernetwork to generate singular-value perturbations ($\Delta S$) for attention layers, enabling flexible per-input adaptation, while static singular-value offsets on MLP layers ensure cross-style stability. To better handle semantic confusions across styles, we design StyleNCE as part of Hystar, an optimal-transport-weighted contrastive loss that emphasizes hard cross-style negatives. Extensive experiments on multi-style retrieval and cross-style classification benchmarks demonstrate that Hystar consistently outperforms strong baselines, achieving state-of-the-art performance while being parameter-efficient and stable across styles.

Published as a conference paper at ICLR 2026
BYSTAR.HYVERNNTWORK--RVVZZ AAAA RETRIEVAL VIA DYNAMIC SVD MODULATION
Yujia Cail: Boxuan Li Chenghao Xu² Jiexi Yan'
School of Computer Science and Technology. Xidian University, Xi'an. Shaanxi, China School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, China (yujiacai, boxuan.i, chx xidian. edu. en, jxyan 995 @gmailmmm...... com
ABSTRACT
Query-based image retrieval (QBIR) requires retrieving relevant images given di- and often stylistically heterogeneous queries, such sketches, artworks, low-resolution previews. While large-scale vision-language representation mod- els (VLRMs) like CLIP offer strong zero-shot retrieval performance, they struggle with distribution shifts caused by unseen query styles. In this paper, we propose the Hypereetwork-drive Style-adaptive Retrieval (Hystar), a lightweight frame- work that dynamically adapts model weights to each query's 's style. Hystar em- ploys hypernetwork to generate singular-vale value perturbations (AS5) for attention layers, enabling flexible per-input adaptation, while static singular-value offsets on MLP layers ensure cross-style stability. To better handle semantic confu- sions across styles, we design StyleNCE as part of Hystar, an opainal-transport- weighted contrastive loss that emphasises hard cross-style negatives. Exten- sive experiments multi-style retrieval and cross-style classification bench- marks demonstrate that Hystar consistently outperforms strong baselines, achiev- ing state-of-the-art performance while being parameter-efficient and stable across styles.
INTRODUCTION
Query-based image retrieval (QBIR) (Thomee & Lew 2012 is core mechanism for accessing visual information at scale. Given query, the system must rapidly return images aligned with the user's intent from large database (Li et al. 2022a 2023), enabling modern image search and many downstream applications (Isinkaye et al. 2015). In practice, user queries are highly diverse and personalized, especially in style (Li et al. 2021b Johnson et al. 2015). Expressing intent appro- priately and making retrieval models adapt flexibly to heterogeneous query styles remain central challenges for QBIR. Prior works have sought address this through style-invariant representa- tions (e.g., Dwme SE Network; Semi3-Net (Lu et al., 2021 Lei et al. 20201), cross-modal alignment and domain adaptation (e.g., BDAA-SetRRe;; Adapt and Align; Domain-Smoothing Net- work (Chaudhuri et al. 2022 Dong et al. 2023 Wang et al., (021)). and cross-category general- ization (c.g., Generalising Fine- Grnener SBIR (Pang et al.j (0191). However, these methods are typically designed for style-specific datasets, limiting their scalability.
Furthermore, recent advancements in large-scale vision- vision-language representation models (VLRMs), such as CLIP (Radford et al.) 2021 Li et al. (202b) 2021a). have demonstrated significant capabil- ities in discrimination and generalization abilities, enabling QBIR through the incorporation of rich semantic priors acquired during pretraining. Nevertheless, performance tends to deteriorate when the query style significantly diverges from those encountered during pretraining (c.g., sketch, car- toon, or artwork), primarily due to the distributional mismatch in the shared embedding space (Sain) ct al. 2023 et al. 2024a). A direct remedy is fine-tuning VLRMs on target retrieval datasets. For large models, full fine- tne-ttnnig is expensive and prone catastrophic forgetting (Laurier Kemker et al. 2018). Parameter-efficiem fine- mee-tunin (PEFT) methods, such as LoRA (Hu et al. (002) and
"Equal contribution
1 "orresponning author

---

Published as a conference paper at ICLR 2026
VPT (Zhou al. 2022c Jia et al., 2022 Zhou et al. 2022b), freeze the backbone while introduc- ing a small number of trainable parameters. Despite their efficiency, existing PEFT approaches are static (Chavan al. 2023), relying single parameter set shared by all inputs, limiting adaptation
to diverse, unseen styles at inference (Dong et al. 2023
Li et al.
2024a).
To tackle this issue, some methods (Li
2024b Ge al.
2025
Yu et al. 2024 Yan et al. 2024)
consider pretraining separate style-specific units and selecting or composing them at test time. For instance, VB-LoRA (Li al. 2024b) utilizes vectorized LoRA bank select the top-k modules based on the input. However, such approaches inevitably entail extensive annotation and a large library of style units, struggling with unseen or cross-style scenarios. Recently, line of work ex- emplified by FreestyleRet (Li et al.) [024a]] Tang et al. (2025) leverages style cluster priors extracted from training data to guide prompt learning(Jia et al. 2022 Zhou et al. 2022c b). achieving partial style adaptation. Though these methods have shown some effectiveness, they are inherently lim- ited by the styles observed during training and often underperform on out-of-distribution samples. Consequently, developing methods for flexible, data-driven adaptation to unseen styles remains
significant challenge (Wang et al. 2022 Zhou et al. 2022a et al. 2025 Dong et al. 2025).
In this paper, we introduce the Hypernetwork-driven Style-adaptive Retrieval method, Hystar, framework for flexible and generalized QBIR that dynamically adapts to diverse styles of different queries, including previously unseen ones. Specifically, Hystar extracts query-specifie style repre- sentations and employs a lightweight modulation mechanism, where a hypernetwork-driive generates iisaance-ooniiioone updates for attention layers. Rather than predicting full low-rank ma- trices, Hystar predicts only singular-valus perturbations (AS), which reduces prediction difficulty and improves training stability. In parallel, static learnable singular-value offsets on MLP layers provide stable cross-style calibration. This combination of dynamic adaptation and static robustness enables flexible yet stable style-specific retrieval.
module
Moreover, we introduce StyleNCE, an optimal-transport transport-weightee contrastive criterion that per- forms importance-weighter global matching over positives and negatives, better modeling semantic confusions in cross-style retrieval (Robinson et al. 2020) Jiang et al.) 2023). Overall, Hystar cou- ples an adaptable architecture with a difficulty-aware objective, emphasizing cross-style semantic alignment while remaining lightweight and stable.
Our contributions are summarized as follows:
Hypernetwork-driven dynamic PEFT. We introduce a hypernetwork that generates input- conditioned SVD modulations, overcoming the rigidity of static PEFT. It enables fine-grained, per-input adaptation on attention layers while relying on stable, precomputed offsets for MLPs, thereby achieving both flexibility in handling diverse inputs and stability during optimization.
Style-adaptive contrastive learning. We propose StyleNCE, OT-weighted contrastive loss that adaptively emphasizes hard cross-style negatives, leading to more robust retrieval under dis- tribution shift.
Strong empirical results. Extensive experiments on multi-style retrieval and cross-style classifi- cation benchmarks demonstrate the effectiveness of Hystar in dynamic PEFT for style adaptation and multimodal generalization, consistently outperforming strong baselines.
2 RELATED WORK
Query-based Image Retrieval. Query-based image retrieval (QBIR) has long been a central topic in computer vision, with early surveys highlighting its importance for accessing large-scale visual data (Thomee & Lew] 2012). Traditional methods often relied on handcrafted features or shallow models (Li & Li. 2018 Kumar Verma et al.) 2019), but recent advances in vision-language repre- sentation models (VLRMs), such as CLIP (Radford et al. 2021), have enabled powerful zero-shot retrieval by aligning images and text in shared semantic space. Nevertheless, these models are sen- sitive to style variations (e.g., sketches, artworks, low-resolution previews), leading to degraded performance under distribution shifts (Li al. 2024a Qiu et al. 2022).
Parameter-Efficiens Tine-Tunigg (PEFT), To mitigate the cost of adapting large-scale VLRMs,
parameter-efficient fine-tuning (PEFT) methods have been proposed. LoRA (Hu et al. 2022 and
2

---

Published as a conference paper at ICLR 2026
VPT (Jia et al. 2022) introduce small number of trainable parameters while freezing the backbone, achieving efficient adaptation. However, these methods are inherently static, applying the same parameter mapping all inputs, which limits their ability to generalize to unseen query styles (Ha et al. 2016). More recent approaches, such as FreestyleRet (Li et al. 2024a), leverage style-cluster priors to guide prompt learning and achieve partial style adaptation. Yet, their reliance observed style clusters hampers performance in truly out-of-distribution scenarios.
Dynamic Modulation and Hypernetworks. An alternative line of work explores dynamic adap- lation. Some studies pretrain multiple style-specific modules and perform selection at inference (Li ct al. 2024b), but this approach requires large annotated style librarics. Others attempt dynamic module composition (Gu et al. 2024), though they constrained by fixed bank of predefined modules. Hypernetworks (Ha et al. 2016) offer a promising alternative by generating instance- conditioned parameters, reducing the dependence on explicit style clusters. These insights motivate our design of dynamic-static hybrid framework that leverages hypernetwork-driver modulation for flexible yet robust multi-style retrieval.
Contrastive Learning under Distribution Shift. Contrastive learning (Hadsell et al. 2006 Oord jet al. 2018 He et al., [020)] Chen et al. 2020 has been a cornertoone multimodal retrieval. Stan- dard objectives such as InfoNCE (Oord et al. 2018) treat all negatives equally, which is suboptimal
in cross-style scenarios where distinguishing between easy and hard negatives is crucial. Our pro- posed StyleNCE introduces an optimal-transpe transport (Peyré al., 2019 Ren et al. 2025b You al., Zhang et al. 2025) formulation to explicitly reweight negatives by difficulty (Robinson et al.
2025
2020
Jiang
2023). enhancing robustness against semantic confusion across styles.
METHOD
In this section, we first introduce the problem setup of QBIR under diverse style variations in Sec. 3.1 and present the overall pipeline (Fig. I. The core idea is to enhance the adaptability of sin- gular value modulation through hypernetwork-ddrienn dynamic parameterization. We then detail our hybrid dynamic PEFT mechanism with static singular value modulation in Sec. S.2.3.2 Sec. 3.3 further describes our weighttt StyleNCE loss, which leverages optimal transport to enable flexible and efficient optimization across heterogeneous input styles. Finally, See. [3.4] introduces the training and inference procedure of our framework. Our framework is conceptually related to VI.RM-based
retrieval (Radford et al., 2021 Li et al. 2022b 202 cross-style scenarios.
Sain et al. 2023 but specifically targets
3.1 PROBLEM DEFINITION AND PIPELINE
We study QBIR in the presence of significant style variations, including sketches, paintings, low- resolution images, and text queries. In this setting, queries may originate from styles different from the target gallery (e.g., natural photos vs. sketches or artistic renderings). The goal is to retrieve semantically aligned images despite style discrepancies. Formally, given a query g (possibly from a disjoint style) and candidate set } we aim to learn an embedding function fo(-) such that positive pairs (g. px are closer in the embedding space than negatives (q. pT).
Multi-style QBIR poses two main challenges:
1. Cross-style discrepancies. Distinct styles differ substantially in appearance and statistics, hin-
dering pretrained models (Radford et al.) 2021 Sain al. 2023 Zhou et al.) 2022a
2. Static PEFT limitations. Methods such VPT (Jia
al. 2022 Zhou al. 2022b and
LoRA (Hu et al. 2022) excel on seen styles but fail to generalize to unseen styles.
To overcome these challenges, our dynamic PEFT framework leverages the pretrained DINOv2 backbone (Oquab et al.) 2023) to extract style-aware features A lightweight hypernetork maps z to singular value increments AS for attention layers, while static singular value modulation is applied to MLP layers, enabling style-adaptive yet stable transfommations. Training is guided by the we-ghttt StyleNCE loss, which emphasizes hard negatives while maintaining efficient opti- mization. As shown in Fig. i the query is initially routed through style extraction branch, and the
3

