WIMFRIS: WIndow Mamba Fusion and Parameter Efficient Tuning for Referring Image Segmentation
Authors: Seunghun Moon
Hyunwoo Yu
Haeuk Lee
Suk-Ju Kang
Venue: ICLR 2026 Poster (2026)
Keywords: Referring image segmentation
parameter efficient tuning
computer vision
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: This paper introduces WIMFRIS, a framework that achieves state-of-the-art in referring image segmentation by proposing a novel HMF neck module to efficiently fuse text with visual features , overcoming a key performance bottleneck in prior methods.

Abstract: Existing Parameter-Efficient Tuning (PET) methods for Referring Image Segmentation (RIS) primarily focus on layer-wise feature alignment, often neglecting the crucial role of a neck module for the intermediate fusion of aggregated multi-scale features, which creates a significant performance bottleneck. To address this limitation, we introduce WIMFRIS, a novel framework that establishes a powerful neck architecture alongside a simple yet effective PET strategy. At its core is our proposed HMF block, which first aggregates multi-scale features and then employs a novel WMF module to perform effective intermediate fusion. This WMF module leverages non-overlapping window partitioning to mitigate the information decay problem inherent in SSMs while ensuring rich local-global context interaction. Furthermore, our PET strategy enhances primary alignment with a MTA for robust textual priors, a MSA for precise vision-language fusion, and learnable emphasis parameters for adaptive stage-wise feature weighting. Extensive experiments demonstrate that WIMFRIS achieves new state-of-the-art performance across all public RIS benchmarks.

Published as a conference paper at ICLR 2026
WIMFRIS: WINDOW MAMBA FUSION AND PARAMETER EFFICIENT TUNING FOR
REFERRING IMAGE SEGMENTATION
Seunghun Moon"), Hyunwoo Yu" Haeuk Lee Suk-Ju Kang Haeuk
*Deaarmmett of Electronic Engineering. "Department of Artificial Intelligence 1.2 Sogang University, South Korea (moonsh hyunwoo137, leemail, i,, s jjkang) @sogang............. ac kr
ABSTRACT
Existing Parameter-Efficien Tuning (PET) methods for Referring Image Segmen- tation (RIS) primarily focus on layer-wise feature alignment, often neglecting the crucial role of neck module for the intermediate fusion of aggregated multi-scale features, which creates a significant performance bottleneck. To address this lim- itation, we introduce WIMFRIS, novel framework that establishes powerful neck architecture alongside a simple yet effective PET strategy. At its core is our proposed Hierarchical Mamba Fusion (HMF) block, which first aggregates multi-scale features and then employs a novel Window Mamba Fuser (WMF) module to perform effective intermediate fusion. This WMF module leverages non-overlapping window partitioning to mitigate the information decay problem inherent in State-Space Models (SSMs) while ensuring rich local-global context interaction. Furthermore, our PET strategy enhances primary alignment with a Mamba Text Adapter (MTA) for robust textual priors, a Multi-Scale Aligner (MSA) for precise vision-language fusion, and learnable emphasis parameters for adaptive stage-wise feature weighting. Extensive experiments demonstrate that WIMFRIS achieves new state-of-the-ans performance across all public RIS bench- marks. Code is available here
INTRODUCTION
Referring Image Segmentation (RIS) is a dense prediction task that generates precise pixel-level masks for objects described in natural language. Unlike semantic segmentation Xie al. (2021) });
Yan et al. 2202)) Fu et al. (2025); Shim et al. (2023): Kang et al. (2024); Yu et al. (2024) which classifies each pixel into fixed set categories, RIS requires jointly modeling capacity of fine- grained visual content and open- voccaullyy text semantics to localize the target instance. While early RIS methods Li al. (2018); Liu al. (2017) utilize separate backbones with late fusion mechanisms like fully connected or recurrent modules, frameworks following transformer architec- tures Dosovvissyy et al. (2020) leverage the cross-attention mechanism for more precise and robust lasion-language alignment.
Another line of work Xu et al. (2023) :: Wang et al. (2024b); Huang et al. (0025) has achieved great success by employing Parameter- Efficient Tuning (PET) approaches to adapt large, pre-trained back- bones such as CLIP Radford et al. (2021) or DINO Caron et al. (202)) I): Oquab et al. (2023) for the RIS task. These methods utilize lightweight trainable modules to efficiently adapt the powerful, pre-trained features, while keeping the vast majority of the backbone parameters frozen. These modules are specifically designed to efficiently refine and align the backbone's 's rich visual represen- tations with the fine-grained linguistic cues the referring expression, enabling precise pixel-level localization.
Despite their effectiveness, existing PET-based approaches predominantly focus on designing spe- cific adapters align textual and visual features from corresponding encoder layers. However,
"Equal contribution.
"orresponning author.

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Published as a conference paper at ICLR 2026
Table I: Analysis of neck module functioning. Omission of the neck module results in significant performance degradation, highlighting its critical role in performing intermediate fusion. Incor- porating our HMF-block into existing PET-based approaches further improves their performance. Furthermore, our WIMERIS utilizing proposed HMF-block and PET framework achieves state-of- the-art performance.
RelCOCO
Text
Vision Fnc.
Method
PET Params
vol
Insi
72.2
73.9
70.8
CLIP-VIT-B1E
ETRIS
1AM
ETRIS
74.5
72.9
Ours
75.7
77.5
73.1
CLIP-VT--/16
74.3
75.8
70.8
DINOV2-IV14
DETRIS
1.MM
DETRIS
76.4
75.8
77.7
72.9
TAM
Ours
77.2
74.3
these adapters are designed for basic, layer-wise fine-tuning, providing only a generic primary alignment. The task-specific intermediate fusion, which requires aggregating and contextualizing these individually aligned features from across all layers, therefore more distinct and criti- cal process. This highlights the importance of the neck architecture performing intermediate fu- sion. Consequently, the absence carefully designed neck can cause an information bottleneck, preventing the full potential of the rich, multi- modal features from being fully exploited and ultimately hindering performance. As shown in Table the omission of neck module leads to
Extsting Intermedditteeeeeeeeee.iiiiee Faston
substantial performance degradation, demon- strating the importance of not only performing layer-wise tuning, but also fusing the resulting features to achieve fine-grained, task-specific feature.
Vis. 1" Layer
CA
Vis. 2 Layer
CA
Comcatenate
Task Decoder
I
Vis. N Layer
CA
This paper proposes novel neck archi- tecture, namely Hicrarchical Mamba Fusion (HMF) block, that enables powerful intermedi- ate vision-language modality fusion. Our mod- ule be easily integrated into existing PET- based RIS methods and enhances the perfor- mance of existing approaches, as shown in Ta- ble T Existing neck architectures are subopti- mal because they are designed redundantly repeat the basic modality alignment already handled by the PET adapters each layer. In contrast, as illustrated in Fig. [ rather than fusing layer-wise features separately, our HMF block first aggregates the individually-tune sual features from backbone layers into a uni- fied,
(b) Our Intarmedione Fusion
Text
Vis. 1a Layer
Task
2dd Layer
Concatcnate
WMF
Decodar
Vis. N+ Layer
Comparison between previous interme- diate fusion and our approach. Instead of fus- ing layer-wise visual features with text prior sepa- rately using conventional cross-attentions (a), we first aggregate them for unified, multi-semantic visual representation and conduct modality fusion via our proposed WMF module in HMF neck (b).
Figure 1:
vi-
representation. It then per- forms a single, powerful intermediate fusion with linguistic cues with our novel Mamba-based Gu & Dao 2023) Window Mamba Fuser (WMF) module. Specifically, WMF module partitions the visual feature map into non-overlapping win- dows and prepends a shared global textual prior each window sequence. WMF processes these compact windowed subregions in parallel with a Mamba layer, effectively constraining the over- all sequence length. This mitigates the exponential decaying problem Wang al. (2024a), where SSMs inherently bias toward recent tokens as repeated hidden state updates attenuate information from distant sequential positions exponentially, thereby enabling effective modality fusion. Addi- tionally, attaching the global textual token to every window sequence ensures that each local region directly interacts with global linguistic context.
Moreover, we introduce a simple yet effective PET framework designed to empower the backbone adapters to more effectively perform their role of primary alignment. Specifically, we design the Mamba Text Adapter (MTA), text adapter utilizing the State-Space Model (SSM)-based Mamba block that provides an enhanced global textual prior with full sequence awareness and linear compu- tational complexity. Furthermore, leverages stip-convolutional Receptive Field Mixer (RFMixer) module to extract features across multiple receptive fields. It also employs multi-head cross-attention layer to interact with the
design the Multi-Scale Aligner (MSA), vision adapter that
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Vinad Farnes
dMeel Tocmal
Taxsbble
MSA
Froze
Extphesx
Black
Block
+
Block
Task Decoder
HMF
clephant facing
Block
e
BNook
+
Blook
Toura bentne
MIA
MIIA
MITA
(el Chemall Pramerork
Attentiam
Mixerr
Figure 2: (a) Overview of WIMERIS architecture. Frozen CLIP text encoder layers and DINOv2 vision encoder layers are parameter-efficient tuned by MTA (b) to get enhanced global textual fea- tures ft, and MSA (c) with learnable emphasis parameters and RFMixer (d) to obtain fine-grained visual features fe- Subsequently, our HMF block performs powerful rision-languaee intermediate modality fusion.
enhanced global textual prior, thereby improving primary modality fusion. Lastly, we adopt learn- able emphasis parameter before each vision adapter to consider the different contributions of each backbone stage. This allows the model assign an independent and adaptive contribution to each stage during training, ensuring that more informative stages can exert greater impact on the fused features. With only 1.3% to 2.2% backbone parameter updates, the proposed framework achieves more robust global textual prior and enables more effective layer-wise modality fusion.
Incorporating the proposed HMF block and the PET strategy, we introduce a novel framework for RIS, namely WIMFRIS (WIndow Mamba Fusion and parameter-efficient tuning for RIS). Our main contributions can be summarized as follows:
We find that existing PET-based RIS approaches focus on layer-wise modality fusion and neglect the importance of aggregated intermediate modality fusion, and we propose a novel HMF block designed for effective intermediate modality fusion with its core WMF module.
We propose a PET strategy that combines an SSM-based MTA for strong global textual pri- ors, an MSA with RFMixer module and cross-attention layer for precise vision-language fusion, and learnable emphasis parameters to adaptively weight each backbone stage.
We introduce WIMFRIS, a novel RIS framework leveraging our HMF and PET strategy that achieves new state-of-the-art performance in every RIS benchmarks.