---

Published as a conference paper at ICLR 2026
Art
Image
Tuning
Frozen
Malti- Head
Norm
Norm
xN
CLS
Sketth
t7
1.5
S
V
S
Retrieval
DINOv2
Hypernetwork
SVD-Modulation
Figure 1: Overview of the Hystar framework. For multi-style queries, style features are first extracted using DINOv2. These features are fed into a hypernetwork to produce dynamic singular- value increments for attention layers, enabling style-conditioned modulation of the feature encoder. Additionally, static singular-value increments are applied to the MLP layers, serving as a fixed parameter modulation. Together, these mechanisms guide the encoder to produce style-diverse trieval predictions.
extracted style is injected into the backbone via Hyper-SVD Modulation. Thereafter, the adapted backbone encodes the query, and the encoded feature is optimized under StyleNCE supervision. This design achieves a balance between computational efficiency and cross-style generalization, significantly boosting retrieval performance under heterogeneous styles.
3.2 DYNAMIC-STATIC HYBRID PARAMETER-EFFICHE FINE-TUNING
sntamic-statie hybrid PEFT method tailored for multi-style adaptation. Let Wo Rdi xd2 denote a pretrained VLRM weight matrix (e.g., attention projections or MLP weights). Our goal is to preserve the spectral structure of Wo while introducing structured, singular value-based updates AW (Lingam et al.) 2024 Wang et al. (025).
We propose
To achieve this, we decompose Wo E [R x as Wo = T.VT where U E RRdi and E Rdaxdd are orthogonal matrices and Σ E Rd (ddixaa x is diagonal. Rather than learning full-rank update, we modulate singular values such that W = Wo + AW = U[E + AT)VT, with U, V, and E frozen and only diagonal increments AE learned. Using the diagonalization operator sp()) this can be written as W = Up(s + V where 8 denotes the original singular values and As their learnable increments.
This preserves pretrained spectra while enabling efficient, stable adaptation. (A formal justifica- tion of the stability of this modulation, based on spectral norm theory, is provided Appendix (A] Beyond providing stable updates, SVD modulation also offers a geometric advantage for style adap- tation. In this formulation, U and V define the semantic subspace of the pretrained model, while Σ determines the scaling along these spectral directions. By modulating only the singular values while keeping and V fixed, the model adapts through smooth, weight manifold. The style-dependent increments As drive this process, effectively rescaling the intrinsic spectral directions according the current style. This mechanism aligns the model's repre- sentation geometry with style-induced variations, enabling flexible yet stable cross-style adaptation.
deformations in the
Compared with conventional low-rank updates such as LoRA, SVD modulation constrains the up- date directions within the pretrained spectral subspace. While LoRA introduces new rank compo- nents that may interfere with the pretrained semantics, our method only rescales the principal spec- tral directions, preserving the semantic geometry while adapting their strength to the target style. This spectral alignment enables style-specific ficxibility without disrupting the underlying repre- sentational structure. Although LoRA introduces no decomposition stage, SVD modulation only
4

---

Published as a conference paper at ICLR 2026
incurs one-time cost during model initialization for the SVD factorization, after which U, V can be cached and reused. Moreover, the static modulated weights can be merged into the base model for inference, ensuring negligible runtime overhead. In return, the spectral regularity and geometry- preserving adaptation of SVD bring clear advantages in stability and cross-style generalization.
We decompose As into dynamic and static components:
As
Addyn + A Satat-
(I)
The static increment Asma is globally shared across styles. Initialized at zero to preserve pretrained weights. evolves during training to capture stable cross-style corrections, ensuring robustness across heterogeneous inputs. A detailed analysis of this dynamic-statie decoupling design is pro- vided Appendix The dynamic incremen Sayn is style-dependent and generated by hypemett- work conditioned on the style embedding 2 E Rd extracted from the [CLS] token the DINOv? encoder output, applied to attention layers for cross-style flexibility. Appendix D.3 provides an ablation study on the choice of style extractors. Specifically, the hypernetwork H [2; produces
Asayn H((z; w! o (W(()) +(()) +62 (H)
where & denotes the learnable parameters W ( W D D and (.)) nonlinearity (ReLU//ELLU) (Hendrycks & Gimpell 2023) Nair& & Hinton 2010). The hypernetwork takes the d-dimensional style embedding as imput and outputs vector of dimension sponding to the number of singular values modulated in each attention projection. Unless otherwise specified, the hidden layer width is set to 2r. This decomposition allows Asdyn to adaptively capture cross-style discrepancies, providing local flexibility (style adaptation). while Assut ensures global robustness (task consistency).
maindd,d, corre-
By jointly incorporating style-dependent and style-independent updates in the spectral domain, our approach achieves parameter efficiency while balancing adaptability and robustness, providing principled new perspective for multi-style image retrieval (Li et al. 2024a Sain et al.) 2023).
3.3 STYLENCE: OT-WEIGHTED CONTRASTIVE LOSS
Positives
Hard Negatives (Important)
Easy (ega)))es(Many)
pos
dhard
deary
Rhard
Focused Set
Reary
Raor
Reasy Rhard
deasy x dhard
Tgnored Set
X
StyleNCE
Style Queries
Figure 2: Motivation behind StyleNCE. In multi-style queries, most negatives lie far from the query and provide little training signal, whereas hard negatives, though fewer, may be closer the query than positives due to style-induced abstraction. StyleNCE focuses on these hard negatives, preventing gradients from being dominated by easy samples and ensuring effective optimization for style-diverse retrieval.
Cross-style retrieval suffers from inherent distributional gaps, sketches emphasize contours, art abstracts geometry, and photos contain complex textures and backgrounds. Standard contrastive losses (e.g., InfoNCE (Oord al. 2018 He al. 2020 Chen et al. 2020 treat all negatives

---

Published as a conference paper at ICLR 2026
equally, failing to distinguish between easy negatives (semantically distant, offering little supervi- sion) and hard negatives (semantically similar yet stylistically different, crucial for generalization). Overemphasis easy negatives accelerates convergence but weakens out-o--distribution robust- ness. We propose to reweight negatives by difficulty, amplifying the contribution hard negatives.
Building the insight, design StyleNCE, which explicitly reweights negatives according to their difficulty. Given N multi-style queries Q = [q)....,9h .4v] and their ground-truth positives p 1=-p,,--. pp,..... PNJ, InfoNCE is:
N exp (sim 4i pt)/T)
CIafONCE = N log
(3)
exp (sim(q:.P))/T)
StyleNCE modifies InfoNCE by introducing difficulty-aware negative weighting 2020 Jiang al. 2023):
(Robinson et al,
exp (.)
LStylNNCE N
log exp (sim pi)/T) + jztt Way exp (sim(qs Pt)/T)
(4)
where 7 balances positives vs. negatives, and wij encodes the difficulty of each negative sample.
To compute these difficulty-aware weights, we employ optimal transport (OT) formulation (Peyr al. 2019 Ren al. 2025a Liu et al. 2025). define a cost matrix
[exp((1 sim(q. P)))/A,, i j.
Cip
(5)
co,
= J.
where À > 0 controls emphasis hard negatives.
We then solve the OT problem
min (C,T), s.t. 11.71 11 11 TERNxN
1
(6)
where the row- and column-wise normalization constraints explicitly ensure
To 1, TTy=1 [1,
We set the aifficulty-aware weights wij Ti and solve this efficiently using Sinkhorn (Cuturi 2013) iterations. These OT-based weights systematically amplify hard negatives while maintain- ing balanced contributions across the batch, allowing StyleNCE to effectively capture cross-style discrepancies.
3.4 TRAINING AND INFERENCE
As shown in Figure I during training, Hystar first feeds the input image into the style-aware module (DINOv2) to obtain a style latent vector z. This vector then passed into the hypernetwork to generate style-specific weight updates for the VLRM attention layers. The updated VLRM encoder processes the input image to produce its embedding. The overall training objective is the proposed StyleNCE loss.
We randomly select a sample from one style in the training style set as the anchor, and use its corresponding ground-truth image the positive. This encourages the model to learn style-invariant representations while exposing it to diverse style variations.
During inference, embeddings obtained in the same manner in training, i.e., by first extract- ing the style vector 2, applying the hypernetwork to adjust the VLRM attention weights, and then encoding the input image.

---

Published as a conference paper at ICLR 2026
4 EXPERIMENT
4.1 EXPERIMENTAL SETTINGS
Implementation Details. For backbone selection, we primarily use CLIP (ViT-L/14) (Radford et al., (021) and BLIP (COCO-pretrained) (Li et al. (022b), while additional experiments are conducted on ALBEF (COCO-pretrained) (Li et al., 202 (see Appendix E.2 to demonstrate generality. All trainable models are trained on a single NVIDIA A6000 GPU. For our model, unless otherwise specified, we
batch size of 48 and train for 35 epochs. We use learning rate of 1 x 10-3 for the static modulation branch and x 10-5 for the dynamic hypernetwork to ensure sta- ble training. An ablation the width and depth of the hypernetwork is provided in Appendix [D.2 Dynamic style modulation is injected into the 4th, 7th, 10th, and 13th layers of the CLIP and BLIP ViT backbones. Details on the style injection positions are provided in Appendix D.I The hyper- parameters of the StyleNCE loss are fixed as 80 and À = 1. The Sinkhorn algorithm is run with a sufficiently large number of iterations (50) to ensure convergence. For additional analysis on the effect of varying these parameters, see Appendix G All inputs follow the default preprocessing pipeline of the selected vision-language representation backbone.
Datasets. To validate the effectiveness of our proposed approach, we conduct systematic exper- iments the DSR (Li al. 2024a) and DomainNet (Peng et al. 2019) datasets. Fine-grained style-level retrieval on DSR serves as our primary benchmark, and we further evaluate on Domain- Net for zero-shot category retrieval and cross-style classification to assess generalization unseen styles.
Baseline Methods. Baselines include CLIP, BLIP, VPT, LoRA, (IA) (Liu et al. 2022). Adapt- Former (Chen et al. 2022). SSF (Lian et. 2023 FreestyleRet- CLII,, FreestyleRet- ILIP as well as recent multimodal models ImngeBind (Girdhar et al. 2023) and LanguageBind (Zhu et al. 2023b). (* represents the prompt-tuning version of the vanilla models. ) Following the FreestyleRet evalu- ation protocol, we pretrain VPT, LoRA, (IA)², AdaptFormer and SSF DSR, while FreestyleRet methods are evaluated using their official pretrained weights. The ImageBind and LanguageBind results are adopted from FreestyleRet and are not included in all subsequent experiments.
4.2 QUANTITATIVE RESULTS
Fine-grained Diverse Style Retrieval (DSR). We first evaluate our approach on the DSR dataset, which contains grained categories across diverse query styles. As shown in Table [ baseline models such as CLIP and BLIP exhibit substantial performance degradation when faced with large style discrepancies. For instance, CLIP only achieves 47.5% Top-1 on Sketch and 45.0% on Low- Resolution queries. Parameter-efffcient tuning methods like LoRA, VPT, (IA)², AdaptFormer and SSF partially mitigate this issue, improving Top-1 performance to above 70% on Sketch and 80% on Now-Resolution. Retrieval-oricee oriented methods such as FreestyleRet-CLIP and FreestyleRet-BLII achieve stronger results, surpassing 80% Top-1 on Sketch and 90% on Low-Resolution.
Our proposed Hystar consistently outperforms all baselines across all styles. For instance, Hystar- BLIP achieves Top-1 accuracies of 75.6% on Art, 91.0% on Sketch, and 98.8% on Resoluuutn, yielding absolute improvements of roughly 21-25% over BLIP in these challenging settings. No- tably, it also brings consistent gains on text queries, despite not being explicitly trained for them, demonstrating robustness to both style variation and language-drive retrieval.
Overall, these results highlight that Hystar effectively bridges severe style gaps and sets a new state- of-the-art on fine-grained diverse style retrieval.
Stoss-style Generalization (DomainNet). We further evaluate our method's generalization to unseen styles via zero-shot retrieval on the DomainNet dataset. As shown in Table Hystar achieves the best zero-shot retrieval performance on the poarse-graines benchmark, surpassing all baselines across both moderate and highly abstract styles, highlighting its strong cross-style generalization.
In contrast, existing approaches exhibit clear limitations in unseen styles. This suggests that the static methods and the style-specific clustering priors leveraged by FreestyleRet may not generalize
7