2 METHODOLOOG
2.1 ARCHITECTURE OVERVIEW
As shown in Fig. (a), our proposed WIMFRIS is referring image segmentation framework leveraging simple yet effective Parameter-Efficient Tuning (PET) strategy and powerful intermediate fusion neck block. The core of WIMFRIS is the novel Hicrarchical Mamba Fusion (HMF) block visualized in Fig. 3 (b), an SSM-based intermediate vision lasion-language modality fuser that aligns global textual priors to window-partiioneed local visual features with proposed Window Mamba Fuser (WMF) module. addition, we freeze the pretrained CLIP Radford al. (002 text encoder and DINOv22(Qqaa et al. (2023) encoder, then fine-tune them using two PET modules. The Mamba Text Adapter (MTA) employs an SSM-based Mamba block Gu & Dao (003) to consolidate long- range dependencies across text tokens into global textual prior. The Multi-Scale Adapter (MSA) aligns stage-wise visual features with that enhanced prior through the RFMixer module and cross attention layer while using learnalle emphasis parameters to adaptively modulate its contribution.
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2.2 PARAMETER- EFFICCEET TUNING STRATEGY
Although DINO encoder is powerful, its unimodal pretraining lacks visual-text aligning capacity. So employ a lightweight MSA adapter module that leverages outputs of the CLIP text encoder to parameter-efficient tune DINO vision encoder with effective modality aligmment, However, directly using CLIP outputs without further refinement is suboptimal for RIS, which inherently requires precise global contextual text representations. Thus, we utilize Mamba-based MTA module that explicitly strengthens the long-range dependencies among text tokens. By doing so, MTA gener- ates an enhanced global textual prior, which is then fused with layer-wise features of DINO vision encoder via our carefully designed MSA.
Mamba Text Adapter (MTA). As illustrated in Fig. 2 (b), MTA produces text token which can be leveraged as a strong referring context guidance for modality alignment. Given a text token x E R RL,x where Le and C₁ denote the sequence length and the channel dimension at the I-th layer of the CLIP text encoder respectively, the MTA produces refined output x, out with enhanced long-range contextual dependencies. Our MTA can be mathematically formulated as follows:
x = ReLU (Down(x))),
X|_SSM = SSM( (Canooooooooniiiiiiiii (x),))))))
xLues oSiUU (Projin (xi_so))
x = UU (Projout (x x(res) + x x(_
where (-) and (-) denote the ReLU and SiLU activations, respectively. Down(-) and Up(-) are the down-sampling and up-sampling linear operations that reduce and restore the channel dimension of the input. Projin()) and Projout() (-) denote the input and output projection layers for the hidden state of the SSM. ConvID( is a ID convolution. By leveraging SSM-based scanning mechanism, MTA captures rich global text semantics with linear computational complexity, ensur- ing efficient modeling of long-range dependencies. In addition, our MTA consistently improves vision-language modality alignment by furnishing enhanced global textual prior to modality fusing modules.
Multi-Scale Aligner (MSA). The proposed MSA is designed, illustrated in Fig. (c) and (d), around two core components, the RFMixer and the cross-attention block. This adapter module takes input the i-th layer vision feature x H xC, where H, W and C, refer to the height, width and the corresponding layer channel dimensions, respectively. It also receives the textual features fe produced by the MTA-fine-tuned CLIP text encoder as global textual prior that enriches the vision-language modality alignment. The RFMixer consists of multiple branches of depth- wise strip convolutions, enabling to efficiently capture multi-scale contextual information. Specifically, RFMixer can be defined as follows:
DWConve (DWConvixe(
x = TReLU x + Exfee)
(2)
km1
X
x"
Ximi
where DWConverxes (.) denotes the depth-wise 2D convolution with kernel size (c1,02), and @ refers to the Hadamard product. By mixing depth-wise convolutions of varied kernel sizes, the RFMixer aggregates information across diverse receptive fields, capturing both fine-grained details and broader context in a single, efficient module. This multi-scale interaction strengthens the visual features, making them more effective for the subsequent alignment with linguistic cues. We then leverage the RFMixer-enriched visual feature x xunn as the query in a cross-attention block, with the MTAAenhanced global textual prior ft serving both key and value to obtain the aligned fea- ture xy ".ffueed This mechanism enables more robust modality alignment and effectively integrates linguistic context into the visual representation.
Emphasis Parameter. As numerous studies Lin et al. (201)): Raghu et al. 2021); Ghiasi et al. (2022); Villa et al.) (2023) have demonstrated that multi-layer visual backbones capture distinct layer-wise contextual information. Therefore, simply summing each feature map and its adapter
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11EM
Cony
Cony
Cony
Depal
f
Winduw Mamba
Paser
Paiot
Conv
Window
Figure 3: (a) Our HMF block designed for robust and effective intermediate Eision-language modal- ity fusion. It first aggregates the multi-layer vision features using convolutional layers and fuses them with global text class token with WMF module. (b) The WMF module partitions the vi- sion-text feature map into non-overlapping windows and prepends shared global text class token each window. It then performs window-wise SSM scans and reassembles the fused outputs through attentional gating.
output is suboptimal, some layers require stronger fine-tuning than others. To address this, we adopt simple learnable emphasis parameter that adaptively modulates the contribution of cach layer when applying MSA during fine-tuning phase. We parameterize the emphasis gating via scalar logit and a sigmoid activation, initialized as o(p) = ag- The learnable emphasis parameter mathematically defined follows:
= logit(ao) = In app
1- okp
0 Sigmoidd
1 (0,1),
f = x + xu.fuedd
where fü denotes the stage-í fused visual feature. Rather than naively utilizing single trainable parameter, this gives us two key advantages. First, the sigmoid activation ensures the numerical stability that a always lies strictly between and I, preventing the adapter branch from exploding or inverting. Second, enables adaptive modulation of the fine-tuning strength for each stage, ensuring that the stages capturing different contextual information can be fine-tuned dynamically as required.
2.3 HIERARCHICAL MAMBA FUSION (HMF) BLOCK
Our HMF block, shown in Fig. 3 (a), is designed to conduct robust and effective intermedi- ate vision-language modality alignment. HMF block first aggregates these features into a single fine-grained visual representation. It then fuses this representation with the textual class token via our carefully designed SSM-based Window Mamba Fuser (WMF) module. The WMF module parti- tions the visual features into non-overlapping windows. effectively constraining the spatial sequence length. After global textual class token is prepended to each window, the resulting sequences pass through the Mamba layer for strong, hindow-wise local-global modality alignment.
Feature Aggregation. The feature aggregation step consolidates independently processed multi- scale information into a single, unified feature map. Specifically, we first extract three feature maps, f. {1, 2, 3). from two intermediate layers and the final output of the MSA-tuned DINOv2 Oquab et al. (2023) vision encoder. These features are then concatenated along the channel dimen- sion and projected through x convolution, yielding unified feature map f e IR #xWxC The rich multi-scale contextual information consolidated in this feature map allows the WMF module to perform more robust modality fusion with the global textual prior.

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Published as a conference paper at ICLR 2026
Intermediate Fusion. As illustrated in Fig. (b), WMF module is carefully designed for SSM- based vision-text modality fusion. Simply concatenating vision-language feature generates long sequences, resulting in exponential decay which leads to suboptimal modality fusion. To address this, we adopt non-overlapping window partition strategy and incorporate text information to every partitioned window. Our method not only limits the sequence length for each SSM sean but also enhances cross-mes mossl fusion capacity. Specifically, we partition f into Twin = H W/M non- overlapping windows of size M w/. Wws producing
sindoo--artitioned visual feature map fa E R x MxC, This window partitioning effectively limits each hidden state updating range, ensuring that each SSM scan only processes the tokens in window- wise manner. We then expand the textual class token fcla E RIIC to sele' E R and concatenate to each window sequence f funn forming compact sequences 2, E R(M+1)x for window-wise SSM scanning with each visual patch retaining a strong connection to the textual prior. This process can be defined as follows:
3. = ssek (unin [i] j= 1=1,...,
Twins
X = (0z)/=in E Bows x xd1+1)xCC
(4)
Y (X), R
where [: denotes the concatenation and SSM(-) refers the selective scanning operation that captures long-range contextual information. Since each sequence length is fixed to M + 1, WMF prevents excessively long sequences as full-resolution flattening, effectively mitigating the expo- nential decaying inherent to SSMs. Moreover, by prepending textual class token to every window, WMF maintains a strong textual prior across all local regions, enabling robust and fine-graine ane-gne modality fusion. Note that SSM is applied in parallel across all windows, so each subregion is pro- cessed concurrently. After running these sequences through SSM, we split Y back into the fused visual features f: E R" XXCC and the updated textual class token f f!uued E R" xIxc. As result, our WMF module delivers robust vision-text modality fusion with controlled sequence length, significantly alleviating the exponential decay.
2.4 Loss FUNCTION
We employ a text-to-pixel contrastive loss Wang et al. (0222) Loon train WIMFRIS and to pull corresponding text and pixel features together while pushing apart all non-matching pairs. Let P and A be the sets of positive and negative pixel indices for a given expression. For each pixel é, we denote by Z₁ E IRC the text feature and by Z E RC the per-pixel loss as follows:
the visual feature at the pixel level i. We define
log(o -Z)), log (1 - (Ze Z:)).
ie P,
Doon (Z Zc)
(5)
i i,
The overall contrastive objective averages over all labeled pixels:
Leuu (Z,, Zz)
1 IPUN
Σ JEPUN
from (Z,, Z.).
(6)
Minimizing Loon encourages the text-pixel pairs to have high similarity while simultaneously dis- couraging any unrelated text-pixel alignments. We also incorporate two auxiliary losses to better supervise segmentation mask quality and cross-modal consistency. To directly optimize the mask overlap. we define the Dice loss. Let p. E [0, 1] be the predicted probability at pixel and g: {0, 1} the corresponding ground truth mask label. Denoting p and g for the flattened prediction and the ground truth vectors of length M, we compute the following dice score:
2 M
(6)
(b)
P1
g
Dice g)
M 1==1
(b)
Σ
M 66)
P.
-== 9
where = = 1e - 6 for numerical stability. The Dice loss is then computed as follows:
B
Laice =
1
(Dico(p (b)
(g)g())) ()))
(8)
B b=1

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Published as a conference paper at ICLR 2026
which encourages maximal overlap between the predicted and true masks. To align the per-window visual activations with the referring expression, we first project each of the class token outputs from our WMF module against the global textual prior. Denote by L [e..... [ E R Bx......
with ; = (c,, s), where ç the j-th class token and E RC the pooled text state. In parallel, we downsample the ground truth mask G to the same spatial grid (h, w) via nearest-neighhor interpo- lation and then aggregate into non-overlapping windows of size (Wh, (w) by max-pooling, yielding
a binary mask vector m = [m....... MRwin E {0, 1wawin Then we apply a per-window binary cross-entropy loss as alignment loss:
B
Laiggn
BCE(((5), m()))
B Alwin
where BCE denotes the binary cross entropy loss. Summing all components into the final training objective:
Liotal
Acon
Lcon Adice Liiie Aalign Latign-
(10)
This combined loss encourages accurate mask prediction, maximizes overlap, and enforces precise text-pixel alignment.