---

Published as a conference paper at ICLR 2026
Query Style
Art
Sketch
Low-Res
Text
Method
Top-1
Top-5
Top-1
Top-5
Top-1
Top-5
Top-1
Top-5
CLIP
58.5
93.7
47.5
77.3
45.0
75.7
66.1
94.7
CLIP*
58.2
90.4
63.6
93.6
78.8
97.1
72.2
96.4
BLIP*
51.1
85.3
67.1
90.9
77.2
95.8
74.3
95.3
LoRA
63.8
96.5
72.8
96.5
79.7
95.1
70.4
97.1
VPT
66.7
96.5
73.3
97.0
81.4
96.0
69.9
96.1
(IA)*
64.3
96.8
71.8
95.7
80.9
96.1
70.1
96.6
AdaptFormer
65.1
97.0
73.5
96.4
81.1
96.3
69.7
95.8
SSF
64.7
96.4
73.0
97.0
79.9
95.8
70.1
96.3
ImageBind
58.2
86.3
50.8
79.4
79.0
96.7
71.0
95.5
LanguageBind
67.5
92.9
63.6
89.1
78.6
94.5
79.7
98.1
FreestyleRe--CLIP
71.4
97.8
80.6
97.4
86.4
69.9
97.0
FreestyleRet-B...
74.5
97.4
81.2
97.1
90.5
98.5
81.6
99.2
Hystar-CLIP(Ours)
75.2
97.9
90.2
99.3
98.0
99.4
70.9
97.5
Hystar-BIIP(Ours)
98.1
91.0
99.8
98.8
99.6
Table 1: Retrieval performance the style-diverse QBIR task. We evaluate Top-1 and Top-5 accuracy(%) on the DSR fine-grained benchmark. The two forms of our Hystar framework, Hystar- CLIP and Hystar-BLIP, outperform in multiple scenarios with different query styles compared with other baselines. Best results are highlighted in bold.
Query Style
Clipart
Sketch
Painting
Quickdraw
Infograph
Method
Top-1
Top-5
Top-1
Top-5
Top-1
Top-5
Top-1
Top-5
Top-1
Top-5
CLIP
60.9
77.0
49.1
67.0
59.2
75.2
9.1
15.8
41.2
60.3
LoRA
63.0
74.8
54.6
66.8
54.8
67.2
13.1
21.9
28.3
40.4
VPT
71.7
81.6
62.5
73.8
61.7
73.3
14.5
22.6
40.6
54.9
FreestyleRet
69.5
80.3
60.5
73.5
63.7
75.7
12,2
18.7
43.1
58.7
Hystar(Ours)
75.7
86.4
65.8
78.1
65.5
78.3
19.0
29.9
43.7
59.3
Table 2: Zero-shot retrieval performance unseen styles. We evaluate Top-1 and Top-5 ac- curacy(%) on the DomainNet coarse garsse-rrined benchmark. Our proposed Hystar framework demon- strates strong performance in zero-shot category-level retrieval under unseen style conditions. Best results are highlighted in bold.
consistently to unseen styles, whereas the dynamic multi-style adaptation of our method maintains robust performance.
Overall, these findings demonstrate that while static adaptation methods and style-specific priors struggle to generalize, our methods robustly bridge style gaps and establish SOTA performance in zero-shot category-level retrieval.
DomainNet Classification Performance Analysis. To validate the generalizability of our method, we performed zero-shot classification on the DomainNet dataset. 3 the zero- shot classification performance of various methods on DomainNet across six styles. Although our main focus multi-style retrieval, this experiment demonstrates the generalization capability of the proposed Hystar model. Hystar consistently achieves the highest accuracy in all styles, outperform- ing CLIP, LoRA. VPT, and FreestyleRet, particularly in challenging styles such as Sketch (71.2% 64.9%) and Quickdraw (22.9% 14.6%).
8

---

Published as a conference paper at ICLR 2026
Method
Real
Clipart
Sketch
Painting
Quickdraw
Infograph
CLIP
82.4
72.8
64.9
68.1
14.6
53.0
LoRA
62.4
51.7
43.1
42.8
12.8
26.1
VPT
79.9
69.8
61.9
59.2
15.7
46.0
FreestyleRet
84.9
74.1
67.3
68.8
15.9
54.1
Hystar(Ours)
85.7
79.5
71.2
71.0
22.9
54.6
Table 3: Zero-shot classification performance of different methods on DomainNet across six styles. Accuracy (%) for each style is reported, with the best results highlighted in bold.
These results indicate that the representations learned by Hystar are not only effective for retrieval evalso transferable zero-shot classification, highlighting the model's ability to handle diverse visual styles without additional supervision.
4.3
QUALITATIVE RESULTS
(a)CLIP
(b)FreestyleRet
Hy)trr
Figure 3: t-SNE visualization of feature embeddings derived by different methods on the DSR dataset. CLIP: scattered, overlapping clusters. (b)FreestyleRet: more compact but some inter- class entanglement. (c)Hystar: clearly separable, compact clusters, showing strong cross-style alignment.
To further investigate how different models capture cross-style semantics, we visualize the learned embeddings on the DSR dataset using t-SNE, as shown in Figure Figure illustrates that CLIP embeddings form scattered and overlapping clusters, reflecting poor discrimination across styles. Figure b) shows that FreestyleRet improves cluster compactness, yet several categories remain entangled, suggesting limited separation. In contrast, Figure ((c) demonstrates that our proposed Hystar produces well-structured and clearly separable clusters, with minimal overlap between dif- ferent styles. These results indicate that Hystar not only enhances retrieval accuracy quantitatively but also achieves qualitatively superior representation learning by aligning cross-style features into more coherent semantic manifolds.
4.4 ABLATION STUDY
Ablation of the Framework Components. In this section, we perform ablation studies assess the contributions of the components of Hystar, namely StyleNCE. hyperettwrrk-diive modula- tion, and statie modulation. As shown in Table 4 the CLIP baseline performs poorly on style- variant queries. Both the Static-only and Hyper-only modulation modules contribute to improved retrieval performance. The Static module provides a globally robust adaptation, effectively aligning representations across different styles in stable manner; however, it lacks fine-grained flexibility for style-specific adjustments. In contrast, the hypernetwork-bee query-speciff, style adjustments, offering more flexible and personalized adaptation. While this flexibility allows Hyper to capture subtle style variations, it introduces some instability, limiting its standalone improvement.
dynamic modulation generates

---

Published as a conference paper at ICLR 2026
Method
Art
Sketch
Low-Res
Text
CLIP
58.5
47.5
45.0
66.1
CLIP+ + Static
65.7
77.0
83.7
69.2
CLIP + Hyper
63.8
70.6
76.3
66.5
CLIP + Hyper + Static
70.2
85.3
94.2
69.7
CLIP + Hyper + Static + StyleNCE
75.2
90.2
98.0
70.9
Table 4: Ablation study on the DSR benchmark. Unless otherwise specified, all variants are trained with standard InfoNCE loss. We report top-1 1 retrieval accuracy (%) across four query styles: Art, Sketch, now-Resolution, and Text. Best results are highlighted in bold.
Additional Params (%)
Inference Time Increase (%)
Method
Parameters(M)
(pe)d(ms)
CLIP
427
68
VPT
0.2
73
7.4
(IA)*
71
2.9
AdaptFormer
0.5
74
FreestyleRet
476
11.5
96
41.2
Hystar(Qurs)
3.5
108
58.8
Table 5: Computation comparison between our Hystar and representative baselines. For fair- ness, we only compare CLiP-based models; The percentages of additional parameters and inference- time increase are reported with respect the CLIP baseline.
These ablations show the two modules complementary: the Static module provides stable global alignment, while the Hyper module adds query-specific flexibility, and their combination yields clear synergistic gains. Incorporating StyleNCE further improves performance by mining hard style- negatives and boosting cross-style discrininability. Overall, static singular-valus modulation secures robust cross-style adaptation, dynamic hypernetwork modulation further improves it, and StyleNCE strengthens handling of style diversity and hard cases cases-together confirming Hystar's effectiveness for multi-style retrieval under diverse queries.
Computation Comparison. We analyze the computational complexity of our framework com- pared with other baselines. As shown in T Hystar achieves competitive efficiency among re- cent retrieval models. Although it introduces a modest increase in inference latency compared with CLIP, VPT, (IA)², AdaptFormer. and FreestyleRet, the overall parameter scale remains lightweight and only slightly larger than standard vision- languaee encoders. This marginal computational over- head mainly stems from the controller-guided hypemetwork module, which dynamically modulates representations for style adaptation. Importantly, the trade-off between adaptability and efficiency well controlled: Hystar substantially improves robustness preserving a compact and deployable architecture suitable for real-time retrieval scenarios.
style variation while
CONCLUSION
In this paper, we present Hystar, a dynamic multi-style retrieval framework that combines hypernetwork-driven dynamic modulation with static singular-value calibration, achieving a balance between adaptability and stability in parameter-efficies fine-tuning. better handle difficult nega- tives and style discrepancies, we introduce the OT-wwighted StyleNCE loss. Extensive experiments on DSR and DomainNet show that Hystar consistently outperforms strong baselines, while ablations confirm the complementary benefits of dynamic and static modulation. Furthermore, retrieval and classification experiments on unseen styles demonstrate that our method improves the performance of VLRMs under multi-style queries and ensures strong generalization to previously unseen styles. Hystar highlights the effectiveness dynamic PEFT for style diversity and cross-style generaliza- tion, providing solid foundation for multimodal applications robust to heterogeneous inputs.
10