EXPERIMENTAL RESULTS
3.1 DATASETS
We conducted our experiments on three widely adopted referring image segmentation bench- marks for WIMFRIS. RefCOcO Kazemzadeh et al. (2014) comprises 19,994 images sourced from MSCOCO Lin et al. (0114) with 142,210 referring expressions for 50,000 object instances. The dataset is split into 120,624 images for training. 10,834 for validation, 5,657 for test A, and 5,095 for test B, respectively. Each expression averages 3.6 words, and every image depicts at least two objects. RefCOCO+ Kazemzadeh et al. (2144) contains 19,992 images and 141,564 expressions referring 49,856 objects, divided into 120,624 training. 10,758 validation, 5,726 test A and 4,889 test B samples. By excluding absolute location terms, RefCOCO+ poses a more demanding chal- lenge. G-Ref Yu al.] (2016) comprises 26,711 images annotated with 104,560 expressions for 54,822 objects. These expressions were collected via Amazon Mechanical Turk, average 8.4 words in length, and include more detailed spatial and appearance descriptions. We report results using both the Google and UMD splits for G-Ref.
3.2 IMPLEMENTATION DETAILS
We implemented two WIMFRIS variants, each paired with different DINOv2 Oquab et al. 2023)
backbone. For WIMFRIS-B B we employed DINOv2-B/14, and for WIMFRIS-L L we used DINOv2-
L/14. In both cases, the referring expressions were encoded by the CLIP Radford et al. (202I)
text encoder, and all input images were resized to 448 448 pixels. For visual- visual-linguistic modality fusion, we inserted MSA at layers 1, 3, 5, 7, 9 and 11 in WIMFRIS-BB B, and at layers 2, 6. 10, 14, 18 and 22 in WIMFRIS-L. respectively. The loss weights Acon, Adices and Aulign and 0.2, respectively. MTA was also applied at layers 1, 3, 5, 7, 9 and 11 of the CLIP text encoder in both WIMFRIS-B B and -L configurations. Following DETRIS Huang al. (2025). we initialized ao to 0.2. We trained WIMFRIS for 50 epochs under the Adam optimizer Kingma 2014) with an initial learning rate of le-4 for WIMFRIS-B B and 2e-4 for WIMFRIS-L. which were reduced by a factor of 10 at epoch 35. We conducted all experiments with NVIDIA RTX4090 GPUs with a batch size of 32 for WIMFRIS-B and 64 for WIMFRIS-L. We evaluated the model performance using the mean Intersection- crresectionnover--NNe Union (mloU) metric between the predicted masks and the ground-truths following Wang et al. 2022). We leverage the task decoder from DETRIS, replacing cross-modal neck with our WMF
set to 0.5, 0.3,
3.3 COMPARISON WITH STATE-OF-THE-ART METHODS
We evaluated WIMFRIS on RefCOCO, RefCOCO+ and G-Ref, as well as on the mixed RefCOCO dataset. The results, including comparisons with both full fine-tuning and PET methods, are shown
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Table 2: Comparison State-of-the-art RIS methods and the PET RIS methods on RefCOco, RefCOCO+ and G-Ref datasets without using extra data and Mixed RefCOCO dataset, evaluated using the mloU metric. Models marked with are trained on the mixed RefCOCO, RefCOCO+ and G-Ref data. The best results are written in bold.
RefCOCO
RefCOCO+
G-Ref
Method
Avg
val
lestA
tesals
val
lestA
testB
val(u)
(est(
valkg)
Foll Finc-tuning
ReSTR Kim et al.
2022
68.4
72.1
66.4
57.9
61.5
50.9
56.2
56.9
61.3
LAVT
Yang di
2721
74.5
76.9
70.9
65.8
71.0
59.2
63.3
63.6
63.7
67.7
CrossVLI Cho al.
2125
75.5
77.5
72.7
67.3
72.0
60.1
66.2
62.1
69.2
7hD
73.5
63.9
63.1
66.8
ReLA/LLu al 2726
75.6
77.8
72.8
70.4
74.8
63.9
68.77
69.6
66.9
71.2
Lang
76.9
73.3
LJSA--B [ai of
M124
74.9
79.1
72.3
65.1
70.8
58.1
67.9
70.6
69.9
MagNel hay al.
A124
76.6
78.3
72.2
68.1
73.6
61.8
67.8
69.3
65.3
70.3
ReMamher
2024
71.6
73.3
68.4
61.6
54.0
61.1
64.6
RISCLIP-B
Kim al
2025
75.7
78.0
72.5
69.2
73.5
60.7
67.6
68.0
70.7
RISCI.IP-1
Parameter Elficient Tuning
ETRIS
20233
66.9
Rarl 2TTa
Wang al.
2024h)
72.4
75.9
68.3
65.0
70.8
56.9
63.4
63.8
61.6
66.5
DETRIS-B Hunnn et
2025
76.0
78.2
73.5
68.9
74.0
70.4
DETRIS-I. Fmang
2115
77.3
79.0
75.2
70.8
75.3
64.7
69.3
70.2
72.2
Ours-B
77.3
79.2
74.8
70.2
75.1
63.5
69.2
69.4
67.1
71.8
With Mixed Training Data
PolyForme-11 Liu et al
2023b
76.9
78.5
74.8
72.2
15.7
66.7
72.2
71.2
73.5
UNINEXT
Yan
2025
81.2
82.4
79.1
72.6
76.3
66.8
74.1
75.4
76.0
DETRIS
Huang et al.
2025
81.0
81.9
79.0
75.2
78.6
70.2
74.6
75.3
77.0
Ours-I."
81.8
82.8
79.7
76.6
80.5
72.5
75.4
75.9
78.2
in Table On the standard splits (i.e., RefCOCO, RefCOCO+ and G-Ref), WIMFRIS-B achieves an average loU of 71.8, surpassing most of prior PET methods and matching all full fine-tuning methods. Scaling up WIMFRIS-L further raises average IoU to 73.4, setting new state-of- the-art across all full fine-tuning and PET approaches. When trained on mixed RefCOCO datasets, WIMFRIS-L reaches the average IoU of 77.7, achieving higher performance compared previous methods. These results underscore the ability of WIMFRIS to align and fuse visual and linguistic features robustly, enabling effective handling of complex referring open- open-vocabulary expressions and dense predictions compared to previous methods.
3.4 ABLATION STUDIES
Effects of Window Size of the WMF Module. Table reports ablation study the effect of the window size. The first row corresponds to naively concatenating all visual and textual tokens without window partitioning, which yields the lowest performance, achieving IoU of 75.7 on val, 77.3 on test A, and 72.6 on test B. As the window size decreases, the number of windows increases, well the number of enhanced global textual priors prepended. When we the window size of 4 x 4, WIMFRIS achieves its optimal performance. It is worth noting that applying the window size of spatial context among patches caused by the overly small window size. These results demonstrate that naively concatenating multi-modal features exposes SSMs to excessive exponential decay and weakens modality alignment, while window partitioning effectively constrains sequence length and preserves cross-modal interactions.
degrades performance. We attribute this to the excessive fragmentation of
Effects of our PET Strategy. We performed an ablation study the RefCOCO validation split to verify each component of our PET strategy and the results are shown in Table (b). The results demonstrate that each proposed module, the Multi-Scale Aligner (MSA), Emphasis Parameter (EP), and Mamba Text Adapter (MTA), incrementally and effectively contributes to the RIS performance. The MSA itself provides performance gain, validating its ability to capture multi-scale context via
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Table Ablation studies on the window size of the WMF module and the PET strategy components.
(a) Window size. denotes that simply catenated the multi-modal tokens without window partitioning. The second smallest window size of 4 x 4 yields the best performance.
(b) PET strategy. MSA, EP, and MTA refers to Multi- Scale Aligner, Emphasis Parameter, and Mamba Text Adapter, correspondingly. Params denotes the trainable parameters of the backbone model.
Window
RefCOCO
MSA
MIA
Params
ReCCOCO
Size
lestA
test3l
15.7
77.3
72.6
Defxult
X
x 16
76.3
738.5
73.8
V
0.8M
76.4
78.2
73.9
8
76.8
78.8
74..
x
2:2M
76.8
75.7
74,2
74.5
79.2
the RFMixer, Performance is slightly improved by the addition of EP, validating its crucial auxiliary role adaptively weighting the contribution each backbone stage. addition, the MTA proves effective, underscoring the importance of its SSM-enhanced global textual prior. The full PET framework combining all three components achieves the best results, showing that our complete PET configuration is the most effective approach.
3.5
QUALITATIVE RESULTS
4 presents the visualized results of five challenging scenarios: cluttered table settings, overlapped people, occluded objects behind netting, reflected animals, and densely arranged objects. Panel (a) and (b) shows the raw image and the ground truth segmentation mask, while panel (c) displays the results of DETRIS. Panel (d) and (e) shows the results of WIMFRIS. Across all challenging cases, including densely cluttered and severe occluded scenes, WIM- FRIS better captures open-vocabulary expres- sions and produces finer segmentation mask compared to DETRIS by effectively aligning multi-modal features. WIMFRIS successfully distinguishes the target from similar nearby ob- jects, guided by its enhanced global textual prior.
Fig.
"veplish muglin fvek"
Alre shire butind catcher
"lanp"
klty roflection
14] Image
(d10an-B
OureL
CONCLUSION
Figure 4: Qualitative results of our WIMFRIS compared with DETRIS by the input images and the referring text expressions. WIMFRIS better references the textual expressions and produces more precise segmentation masks.
We introduce WIMFRIS, novel Referring Image Segmentation (RIS) framework featur- ing two parameter-efficient adapters, MTA and MSA, and new neck architecture, the Hierar- chical Mamba Fusion (HMF) block with Win- dow Mamba Fuser (WMF) module. Our WMF module leverages windowed design overcome the exponential decay limitations of SSM-based fusion, while our adapters provide richer multi- modal context with minimal parameter updates. Extensive experiments on standard benchmarks validate our approach, demonstrating that WIMFRIS surpasses existing PET and full fine-tuning methods set state-of-the the--thhrttttt- art for efficient and accurate RIS.
LIMITATIONS AND FUTURE WORKS
While WIMFRIS demonstrates state-of-the-an performance, we identify several promising avenues for future work. These include investigating the generalizability of HMF block and PET strat-

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egy other dense prediction tasks to validate their versatility, exploring more efficient methods for bi-directional information flow -ision-to-text) without compromising parameter efficiency, and adapting the framework to the challenging frontier of zero-shot referring image segmentation.