---

Published as a conference paper at ICLR 2026
REPRODUCIBILITY STATEMENT
We have taken steps in this work to ensure the reproducibility of our results. All datasets used in our experiments are publicly available. In the main paper and appendices, we provide complete details of all experimental setups, including model architectures, training and evaluation protocols, and hyperparameters. All random seeds are fixed, ensuring that others can replicate our results with the provided code. We believe that the measures we have taken to ensure reproducibility will facilitate straightforward replication and verification of our findings, as well as allow the community to build upon our results in the future.
ACKNOWLEDGEMENTS
Our work is supported in part by the National Key R&D Program China (No. 2023YFC3305600, 809180207))))), National Natu- the Joint Fund of Ministry of Education of China 62302372), and Xidian University Specially ral Science Foundation of China (62132016, 62571412, Funded Project for Interdisciplinary Exploration (TZJHF202509).
REFERENCES
Ushasi Chaudhuri, Ruchika Chavan, Biplab Banerjee, Anjan Dutta, and Zeynep Akata. Bda-sketret: Bi-level domain adaptation for zero-shot sbir. Neurocomputing. 514:245-255, 2022.
Arnay Chavan, Zhuang Liu, Deepak Gupta, Eric Xing, and Zhiqiang Shen. One-for-all: Generalized lora for parameter-efficient fine-tuning. arXiv preprint 2xiv:2300.07967, 2023.
Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang. and Ping Luo. Adaptformer: Adapting vision transformers for scalable visual recognition, 2022. URL htthtts::
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning visual representations. International conference on machine learning. pp. 1597-1607. PmL., 2020.
Marco Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems, 26, 2013.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hi- erarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pp. 248-2442255 leee, 2009.
Hao Dong, Moru Liu, Kaiyang Zhou, Eleni Chatzi, Juho Kannala, Cyrill Stachniss, and Olga Fink. Advances in multimodal adaptation and generalization: From traditional approaches foundation models. arXiv preprint arXiv:2501.18592, 2025.
Shiyin Dong, Mingrui Zhu, Nannan Wang, and Xinbo Gao. Adapt and align improve zero-shot sketch- based image retrieval. arXiv preprint arXiv:2305.05144, 2023.
Chendi Ge, Xin Wang, Zeyang Zhang, Hong Chen, Jiapei Fan, Longtao Huang, Hui Xue, and Wenwu Zhu. Dynamic mixture of curriculum lora experts for continual multimodal instruction tuning. arXiv preprint wwXi::250.11162. 2025.
Rohit Girdhar, Alaaeldin Bl-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 15180-15190, 2023.
Jihao Gu, Shuai Chen, Zelin Wang, Yibo Zhang, and Ping Gong. Sara: singular-value based adaptive low-rank adaption. arXiv preprint arXiv:2408.03299. 2024.
David Ha, Andrew Dai, and Quoe V Le. Hypernetworks. arXiv preprint arXiv: 160.009199 2016.
11

---

Published as a conference paper at ICLR 2026
Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning invariant mapping. In 2006 IEEE computer sociery conference on computer vision and pattern recognition (CVPR (CV),'06), volume 2. pp. 1735-1742. IEEE, 2006.
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp- 9729-9738, 2020.
Dan Hendrycks and Kevin Gimpel. Gaussian enror linear units (gelus), 2023. URL https:// arxiv.org/abs/1600 08415
08415
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022.
Folasade Olubusola Isinkaye, Yetunde O Folajimi, and Bolande Adefowoke Ojokoh. Recommenda- tion systems: Principles, methods and evaluation. Egyptian informatics journal, 16(3):261-273,
2015.
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. Visual prompt tuning. In European conference computer vision, pp. 709-727. Springer, 2022.
Ruijie Jiang, Prakash Ishwar, and Shuchin Aeron. Hard negative sampling via regularized opti- mal transport for contrastive representation learning. In 2023 International Joint Conference on Neural Networks (IJCNN), pp. 1-8. IEEE, 2023.
Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, and Li Fei-Fei. Image retrieval using scene graphs. In Proceedings of the IEEE conference com- puter vision and pattern recognition, pp. 3668-3678, 2015.
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler Hayes, and Christopher Kanan. Measuring catastrophic forgetting in neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shah- baz Khan. Maple: Multi-modal prompt learning. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 19113-19122, 2023.
Vinay Kumar Verma, Aakansha Mishra, Ashish Mishra, and Piyush Rai. Generative model for zero- shot Axetch-based image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0-0, 2019.
Linda Laurier. Continual learning: Overcoming catastrophic forgetting in neural networks-a survey. RN, 1:1.
Jianjun Lei, Yuxin Song, Bo Peng. Zhanyu Ma, Ling Shao, and Yi-Zhe Song. Semi-hetcrogencous three-way joint embedding network for sketch-based image retrieval. IEEE Transactions Cir- cuits and Systems for Video Technology, 30(9):3226-3223 3237, September 2020. ISSN 1558-2205. doi: 11110/tcstt.21992936666
URL httt:://aaa. doi ort. http:///1..111//CC//11 1ww1/1111SSSS 2019.
2936710
Hao Li, Xu Li, Belhal Karimi, Jie Chen, and Mingming Sun. Joint learning of object graph and rela- tion graph for visual question answering. In 2022 IEEE International Conference on Multimedia and Expo (ICME), pp. 01-06. IEEE, 2022a.
Hao Li, Jinfa Huang, Peng Jin, Guoli Song, Qi Wu, and Jie Chen. Weakly-supervised 3d spatial reasoning for text-based visual question answering. IEEE Transactions Image Processing. 32: 3367-3382, 2023.
Hao Li, Yanhao Jia, Peng Jin, Zesen Cheng, Kehan Li, Jialu Sui, Chang Liu, and Li Yuan. Freestyleret: retrieving images from style-diversified queries. In European Conference on Com- puler Vision, pp. 258-274, Springer, 2024a.
12

---

Published as a conference paper at ICLR 2026
Jindong Li, Yongguang Li, Yali Fu, Jiahong Liu, Yixin Liu, Menglin Yang, and Irwin King. Clip- powered domain generalization and domain adaptation: A comprehensive survey. arXiv preprint arXiv:2504. 14280, 2025.
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (eds.), Advances in Neural Information Processing Systems, volume 34, pp. 9694-9705 Curran Associates, Inc., 2021a. URL htt
files/
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language- image pre- training for unified vision- langugg understanding and generation. In International conference on machine learning, pp. 12888-1290M PMLR, 2022b.
Xiaoqing Li, Jiansheng Yang, and Jinwen Ma. Recent developments of content-based image retrieval (cbir). Neurocomputing, 452:675-689, 2021b.
Yang Li, Shaobo Han, and Shihao Ji. Vb-lora: Extreme parameter efficient fine-tuning with vector
banks. Advances in Neural Information Processing Systems, 37:16724-166751 2024b..
Yi Li and Wenzhao Li. A survey of sketch-based image retrieval. Machine Vision and Applications, 29(7):1083-1100 1100, 2018
Dongze Lian, Daquan Zhou, Jiashi Feng, and Xinchao Wang. Scaling shifting your features: A new baseline for efficient model tuning, 2023. URL htttht:s:////xiiii wwwpz/iii.rrrrooooooooo abs 221 0. 08823
Vijay Chandra Lingam, Atula Neerkaje, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Eunsol Choi, Alex Dimakis, Aleksandar Bojchevski, and Sujay Sanghavi. Svft: snrameter-efficieen fine-tuning with singular vectors. Advances Neural Information Processing Systems, 17:41425-40 414444444 41446, 2024.
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning, Advances in Neural Information Processing Systems, 15:1950- 2022.
Wenjing Liu, Qin Ren, Wen Zhang, Yuewei Lin, and Chenyu You. Together, then apart: Revisiting multimodal survival analysis via a min-max perspective. arXiv preprint (.X.xi2211118800 2025.
Peng Lu, Gao Huang, Hangyu Lin, Wenming Yang. Guodong Guo, and Yanwei Fu. Domain-aware se network for sketch-based image retrieval with multiplicative euclidean margin softmax. In Proceedings of the 29th ACM International Conference on Multimedia, pp. 3418-3426, 2021.
Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference machine learning (ICML-10), pp. 807-814, 2010.
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predic- tive coding. arXiv preprint www.iv:807.0774440 2018
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniee, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023.
Kaiyue Pang, Ke Li, Yongxin Yang, Honggang Zhang, Timothy M Hospedales, Tao Xiang, and Yi-Zhe Song. Generalising fine-grained keetch-based image retrieval. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 677-686, 2019.
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 1406-144 1415, 2019.
Gabriel Peyré, Marco Cuturi, et al. Computational optimal transport: With applications to data science. Foundations and Trendy@ in Machine Learning, 11(-6))355-607, 2019.
13

---

Published as a conference paper at ICLR 2026
Jielin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao, Bo Li, and Mu Li. Benchmarking robustness of multimodal image-text models under distribution shift. arXiv preprint ar arXiv:2212.0804442 2022.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp- 9748-8766 PmLR, 2021.
Qin Ren, Yifan Wang, Ruogu Fang, Haibin Ling, and Chenyu You. Otsurv: A novel multiple instance learning framework for survival prediction with heterogeneity-awan optimal transport. In International Conference Medical Image Computing and Computer-Assisted Intervention, pp. 439-449. Springer, 2025a.
Qin Ren, Yufei Wang. Lanqing Guo, Wen Zhang, Zhiwen Fan, and Chenyu You. Scale where matters: Training-fre frree localized scaling for diffusion models. arXiv preprint arXiv:2511.19917, 2025b.
Joshua Robinson, Ching Yao Chuang, Suvrit Sra, and Stefanie Jegelka. Contrastive learning with hard negative samples. arXiv preprint arXiv:2010.04592 2020.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Anceshan Sain, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Subhadeep Koley, Tao Xiang, and Yi-Zhe Song. Clip for all things zero-shot sketch-based image retrieval, fine-grained or not. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 2765- 2775, 2023.
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409. 1556, 2014.
Yunlong Tang, Yuxuan Wan, Lei Qi, and Xin Geng. Dpstyler: dynamic promptstyler for source-free domain generalization. IEEE Transactions on Multimedia, 2025.
Bart Thomee and Michael S Lew. Interactive search in image retrieval: a survey. International Journal of Multimedia Information Retrieval, 1(2):71-86. 2012.
Jindong Wang. Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip S Yu. Generalizing to unseen domains: survey on domain generalization. IEEE transactions on knowledge and data engineering, 35(8):8052-800 8072, 2022.
Zhipeng Wang, Hao Wang, Jiexi Yan, Aming Wu, and Cheng Deng. Domain-smoothing network for zero-shot sketch-based image retrieval. arXiv preprint arXiv:2 18441, 2021.
Zhiwu Wang. Yichen Wu, Renzhen Wang, Haokun Lin, Quanziang Wang, Qian Zhao, and Deyu Meng. Singular value fine-tuning for few-shot class-incremental learning. arXiv preprint arXiv:2503. 102/4 2025.
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to In 2010 IEEE computer society conference computer vision and pattern recognition, pp. 3485-3492. IEBE, 2010.
Jiexi Yan, Zhihui Yin, Chenghao Xu, Cheng Deng, and Heng Huang. Retrieval across any domains via large-scale pre-trained model. In Forty-first International Conference on Machine Learning, 2024.
Hantao Yao, Rui Zhang, and Changsheng Xu. Visual- Visual-language prompt tuning with knowledge- guided context optimization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6757-6767, 2023.
14