ACKNOWLEDGMENTS
This work was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (In- formation Technology Research Center) support program vised by the IITP (Institute for Information Communications Technology Planning Evaluation) (33%), The National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2224-004142235 (33%), Development of an analog-digitee mixed ultra-low power neuromorphic edge SoC (RS-2025 200-20222222000000 14799-01), the Sogang University Grant of 202512025.00 and Institute of Information commu- nications Technology Planning & Evaluation (ITPP) grant funded by the Korea government (MSIT) (RS-2022-00143911 Al Excellence Global Innovative Leader Education Program).
super-
(33%), Samsung Electronics Co., Ltd (IO251218
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A RELATED WORKS
A.1 CTAAMETER-EFIICIAE TUNING (PET) FRAMEWORK FOR REFERRING IMAGE SEGMENTATION (RIS)
Chaameter-fffiiienn Tuning (PET) approaches adapt pretrained models with minimal updates, re- ducing compute and memory overhead. Unlike full fine-tuning, which updates all weights, PET restricts updates to small adapter modules. Common strategies include adding task-specific modules or adapters Houlsby et al. (2019): Li & Liang (202T): Zhou et al. (2022). sparse parameter updates 20021 Guo et al. (2020), and low-rank factorization Hu et al. (2022); ; Karimi Ma- habadi et al. 2021 Hao et al. (2023: Liu al. (024a)). Recently, PET has been extended to dense vision- language tasks such as Referring Image Segmentation (RIS). For example, ETRIS Xu
Zaken et al.
et al. (2023) introduces lightweight adapter called Bridger for cross-modal interaction and lever- ages spatial priors to refine intermediate features. BarleRla Wang et al. (2024b) applies PET to CL.IP Radford et al. (2021) encoders, addressing early fusion limitations and the lack of multi-scale
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modeling. DETRIS Huang et al. (2025) employs dense interconnections between the backbone lay- ers to improve feature propagation and mitigate alignment issues on encoders like DINO (quab et al. (002)). These PET frameworks highlight the growing interest in efficient adaptation for dense vision-language prediction tasks.
A.2 STATE-SPACE MODELS (SSMS) FOR VISUAL APPLICATIONS
State-Space Models (SSMs) Gu et (2021) Smith et al. 2022) have recently emerged as powerful architectures for capturing long-range dependencies with linear computational complexity, com- pared to the quadratic complexity of transformers. These models, notably exemplified by Mamba Gu & Dao (2023), have expanded from their origins in natural language processing Mehta et al. (2022) to various visual tasks Zhu et al. (2024); RRan & Xiang (2024). In visual applications, SSMs typically address the inherent spatial and sequential dimensions of data through specialized scan- ning techniques. For example, VMamba Liu al. (2024b) employs cross-scan modules to integrate spatial context effectively, overcoming the limitations of traditional state-space methods in non- causal spatial data. ReMamber Yang et al.] (2024) introduces a multi-modal extension of Mamba tailored for RIS, leveraging a unique twisting mechanism that fuses textual and visual features by sequentially processing hybrid feature cubes across channel and spatial dimensions. Furthermore, recent frameworks such as MFuser Zhang & Tan (2025) utilize hybrid attention-Mambl modules to bridge the gap between vision foundation models and vision-language models, providing a ro- bust solution for
semantic segmentation by enhancing cross-modal alignment and spatial granularity. These advancements highlight the effectiveness and adaptability of SSMs, particularly efficiently addressing the complex spatiotemporal dynamics of modti visual tasks.
B PRELIMINARIES
B. STATE SPACE MODELS (SSMS)
SSMs model a hidden state h(t) E RN evolving over time map an input sequence xxt) R to an output v() E R. In continuous form, they are represented by the following ordinary differential equation:
(t) Ah(() + Bx(t), y(t Ch((),
(11)
where E R governs the internal state transitions, B Rxx1 projects the input the state space, and C E pIxN maps the hidden state to the output.
To apply such models in deep learning systems, these continuous-time equations commonly discretized via Zero-Order Hold (ZOH) method with step size A yielding discrete forms of A and B:
A (A), ((x)) (AA) - 1) AB, B (AA) (exp
(12)
and the resulting discrete-ting time state-space equations are written as:
h4 = Ah4-1 + Brg.
(13)
yt Ch+-
Classical SSMs employ fixed aggut-agiostic parameters, whereas recent advances such as Mamba Gu & Dao (202)) introduce data-dependent parameterization. Specifically, Mamba dynamically generates B and C from the input and adapts A per channel enabling input-dependent dynamics with linear-time computational complexity.
B.2 EXPONENTIAL DECAY IN SSMS
Although discrete SSMs enable efficient linear- time sequence modeling, they suffer from a fun- damental exponential decaying property that undermines long-range dependencies. Consider the
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Figure 5: Additional qualitative results on RefCOCO dataset.
discrete recurrence:
ht A + B rt:
ho =0 0,
(14)
where NNN and B Nx1, Unrolling over steps gives the closed-form
hr Σ A 1-k BBBBag BIr.
(15)
For simplicity, we assume an induced matrix norm so that whenever the spectral radius (i.e. the maximum absolute value of the eigenvalues) of A is below one, there exist constants M > 0 and A E (0, 1) satisfying ||A'|| S MX" for all n, and we further assume zero initial state ho = 0 with fixed A and B throughout the sequence. Taking norms and using submuliipliativity (i.e.. |XY|| s [X]]) we obtain
|/ell| -
A
(16)
If p(A) < 1 (as ensured by A exp(AA) with A
0. then there exist constants M > 0 and
E (0, 1) such that
| S MX", vn 2 0.
(17)
Substituting yields
/hell S M B
|
(18)
Because X t-k decays geometrically as the gap / k grows, inputs 3e from even moderately distant timesteps contribute only tiny fraction to h₁ Specifically, the information from past inputs decays an exponential rate. In practice, this decay means that early tokens, whether visual patches or words in a referring expression, are effectively invisible to later states, unless very large weights or additional architectural mechanisms are introduced. Mitigating this exponential attenuation therefore essential for tasks requiring strong long-range and cross-modal integration.