---

Published as a conference paper at ICLR 2026
Hantao Yao, Rui Zhang, and Changsheng Xu. Tep: Textual-basee -ased alass- prompt tuning for visual-language model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14438-23448,, 2024.
Chenyu You, Haocheng Dai, Yifei Min, Jasjeet S Sekhon, Sarang Joshi, and James Duncan. Un- covering memorization effect in the presence of spurious correlations. Nature Communications, 16(1):5424, 2025.
Xiaoyan Yu, Tongxu Luo, Yifan Wei, Fangyu Lei, Yiming Huang, Hao Peng, and Liehuang Zhu. Neeko: Leveraging dynamic lora for efficient multi-character role-playing agent. arXiv preprint arXiv:2402.13717, 2024.
Wen Zhang, Qin Ren, Wenjing Liu, Haibin Ling, and Chenyu You. Supervise less, see more: Training-free nuclear instance segmentation with prototype-guided prompting. arXiv preprint arXiv:2511.19953, 2025.
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy. Domain generalization: A survey. IEEE transactions on pattern analysis and machine intelligence, 45(4):4396 4415, 2022a.
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwci Liu. Cocoop: Conditional prompt learning for vision-language models. arXiv preprint arXiv:2203.05557, 2022b.
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision- language models. International Journal of Computer Vision, 13009):2337-2348, 2022c.
Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang. Prompt-aligned gradient for prompt tuning. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 15659-15669, 2023a.
Bin Zhu, Bin Lin, Munan Ning, Yang Yan, Jiaxi Cui, Hongla Wang, Yatian Pang. Wenhao Jiang, Junwu Zhang, Zongwei Li, et al. Languagebind: Extending video-language pretraining to modality by language-basee semantic alignment. arXiv preprint arXiv:2310.01852, 2023b.
15

---

Published as a conference paper at ICLR 2026
APPENDIX
Due to the space limitations of the main text, we provide additional results, ablations, and analysis in this supplementary material. The appendix is organized follows:
Section
A
Theoretical intuition of singular-value modulation.
Section
Analysis for dynamic attention and static MLP design.
Section
Cross-modal and cross-style retrieval results.
Section
D
Additional ablation of Hystar.
Section D.1
Ablation on injection layers.
Section D.2
Ablation on the width and depth the hypernetwork.
Section D.3
Ablation on the style extractor.
Section Broader generalization studies.
Section
E.1
Generalization evaluation on image classification.
Seectin
Generalization to other vision-language representation models (VL-
-
RMs).
Section F
Analysis of special styles.
Section
F1
Analysis of extremely distinctive styles.
Section
Analysis of mixed-style queries.
Section
Additional ablation of StyleNCE loss.
Section G.1
Effect of positive-negative balance coefficient 7
Section
Sensitivity analysis of hard-negative weight in OT optimization.
Section
Comparison of StyleNCE and other loss functions.
Section Retrieval result visualization.
Section
Retrieval result visualization on DSR.
Section Retrieval result visualization DomainNet.
A THEORETICAL INTUITION OF SINGULAR- VALUE MODULATION
In this section, provide theoretical justification for our design choice of singular- value modula- tion. In our design, the Hypernetwork predicts low-rank updates by modulating the singular values of LoRA weight matrices, instead of predicting the full weight increment. Let Wo Rdixd2 denote a pretrained weight matrix, and let its singular value decomposition
Wo = UEVT, Σ =
where r = min(d), d2). Our Hypernetwor outputs singular value increment AE, resulting in the updated weight
W U
The key insight is that the spectral norm (largest singular value) of the update is directly controlled:
IW 2o
| max Ass]).
In other words, by modulating only the singular values, we bound the maximum amplification along any input direction, avoiding gradient explosion or collapse. Compared with predicting the full ma- trix increment AW, this approach is both computationally efficient (requiring far fewer parameters) and stable training.
This theoretical intuition motivates engineering choice: singular-vall value modulation ensures that the Hypernetwork can provide style-adaptive updates controlled and lightweight manner, which crucial for stable multi-style retrieval.
16

---

Published as a conference paper at ICLR 2026
Method
Art
Sketch
Low-Res
Text
Dynamic-All Design
63.7
69.9
74.8
66.3
Reversed Design
64.1
71.2
80.2
69.1
Original Design
75.2
90.2
98.0
70.9
Table Comparison of different dynamic modulation strategies in Hystar, including the orig- inal design (dynamic-attention and static-MLP), the reversed design (dynamic-MLP and static- attention), and the fully dynamic design (dynamic-attention and dynamic-MLPP) The table reports Top-1 accuracy (%) for joint retrieval with different style queries combined with text. Best results are highlighted in bold.
B ANALYSIS FOR DYNAMIC ATTENTION AND STATIC MLP DESIGN
To verify the design rationale employing dynamic modulation in the attention layers while keep- ing the MLP layers static, construct reversed variant of Hystar. In this variant, all attention layers are statically fine-tuned, whereas dynamic modulation is injected into the first linear layer of the MLP blocks the same layer indices the original dynamic-attention setup. This design sures comparable parameter counts while allowing the second linear layer to be indirectly affected. We also include additional variant where both the attention and MLP layers dynamically modulated at the same layer indices as in the original design. However, this setting introduces larger number of trainable parameters, making it incomparable with the other variants under fixed parameter budget. Moreover, applying static tuning to all layers would contradict the purpose of our style-conditioned modulation and therefore not considered.
As shown in Table 6 the original design achieves the highest Top-1 accuracy across all style do- mains, demonstrating that dynamically adapting attention most effective for cross-style gener- alization. In contrast, the Dynamic-All configuration performs the worst, indicating that applying dynamic modulation to all layers introduces excessive flexibility and leads to unstable training. The reversed design also underperforms, suggesting that dynamic modulation in MLPs cannot effec- tively capture style variations. Conceptually, attention layers govern cross-token relationships that directly influenced by style cues such texture and composition, making them well-suited for dynamic modulation. In contrast, MLP layers primarily refine semantic features within tokens; keeping them static preserves semantic stability and prevents overfitting transient style attributes. These results confirm that our hybrid strategy, which employs dynamic attention for style adaptation and static MLPs for semantic stability, achieves the best balance between adaptability and stability.
C CROSS-MODAL AND PROSS-STYLE RETRIEVAL RESULTS
Model
Art+Text
Sketch+Text
Low-Res+Text
Text
CLIP*
57.8(-14.4)
65.0(-7.2)
84.7(+12.5)
72.2
LoRA
72.2(+1.8)
79.3(+8.9)
84.7(+14.3)
70.4
VPT
70.6(+0.7)
79.0(+9.1)
84.3(+14.4)
69.9
FreestyleRet
76.6(+6.7)
82.5(+12.6)
86.7(+16.8)
69.9
Hystar(Ours)
79.9(+9.0)
91.6(+20.7)
98.2(+27.3)
70.9
Table 7: Top-1 accuracy (%) for joint retrieval with different style queries combined with text. Values in parentheses indicate the accuracy gain over text-only queries. Best results are highlighted in bold.
This section presents the quantitative performance our model on joint style-text text retrieval tasks, followed by a breakdown across different query styles. Following the evaluation protocol established in FreestyleRet, we compute the similarity between each query (style or text) and all gallery images, and adopt the maximum similarity the final retrieval score.
17

---

Published as a conference paper at ICLR 2026
Table reports top-1 accuracy for three joint query modes (Art+Text, Sketch+Text, Low-Res+ Text). On Aet+ our method achieves 79.9%, surpassing FreestyleRet (76.6%), CLIP (57.8%). as well parameter--fffiiiet tuming baselines such as LoRA (72.2%) and VPT (70.6%). For Sketch+Text the advantage becomes more pronounced: our model obtains 91 91.6%, substantially outperforning FreestyleRet (82.5%) and CLIP (65.0%). Under the Low-Res+Tex setting, method reaches 98.2%, clearly ahead of FreestyleRet (86.7%) and CLIP (84.7%).
Compared to text-only queries, incorporating style queries provides significant performance gains: +9.0% (Art), +20.7% (Sketch), and +27.3% (Low-Res), all markedly higher than those achieved by baseline methods. Moreover, relative to style-only queries (Table T). our joint approach also yields further improvements (+4.7% on Art, +1.4% on Sketch, and +0.2% on Low-Res). These results suggest that while the marginal benefit of adding text diminishes when style-only queries are already strong, the consistent improvements across all settings highlight the complementary nature of textual and stylistic cues, underscoring the robustness of our multimodal integration strategy.
D ADDITIONAL ABLATION OF HYSTAR
In this section, we conduct additional ablation studies to better understand the design choices of Hystar. We systematically analyze how different architectural and functional components contribute to performance and efficiency. Specifically, injection layers for dynamic modulation, (2) the width and depth configuration of the hypernetwork, and (3) the choice of style feature extractors used for conditioning. Together, these studies provide deeper insights into the trade-off's between adaptability, stability, and computational cost in our framework.
examine three key aspects: (1) the selection of
D.1 ABLATION ON INJECTION LAYERS
Table & presents an ablation study on the choice of hypernetwork injection layers across three ma- jor query styles (Art, Sketch, and now-Resolution). The upper block shows results for various middle-layer injection schemes, which constitute our main design. We observe that selectively injecting into middle layers (e.g., [4, 8, 10, 12) or {4, 7, 10, 13)) consistently yields the best trade-off between retrieval accuracy and parameter overhead. Injecting into all intermediate lay- ers ((4, 5, 6, ... 12}) yields marginal performance improvements but incurs substantial increase in parameter cost. Moreover, the overly aggressive style-aware adaptation introduces instability, leading to performance degradation compared to configurations with fewer injected layers.
Injection Layers
Art
Sketch
Low-Res
Top-1 Avg
A Params
Middle-layer injection (main study)
(4,5,6,.... 12}
69.3
76.5
84.7
76.8
+33.IM
(4,6, 8, 10, 12)
75.4
90.1
98.6
88.0
+18.4M
7,, 13)
75.2
90.2
98.0
87.8
+14.7M
(1,
71.4
88.3
96.8
85.5
+11.0M
Additional evidence: early / late layers
(1) (16, 19, 22,
60.2
72.4
76.3
69.6
+3.7M
63.1
75.4
81.7
73.4
+14.7M
Table Ablation study hypernetwork injection layers across three major query styles (Art, Sketch, and nww-Reeolution). We report Top-1 accuracy (%) for each style, averaged performance, and parameter overhead (A Params). Best results are highlighted in bold.
The lower block provides additional evidence early and late layers. Injection into the very early layer (f13) results in poor performance, indicating that low-level features captured early layers less relevant for style-aware retrieval. Similarly, injecting exclusively into late layers (f16, 19, 22, 24}) produces only moderate gains, suggesting that later layers are dominated by high- level semantic features, leaving limited room for style modulation. Overall, this ablation confirms
18

---

Published as a conference paper at ICLR 2026
that the middle layers the most effective region for hypernetwork injection, balancing accuracy improvement and parameter efficiency.
Network Architecture
Art
Sketch
Low-Res
Top-1 Avg
Params
Width Ablation
(768 1024 + 1024]
70.7
84.3
90.2
81.7
+1.8M
(768 2048 1024)
75.2
90.2
98.0
87.8
+3.7M
(768 4096 1024]
75.4
90.8
98.6
88.3
+7.3M
Depth Ablation
(768 1024)
68.6
80.9
88.4
79.3
+0.8M
(768 + 2048 - 1024]
75.2
90.2
98.0
87.8
+3.7M
(768 + 2018 - 2048 + 1024}
74.9
90.4
97.6
87.6
+7.9M
Table 9: Ablation study on hy hypernetwork layer configurations. We vary the width and depth of the hypernetwork that predicts dynamic modulation parameters for attention layers. The input di- mension (768) corresponds the feature output the DINOv2 encoder, and the target dimension (1024) corresponds to the singular-value dimension of CLIP"s attention weights. All intermediate linear layers are followed by ReLU activation (omitted in notation for brevity), except for the final projection layer. We evaluate Top-1 I accuracy (%) across three major query styles (Art, Sketch, and Nww-Resolution, along with the averaged performance and parameter overhead (A Params). Best results highlighted in bold.
D.2 ABLATION ON THE WIDTH AND DEPTH OF THE HYPERNETWORK
The results Table show clear trends regarding the structural design of the hypernetorr. Increas- ing the hidden width consistently improves accuracy across all query styles, but the gain saturates beyond moderate expansion. Specifically, moving from 1024 2048 hidden units yields large performance boost, whereas further enlarging to 4096 offers only marginal improvement the cost of nearly doubling the parameters. This suggests that excessive width leads to over-parameterization with diminishing returns. Regarding depth, extending the hypernetwork beyond two layers does not improve accuracy and can even cause slight degradation, likely due to optimization instability and redundant transformations. Overall, two-layer configuration with moderate width (2x expansion) achieves the best balance between expressivity, stability, and efficiency. and is therefore adopted as default design.
D.3 ABLATION ON THE STYLE EXTRACTOR
To verify that the performance gain of Hystar does not rely on a specific feature extractor such as DINOv2, we evaluate alternative sources for deriving the style vector z. As shown in Table I0] using pretrained visual features (either from DINOv2, VGG (Simonyan Zisserman 2014) or the CLIP backbone itself) substantially outperforms the static baseline without external style cues. This confirms that explicit style aandtiiniigg-rataee than the particular choice of extractor-is key factor driving improvement. Moreover, the gap between DINOv2 and CLIP- based features is relatively small (83.6 vs. 83.0 Top-1 average), suggesting that the hypernetwork effectively adapts to diverse feature domains and that Hystar's benefit not confounded by using another vision encoder. DINOv2 slightly outperforms CL.IP(self), consistent with its stronger representation of texture and spatial statistics, but both yield similar cross-style generalization trends. These results validate that the proposed method's advantage arises from its adaptive mechanism, not from an unfair reliance on external feature strength.
E BROADER GENERALIZATION STUDIES
In this section, we extend our evaluation beyond retrieval to more diverse task settings and further in- clude experiments other representative lasion-language representation models (VLRMs), aiming
19

---

Published as a conference paper at ICLR 2026
Extractor
Art
Sketch
Low-Res
Text
Top-1 Avg
Static Only
65.7
77.0
83.7
69.2
73.9
VGG
73.4
88.5
96.9
70.1
82.2
DINOv2
75.2
90.2
98.0
70.9
83.6
CLIP(self) 74.1
90.0
97.5
70.3
83.0
Table 10: Ablation on style feature extractors for hypernetwork conditioning. We compare different choices of feature sources used to derive the style vector in Hystar the CLIP backbone across four query styles (Art, Sketch, cow-Resolution and Text). We report Top-1 accuracy (%) for each style and the average.
systematically examine the generalization capability of the proposed hybrid modulation mech- anism across different scenarios and model architectures. Specifically, analyze its adaptability from two complementary perspectives: (1) image classification under the base-to-new transfer set- ting. and (2) mroel validation the ALBEF framework. Together, these studies provide a more comprehensive understanding of Hystar's generalization potential and the stability of the proposed method across tasks and models.
E. GENERALIZATION EVALUATION ON IMAGE CLASSIFICATION
To further assess generalization, we follow the CoOp (Zhou et al. 2022c
image-classffication
protocol and conduct 16-shot base-to-new experiments on ImageNet (Deg al. (0099 and
SUN397 (Xiao et al., 2010). where each class provides 16 labeled samples for training. We compare against baselines including CoOp (Zhou al.) 2022c). ProGrad (Zhu et al. [023a)) KgCoOp (Yao
ct al. 2002). MaPLe (Khattak et al. 2023), and TCP Yao al. 2024). As summarized in Table Hystar delivers the strongest generalization on New and H (harmonic mean) splits on ImageNet
(New: 70.98, H: 73.93), indicating improved adaptation to unseen categories while maintaining base-domain stability. On SUN397, MaPLe attains the best Nes score (78.70), whereas TCP slightly leads H (80.35); Hystar closely follows both metrics (New: 78.41, H: 80.16), showing com- petitive cross-domain robustness. Averaged across datasets, Hystar achieves the best New (74.70) and H (77.03) (77.03), improving over the strongest baselines (e.g., +0.08 New MaPLe and +0.15 H vs. TCP), while accepting marginal drop on Base compared to TCP (79.51 vs. 79.95). These results suggest that the proposed dynamic modulation yields favorable trade-off between base- domain retention and out-of-domain adaptability, which is precisely the balance needed for reliable few-shot generalization.
Datasets
Sets
CoOp
ProGrad
KgCoOp
MaPLe
TCP
Hystar(ours)
Base
76.46
77.02
75.83
76.66
77.27
77.13
ImageNet
New
66.31
66.66
69.96
70.54
69.87
70.98
H
71.02
71.46
72.78
73.47
73.38
73.93
Base
80.85
81.26
80.29
80.82
82.63
81.89
SUN397
New
68.34
74.17
76.53
78.70
78.20
78.41
H
74.07
77.55
78.36
79.75
80.35
80.16
Base
78.66
79.14
78.06
78.74
79.95
79.51
Average
New
67.33
70.41
73.25
74.62
74.04
74.70
72.56
74.52
75.58
76.62
76.88
77.03
Table 11: Generalization results 16-shot image classification benchmarks. Each category contains 16 training samples. We evaluate cross-domain generalization from base to new classes on ImageNet and SUN397. Hystar consistently achieves the highest accuracy New and H (harmonic mean) splits, demonstrating strong generalization to unseen categories. Best results are highlighted in bold.
20

---

Published as a conference paper at ICLR 2026
E.2 GENERALIZATION TO OTHER VISION-LANUUAGE REPRESENTATION MODELS (VLRMS)
To assess the generalizability of our hypernetwork baseed multi-style retrieval framework beyond CLIP and BLIP, we further experiment with the ALBEF backbone. Since ALBEF consists of only 12 encoder layers, we proportionally select layers 4, and for hypernetwork injection. The results are summarized in Table 12 For the StyleNCE loss, we adopt the same hyperparameters as used with CLIP, without any backbone-specific tuning.
Method
Art
Sketch
Low-Res
Text
Top-1 Avg
ALBEF
63.7
52.4
39.1
61.7
54.2
Hystar(ours)
71.0
84.5
91.5
64.3
77.8
Table 12: Multi-style retrieval performance ALBEF backbone across four query styles (Art, Sketch, now-Resolution, and Text). We report Top-1 accuracy (%) for each style and the average. Our hypernetwork injection improves performance consistently, showing generalization beyond the CLIP backbone and BLIP backbone.
As shown in Table 12 the proposed hypernetwork approach improves retrieval performance across all four styles, even when applied to ALBEF without any specialized tuning. While the improve- ments are generally smaller than those observed on CLIP and BLIP (the main experimental back- bone), this validates the architecuree applicability of our method. These results indicate that the benefits of middle-layer injection and cross-style feature learning not limited to single VLRM, supporting the generality of our approach multi-style retrieval scenarios.
F ANALYSIS OF SPECIAL STYLES
In this section, we further analyze Hystar's behavior under challenging and unconventional visual conditions. Specifically, we examine two aspects of style generalization beyond standard artistic domains: (1) adaptation to extremely distinctive and unseen styles that differ drastically from the training distribution, and (2) responses to mixed-style queries that blend multiple stylistic attributes within single image. These analyses reveal how Hystar maintains semantic consistency while flexibly modeling complex or hybrid style variations.
F.1 ANALYSIS OF EXTREMELY DISTINCTIVE STYLES
To evaluate the generalization ability our model to extremely abstract and unseen styles, we construct an evaluation benchmark based the DomainNet dataset. We randomly select 1,000 images from the real domain, covering 50 categories with 20 images per category, and use Stable Diffusion (Rombach et al.) 2022) to generate corresponding versions in three extreme artistic styles: Surrealist Abstract Art, Post- Dost-Impressionis/ Painting, and makhaash Painting. These styles are highly abstract, visually unconventional, and entirely unseen during training. We employ Stable Diffusion with the following textual prompts where {object, } is placeholder, e.g., "cat"):
Surrealist Abstract Art: A photo of (object), surrealist abstract art, dream-like forms, distorted proportions, fluid shapes, high contrast lighting, masterpiece.
Post-Impressionist Painting: A photo of a (object), post-impreasioni onist oil painting, visible brush strokes, vibrant color palette, Van Gogh and Cézanne inspired, expressive texture.
Ink-Wash Painting: A photo of a (object), traditional Chinese ink-wash painting, minimal color, soft brush ink diffusion, paper texture, serene composition.
For qualitative visualization, we display two representative samples for each style(Figure Under the zero-shot setting, we test the model's ability retrieve the correct real-domain images given
21

---

Published as a conference paper at ICLR 2026
queries from these extreme styles. All models (except the original CLIP) are pretrained only on the DSR dataset. The quantitative results are reported Table
As shown in Table 13 all methods experience noticeable performance drop under these extremely abstract and out-of-distributio styles, confirming the significant domain gap between realistic and artistic representations. Our proposed Hystar achieves the highest Top-1 accuracy across all three challenging styles, outperforming the strongest baseline by 3.5% on average. The improvements are especially pronounced on the most abstract Surrealist Abstract Art domain, indicating that dynam- ically modulated attention effectively captures style-specific variations while maintaining semantic consistency. These results highlight Hystar's superior ability generalize across visually divergent and previously unseen artistic domains.
Surrealist Abstract Art
Dost-Imppessionist Painting
ink-Wash Painting
Method
Top-1 Avg
CLIP
13.7
51.4
33.5
32.9
LoRA
12.4
47.0
29.4
29.6
VPT
16.1
52.6
31.2
33.3
FreestyleRet
22.8
70.2
38.6
43.9
Hystar(ours)
76.9
40.1
47.4
Table 13: Retrieval performance under extreme styles. We evaluate multiple methods on three highly distinctive style domains: Surrealist Abstract Art, Post-Impressionist Painting (Van Gogh- like), and Ink-Wash Painting. Results are reported as Top-1 accuracy (%) across the three extreme query types and their average. Best results are highlighted in bold. Our method (Hystar) achieves consistent improvements across all style types. demonstrating superior robustness and generalization to extreme and out-of-dine out-of-distributio visual styles.
(a) Surrealist Abstract Art
(b) Post-Impressionist Painting
(c) Ink- Wash Painting
Figure 4: Examples of images with extreme styles.
F.2 ANALYSIS MIXED-STYEE QUERIES
To study how Hystar responds to style mixtures, we use Stable Diffusion to synthesize three sets of images: Sketch, Art, and their Mixture. To control semantic content, we fix the object category and generate 50 images per set. The prompts are as follows (where {object] is placeholder, e.g., "cat"):
Sketch: A {object], clean pencil sketch, line art, monochrome, minimal shading, white background, highly detailed, professional illustration.
A [object), oil painting, rich brush strokes, vibrant color palette, canvas texture, dramatic lighting, high detail, masterpiece.
Art:
Mixture:
A (object), hybrid style combining oil painting and pencil sketch, partially sketched outlines, visible graphite lines with textured brush strokes, mixed-media look, coherent composition, highly detailed.
22

---

Published as a conference paper at ICLR 2026
Sketch
t-SNE
Middle-Layer Feature
Hystar
Art
Mixture
Figure 5: Style-aware feature distribution learned by Hystar. Hystar maps images from differ- ent styles (Art, Sketch, and their Mixture) into coherent embedding space. As shown in the t-SNE plot, the mixed-style samples (blue) form a transitional manifold between Art (black) and Sketch (red), demonstrating that Hystar's representations smoothly capture style blending while maintain- ing semantic alignment.
For each image, we forward it through Hystar and extract the visual representation from middle Transformer block of CLIP; we then visualize the features by projecting them to 2D with t-SNE. The results(Figure (5 show two compact and separable clusters for Art and Sketch, while the Mixture samples not collapse into either cluster but instead form a continuous "bridge" between them. The bridge shifts toward the visually dominant component style, indicating that Hystar's mid-level representation varies smoothly with style strength and mixture ratio while preserving semantic con- sistency. This behavior suggests that Hystar encodes style in continuous and interpretable manner and maintains good style separability under stable content representations.
G ADDITIONAL. ABLATION OF STYLENCE LOss
In this section, we investigate the sensitivity of the StyleNCE loss to its key hyperparameters. Specif- ically, we study (i) the positive-negative balance coefficient y. which controls the relative weighting between positive and negative pairs, (ii) the hard-negative weight À in the OT optimization, which regulates the contribution of difficult negatives, and (iii) comparison between StyleNCE and other loss functions. All analyses are conducted on the DSR dataset across three representative styles: Art, Sketch, and Low-Resolution
G.1 EFFECT OF POSITIVE-NRGATE BALANCE CORFFICIENT y
Figure 6 illustrates the effect of varying 1, 10, 30, 50, 80, 120, 200, 500. When y is too small (c.g., = 1), the contribution of negative samples becomes negligible, causing training to be domi- nated by positives, which slows convergence and significantly degrades retrieval accuracy. Increas- ing y accelerates convergence and improves final performance, with the best results obtained in the range o 80
120. Further enlarging 7 (e.g., 7 500) does not provide additional bene- fits and instead introduces slight instability, leading to performance drops relative to the mid-range values. These findings highlight the importance of maintaining balanced contribution between positive and negative samples for stable optimization.
G.2 SENSITIVITY ANALYSIS OF HARD-NEGATIVE WEIGHT IN OT OPTIMIZATION
Figure 7 presents a sensitivity analysis of the hard-negative weighting coefficient E 0.1, 0.5, 1.0, 2.0, 3.0. 10.0. We observe that very small values (e.g., x 0.1) place exces- sive emphasis on hard negatives, causing the model to largely ignore easy negatives, which desta- bilizes training and reduces performance. Conversely, excessively large values (e.g., À 10.0)
23

---

Published as a conference paper at ICLR 2026
Sketch
Low-Res
86
96
Accuracy
Accurscy
66
92
90
2
Epach
Epoch
Epoch
yel
yo30
ye80
y-200
y010
y-120
Figure 6: Effect of the positive-negative balance coefficient DSR retrieval accuracy(%6). Re- sults are reported for three styles: Art, Sketch, and now-Resolution. Small values of y (e.g., 1) result in slower convergence and lower accuracy, while moderate values (80 + I 120) achieve the best trade-off between stability and performance.
Sketch
86
94
96
Accunsc
82
1.4
80
64
78
92
90
Epach
Epoch
Epoch
A-0.1
2-0.5
A-2.0
2-5.0
A-0.3
2-1.0
A-3.0
2-10.0
Figure 7: Sensitivity analysis of the hard-negative weight À in OT optimization on DSR retrieval accuracy(%). Results are reported for Art, Sketch, and Low-Resolution. Moderate values (1.0 s A < 3.0) yield the best performance. Very small values underweight easy negatives, whereas very large values fail to effectively exploit hard negatives, limiting performance.
derweight hard negatives, resulting in insufficient hard-negativy mining and moderate performance degradation. Consistently high retrieval accuracy and stable convergence are observed for interme- diate values (A 1.0-3.0). These findings indicate that appropriately balancing the contribution of hard negatives is critical for fully leveraging OT-based optimization.
G.3 COMPARISON OF STYLENCE AND OTHER Loss FUNCTIONS
Method
Art
Sketch
Low-Res
Top-1 Avg
Triplet loss
65.6
71.9
89.5
75.7
InfoNCE loss
70.2
85.3
94.2
83.2
Circle loss
70.4
88.8
96.1
85.1
Triplet loss Hard Negative Sampling
69.3
80.2
93.0
80.8
InfoNCE loss Hard Negative Sampling
72.6
88.4
96.7
85.9
StyleNCE loss(Ours)
75.2
90.2
98.0
87.8
Table Ablation study of different loss functions on the CLIP backbone. Results are reported as Top-1 accuracy (%) across the three major query types used for training (Art, Sketch, and Low- Resolution) and their average. Best results are highlighted in bold.
24

---

Published as a conference paper at ICLR 2026
observe that the choice of loss function has significant impact retrieval per- formance. The baseline Triplet loss achieves the lowest accuracy (75.7% on average), indicating its limited ability to handle cross-style variation. InfoNCE and Circle loss provide clear improvements (83.2% and 85.1 %)., thanks to their better optimization of inter-class separation. Incorporating hard negative sampling further boosts performance for both Triplet and InfoNCE, but the gain is relatively modest (+5.1 and +2.7 points, respectively). suggesting that negative mining alone cannot fully ad- dress style discrepancies.
From Table 14
contrast, our proposed StyleNCE achieves the best results across all three query types, surpassing the best baseline (InfoNCE + hard negatives) by +1.9 points on aver- age. This demonstrates that StyleNCE not only benefits from hard negative mining but also explicitly models style-aware feature alignment, leading to consistent gains across diverse query styles.
H RETRIEVAL RESULT VISUALIZATION
H.1 RETRIEVAL RESULT VISUALIZATION ON DSR
To provide qualitative insights into model behavior, we visualize retrieval examples on the DSR dataset (Fi Typical errors are categorized into three groups: (a) action errors, where retrieved samples contain the correct object but incorrect actions; (b) object errors, where retrieved images contain semantically related but incorrect objects; and (c) buckground errors, where retrievals con- fuse similar contexts while missing the correct foreground. As shown Figure baseline methods frequently suffer from these mistakes, returning visually similar but semantically wrong samples. In contrast, our proposed Hystar consistently retrieves semantically accurate images across differ- ent styles, demonstrating its ability align fine-grained semantics under challenging cross-style conditions better.
(n) Retrieval action errors
(b) Retrieval with object errors
(c) Retrieval background errors
Tlyitar
Baseline
Hylitar
Tanelinn
Hylitar
Taseline
Slowtrh
myStor
Baseline
Art
Hlstta
Ttawiitee
Line Rec
Rllttrr
Taseline
Brssline
Baseline
Hylter
Qualitative retrieval examples on the DSR dataset. We illustrate three common error types made by baseline methods: (a) action errors, (b) object errors, and (c) background errors. Baselines often retrieve visually similar but semantically incorrect results, while our Hystar consistently re- trieves the correct matches, highlighting superior fine- grainerr alignment across multiple styles.
Figure 8:
H.2 RETRIEVAL RESULT VISUALIZATION ON DOMAINNET
We further evaluate retrieval results on the more diverse DomainNet dataset, which contains unseen styles such Clipart, Sketch, Painting, Quickdraw, and Infograph. Figure 9 10
show Top- 10
25

---

Published as a conference paper at ICLR 2026
retrieval examples. Baseline methods often fail under large style shifts, retrieving visually close but semantically irrelevant samples, especially in abstract styles such as Quickdraw and Infograph. In contrast, our Hystar maintains stable cross-style alignment, retrieving semantically correct results across multiple unseen styles. These results demonstrate the strong generalization ability of Hystar beyond the training distribution, confirming its robustness under zero-shot cross-style retrieval.
26

---

Published as a conference paper at ICLR 2026
Clipart
CLIP
CLIP
FreestyleRet
FreestyleRet
Hystar
Hystar
Sketch
CLIP
CLIP
X
FreestyleRet
FreestyleRet
X
Hystar
Hystar
Figure Qualitative Top- retrieval results on the DomainNet dataset across unseen styles (Clipart, Sketch). In the retrieval results figure, use the retrieval outputs from the baseline models, CLIP and FreestyleRet, as our baseline comparison.
27

---

Published as a conference paper at ICLR 2026
Painting
CLIP
CLIP
FreestyleRet
FreestyleRet
Hystar
Hystar
Quickdraw
Pri
CLIP
CLIP
X
FreestyleRet
FreestyleRet
Hystar
Hystar
Figure 10: Qualitative Top-10 10 retrieval results on the DomainNet dataset across unseen styles (Paint- ing, Quickdraw). In the retrieval results figure, we use the retrieval outputs from the baseline models, CLIP and FreestyleRet, as our baseline comparison.
28

---

Published as a conference paper at ICLR 2026
Infograph
CLIP
CLIP
FreestyleRet
FreestyleRet
Hystar
Hystar
Figure 11: Qualitative Top-10 retrieval results on the DomainNet dataset across unseen styles (In- fograph). In the retrieval results figure, we use the retrieval outputs from the baseline models, CLIP and FreestyleRet, as our baseline comparison.
29


Reviews:
[{"review_id": "85TELGlygX", "reviewer": "Reviewer_dZgs", "summary": "This paper presents Hystar, a dynamic multi-style retrieval framework designed to address challenges in Query-based Image Retrieval (QBIR) under significant style variations. Hystar achieves a balance between adaptability and stability in Parameter-Efficient Fine-Tuning (PEFT) by combining two key components: (1) hypernetwork-driven dynamic modulation and (2) static singular-value calibration. To tackle the limitations of standard contrastive losses that treat all negatives equally, the paper introduces the OT-weighted StyleNCE loss. This loss leverages Optimal Transport (OT) theory (via Sinkhorn iterations) to reweight negatives by difficulty: it amplifies the contribution of hard negatives while maintaining balanced batch-wise contributions, enabling effective capture of cross-style discrepancies. Extensive experiments on datasets including DSR and DomainNet demonstrate that Hystar consistently outperforms strong baselines.", "questions": "The proposed method does not appear to be limited to the Style-adaptive Retrieval task and could potentially be applied to similar tasks, such as image or video retrieval. Given the limited number of existing methods for direct comparison in the Style-adaptive Retrieval task, has the author considered evaluating the proposed method on these alternative tasks?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "* The use of singular-value modulation (SVD-based) is conceptually elegant and ensures stable, low-rank updates.\n* The StyleNCE loss introduces optimal transport weighting to emphasize hard cross-style negatives. It meaningfully improves robustness under distribution shifts and has solid theoretical motivation.", "weaknesses": "Many of the key techniques presented in this paper build upon prior research. The overarching design strategy of freezing most parameters while learning only small-scale incremental terms follows the mainstream PEFT framework, whose foundational works include [1] and [2]. Similarly, the OT-weighted StyleNCE loss is grounded in Optimal Transport (OT) theory and Sinkhorn iterations, both of which are well-established mathematical and algorithmic tools as detailed in [3].\n\n[1] Lingam, Vijay Chandra, et al. \"Svft: Parameter-efficient fine-tuning with singular vectors.\" Advances in Neural Information Processing Systems 37 (2024): 41425-41446.\n\n[2] Wang, Zhiwu, et al. \"Singular Value Fine-tuning for Few-Shot Class-Incremental Learning.\" arXiv preprint arXiv:2503.10214 (2025).\n\n[3] Jiang, Ruijie, Prakash Ishwar, and Shuchin Aeron. \"Hard negative sampling via regularized optimal transport for contrastive representation learning.\" 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023.", "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": "vAFumk5HXV", "reviewer": "Reviewer_vPC8", "summary": "This paper introduces HySTAR, a novel framework for query-based image retrieval (QBIR) designed to handle significant variations in query style (e.g., sketches, artwork, low-resolution images). The core problem it addresses is the poor performance of large vision-language models (VLMs) like CLIP when faced with stylistic distribution shifts.\n\nThe paper's two main contributions are:\n- Hypernetwork-driven Dynamic PEFT: A parameter-efficient fine-tuning (PEFT) method where a small hypernetwork dynamically generates input-specific modulations. These modulations are applied to the singular values (SVD) of the attention layers in a frozen VLM, conditioned on a style feature vector.\n- StyleNCE Loss: A contrastive loss function that uses optimal transport (OT) to re-weight hard negative samples, specifically designed to improve semantic alignment across different styles- first to do so for Image Retrieval. \n\nThe authors claim that this combination of dynamic, style-adaptive modulation and a style-focused loss function achieves state-of-the-art results on the DSR and DomainNet benchmarks.", "questions": "- Weakness 1): Can you please address the experimental confound of using DINOv2? To fairly evaluate HySTAR as a PEFT method, could you provide an ablation comparing the baselines (VPT, LoRA) to a version of HySTAR that does not use DINOv2, and instead derives its style vector $z$ from the VLM backbone itself (e.g., from CLIP)?\n- Re: (Weakness 2): What are the precise architectural details of the hypernetwork (input, hidden, and output dimensions)? And how exactly is the style feature $z$ extracted from the DINOv2 model?\n- Re: (Weakness 3): Beyond stability, what is the theoretical or empirical justification for SVD modulation being a superior method for style adaptation compared to other low-rank updates like LoRA?\n- Re: (Weakness 4 / Figure 7): Please clarify what the baselines are and make the figures readable to verify the claims of superior consistency.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Relevant Problem: The paper tackles a well-known and practical limitation of large pre-trained models: their lack of robustness to domain and style shifts. The application to cross-style image retrieval is a challenging and valuable research area.\n - Novel Methodology: The primary technical idea—using a hypernetwork to predict dynamic PEFT parameters based on the input's style—is a creative and novel contribution. Moving from static PEFT (like LoRA or VPT) to an adaptive, input-conditioned PEFT is an interesting research direction. The specific choice to modulate singular values (SVD) is also an unconventional and intriguing approach.\n - Well-Motivated Loss Function: The proposed StyleNCE loss is a strong contribution. The intuition that cross-style retrieval creates many \"hard negatives\" (e.g., a sketch of a cat vs. a photo of a tiger) is sharp. Using optimal transport to systematically identify and up-weight these hard samples is a well-founded and logical approach to improving fine-grained, cross-style discriminative power.", "weaknesses": "Despite the novel ideas, the paper suffers from several major weaknesses, primarily in the experimental design and the clarity of the method's description.\n- Unfair Experimental Comparison (Major Flaw): The central claim of HySTAR's superiority is built on a confounded experiment. As shown in Figure 1, HySTAR's hypernetwork is conditioned on features from DINOv2, a powerful, separate vision model. The baseline methods (CLIP, BLIP, LoRA, VPT, FreestyleRet) do not have access to these supplementary features. The performance gains attributed to HySTAR's architecture (dynamic SVD modulation) may, in large part, be coming from the simple fact that it is a multi-model system using rich DINOv2 features that the baselines lack. This is a critical confounding variable that makes the primary results in Tables 1, 2, and 3 uninterpretable.\n- Missing Architectural and Implementation Details: The paper is missing key details required for understanding and reproducing the method.\n  + Hypernetwork Architecture: The hypernetwork is defined in Eq. 2 as a simple 2-layer MLP, but its dimensions (input, hidden, and output) are never specified.\n  + Style Feature Extraction: It is unclear how the style feature $z$ is extracted from DINOv2. Is it the [CLS] token of the input image? An average pool of patch tokens? This is a crucial, undefined component of the model.\n- Insufficient Ablation Studies: The ablation study in Table 4, while showing the benefit of the \"Hyper\" and \"Static\" modules, is insufficient. There are no ablations on the hypernetwork design itself (e.g., depth, width) to justify the chosen (and unstated) architecture.\n- Limited Theoretical Justification for SVD: The paper's justification for using SVD modulation is thin. Appendix A argues that it provides stability by bounding the spectral norm of the weight update. However, it provides no theoretical or empirical intuition as to why modulating singular values is semantically the correct approach for style adaptation. Why is this superior to other low-rank updates like LoRA ($W_0 + BA$), especially when LoRA is more computationally straightforward (it doesn't require SVD on-the-fly)? The connection between \"visual style\" and \"singular values of attention weights\" is never established.\n- Errors and Poor Presentation in Results:\n   + Error in Table 2: As noted by the user, the paper incorrectly highlights its own score as \"best\" in Table 2. For the \"Infograph\" style (last column), the Top 1 accuracy for CLIP is 60.3%, which is higher than HySTAR's 59.3%. Yet, HySTAR’s score is bolded.\n    + Weak Visualization (Figure 7): The figures are extremely hard to read. It is also one of the most prominent results showcasing the comparison between baseline method and your method for retrieval across various styles. Also, it is not clear what is the baselines comparison? Is it zero shot CLIP? Or FreestyleRet? FreestyleRet has better retrieval results if so, than what is demonstrated in the comparison.", "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": "N84RHAacaA", "reviewer": "Reviewer_RvYN", "summary": "This work addresses the style distribution shift problem in query-based image retrieval (QBIR) (such as queries with styles different from those in the training data, like sketches and artworks), and proposes the Hystar framework. The Hystar framework includes two main components: 1. Hypernetwork-driven Dynamic SVD Modulation: A hypernetwork generates singular value perturbations (ΔS) in the attention layer based on the query style, achieving dynamic adaptation for each input. Simultaneously, static singular value offsets are used in the MLP layer to ensure cross-style stability.\n2. StyleNCE Loss Function: A contrastive loss weighted by optimal transport theory highlights indistinguishable cross-style negative samples, improving the model's robustness to style differences.", "questions": "1. This paper chose DINOv2 as the style feature extractor. Were other features considered (e.g., features from dedicated style analysis networks)? Do different feature choices significantly impact performance?\n2. How does Hystar handle styles that are completely new, extremely abstract, or bizarre in the training data? How does the model respond when the same query image contains a mixture of multiple styles?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The motivation of this work is good, which clearly points out the performance degradation problem of VLM under style distribution shift and emphasizes the static limitations of existing PEFT methods (such as LoRA and VPT).\n2. The experiment was comprehensive and convincing.\n3. The proposed method is efficient in parameters: Only a few parameters need to be fine-tuned.", "weaknesses": "1. This work emphasizes parameter efficiency, but lacks a quantitative analysis of the forward computational overhead of the hypernetwork (relative to static PEFT) (e.g., the percentage increase in inference time). This is important for evaluating its feasibility in real-time retrieval scenarios.\n2. Appendix A provides an intuitive theoretical explanation of SVD modulation (based on spectral norm constraints). However, further exploration is possible, for example, analyzing from the perspectives of generalization theory or manifold learning why SVD modulation is particularly effective for style adaptation.\n3. This work mainly focus on retrieval and zero-shot classification. Further validation of Hystar's generalization ability on other style-sensitive tasks can be achieved, such as stylized image generation guidance and cross-style visual question answering.", "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": "xm1ozu3EdP", "reviewer": "Reviewer_eVsi", "summary": "This paper introduces Hystar, a lightweight framework for Query-based Image Retrieval (QBIR) that addresses the challenge of handling stylistically diverse queries (e.g., sketches, artworks) which cause distribution shifts and performance drops in standard Vision-Language Models (VLMs) like CLIP. Extensive experiments show that Hystar achieves state-of-the-art performance on multi-style retrieval and cross-style classification tasks, offering superior parameter efficiency and stable performance across various query styles.", "questions": "please see above", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. Well-Motivated Problem and Thorough Literature Review: The paper adeptly identifies and tackles the critical, yet underexplored, challenge of style-diversified retrieval in QBIR. The authors provide a comprehensive overview of existing paradigms, such as LoRA-based PEFT methods and those relying on style-cluster priors, and convincingly argue their limitations in generalizing to unseen query styles. This strong motivation establishes a clear and valuable niche for their work.\n\n 2. Innovative and Principled Methodology: The core technical contribution is both novel and well-designed. The use of a hypernetwork to dynamically generate LoRA matrices is a clever adaptation. More importantly, the decision to restrict the hypernetwork's output to singular-value perturbations (∆S) is a key insight. This approach not only enhances training stability but also explicitly promotes generalization across diverse and unseen styles by focusing adaptation on a compact, semantically meaningful parameter space.\n\n 3. Compelling and Multi-faceted Empirical Validation: The authors provide rigorous experimental evidence to support their claims. Through comprehensive comparisons against strong baselines (e.g., FreestyleRet, VPT), they convincingly demonstrate the superiority of their hypernetwork-driven paradigm. Furthermore, the inclusion of t-SNE visualizations offers an intuitive and powerful qualitative analysis, effectively illustrating how Hystar learns more discriminative and style-invariant feature representations.", "weaknesses": "1. Terminological Imprecision: The term \"Vision-Language Models (VLMs)\" is used to describe CLIP. However, in contemporary literature, \"VLM\" often refers more specifically to models that include a text decoder for generative tasks (e.g., GPT-4V, LLaVA). CLIP, being a dual-encoder model designed primarily for contrastive learning and representation embedding, is more accurately categorized as a vision-language representation model. Adopting this more precise terminology would enhance conceptual clarity.\n\n 2. Insufficient Justification for Architectural Choices: The rationale behind using a hypernetwork to generate dynamic components for attention layers while using static offsets for MLP layers, as a specific form of decoupling, requires deeper analysis. The paper would benefit from a more thorough discussion comparing this design choice against other potential decoupling strategies. A theoretical or empirical justification for why this particular split (dynamic attention vs. static MLP) is optimal for the task of style adaptation would significantly strengthen the methodological argument.\n\n 3. Limited Scope of Ablation Studies: The ablation studies, while validating the proposed components, could be more comprehensive by including a wider range of Parameter-Efficient Fine-Tuning (PEFT) baselines. For instance, comparing against other adaptive tuning methods like (IA)³ or Adapter-based approaches would provide a clearer picture of Hystar's performance gains relative to the broader landscape of efficient adaptation techniques, not just the ones it directly outperforms.", "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)