C ADDITIONAL QUALITATIVE RESULTS
In addition to the qualitative results in the main paper, we further display more RefCOCO Kazemzadeh et al.] al.] (2014); Yu et al. (2016) qualitative results on various referring expressions across diverse scenes in Fig. [] The results demonstrate that WIMFRIS effectively identifies and segments target objects even challenging scenarios such as occlusion the first row, complex backgrounds in the second row, and multiple similar instances in the sixth and seventh rows. Notably, in scenes with fine- reeegraaied distinctions, such as differentiating among people in lab coats in the third row or distinguishing between similarly coherent masks aligned with the referring expressions. These sults highlight the robustness of our WIMFRIS and its capacity to localize referred objects with high fidelity, validating its superiority in understanding nuanced textual cues and resolving ambiguities in cluttered visual environments.
D ADDITIONAL ABLATION STUDIES
Effects of Kernel Size of the RFMixer. We conducted an ablation study on depth wise strip convolutions in the RFMixer determine the optimal kernel sizes, summarized in Table
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Table 4: Ablation study on the kernel sizes of the convolutional branches in the RFMixer. Using kernel sizes of 3, 5, and in the RFMixer branches yields the best performance.
Kernel
RefCOCO
Params (M)
Size
val
testA
test
1-1-1
3.02
76.7
78.9
74.7
3-3-3
3.03
76.9
79.2
74.6
5-5-5
3,04
77.0
79.1
74.6
7-7-7
3,05
77.0
79.0
74.3
1-3-5
3.03
77.2
79.0
74.8
3-5-7
3.04
77.3
79.2
74.8
IWCDCC
ParCOCO+
G-Refbu
G-tarigi
0.25
025
022
0.2
0.2
022
839
0:3
6:3
039
DID
D10u2
DRO02 Black
Figure 6: Visualization of the learned a values for the emphasis parameter. The plots show a consis- tent downward trend across all datasets, indicating that the model learns apply stronger fine-tuning to the early, low-level feature layers of the DINOv2 backbone and progressively less to the deeper, high-level semantic layers.
Here, a kernel configuration of a- b denotes three convolutional branches with depth-wise strip kernels of size b, and respectively. Experimental results show that the 3 - 5 kernel sizes delivers the highest loUs. These results validate that leveraging multiple receptive fields via the RFMixer effectively captures rich multi-scale contextual information, yielding fine-grained visual features for dense prediction for referring image segmentation.
Emphasis Parameter Analysis. We visualize the learned a values of the emphasis parameters after training WIMFRIS-B to analyze the layer-wise contribution of our Multi-Scale Aligner (MSA). As shown in Fig. 6 there is a consistent downward trend in values across all datasets as the DINOv2 block stage deepens. The values are highest the initial layers and progressively decrease towards the final layer. This indicates that model adaptively applies more intensive fine-tuning to the early layers, which capture more generic, low-level visual features such as colors or edges. Conversely, the high-level semantic representations from the deeper layers of the self-supervised DINOv2 backbone are already robust and require less task-specific adaptation. This result validates the efficacy of our learnable emphasis parameters, which enable the model dynamically allocate tuning strength where it is most needed for effective vision-language alignment.
Adapter Placement Analysis. We conducted an ablation study comparing default adapter dis- tribution against depth-specific configurations to investigate the optimal placement strategy for our PET adapters. We grouped adapters into low-level (layers 0-5), mid-level (layers 3-8), and high- level (layers 6-11) concentrations while maintaining constant parameter budget, and compared them with our default placement configuration (layers 1, 3, 5, 7, 11). As presented in Table 5) the default configuration consistently yields superior performance compared to all concentrated configurations. This validates that the comprehensive adaptation of hierarchical representations is the most effective, ranging from low-level visual details in shallow layers to high-level semantic ab- stractions in deep layers. Concentrating adapters at a specific depth restricts the model to fine-tune this full spectrum of information. Furthermore, this distributed placement validates the synergy with our Emphasis Parameters (EP), as it provides diverse multi-level inputs that allow the EP module to dynamically optimize the contribution of each layer for effective lisiun-language alignment.
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"reffig
"driver"
"person black uubrella"
right Indy"
"drink above plate"
Prediction
Figure 7: Notable failure cases. Erroneous prediction masks produced when the target object is excessively large (first row), when the context necessary to specify the target object not directly provided or ambiguous within the referring expression (second and third row), when the required specifying context is spatially distant from the target object (fourth and fifth rows).
E NOTABLE FAILURE CASES
In this section, we present failure case analysis of our work for future works. As shown in the first row Fig. 7 erroneous masks occurred when the target object is excessively large, spanning multiple windows. Since WMF partitions the visual features into non-overlapping windows and pro- cesses them in parallel to mitigate exponential decay, the model identifies the object as fragmented patches rather than unified one. In the second and third rows in Fig. 7 WIMFRIS incorrectly segmented the target object when the referring expression ambiguous or lacks specific context, since each window independently aligns its local visual features with the shared global textual prior in our HMF neck. In the fourth and fifth rows in Fig. / when the visual context required to dis- ambiguate the target is spatially distant and located in different window, WIMFRIS yielded some
17

---

Published as a conference paper at ICLR 2026
Table Ablation study on the Impact of PET adapter placement strategies. Default 1,,,,,,7,9, 11] configuration yields the best performance. Note that the layer number is zero-indexed.
Adapter
RefCOCO
Placement
val
testA
testB
0, 1, 2, 3, 4, 3, 4, 5, 6,7,8 6, 9, 10,
76.6
78.4
74.1
76.8
78.3
74.1
76.6
78.6
73.8
7,9,5,7,9.11
77.3
79.2
74.8
failed prediction. This is due to the spatial isolation inherent in window partitioning where the win- dow containing the target object cannot directly access the visual features of the context object in separate window. Given the aforementioned limitations, future works are encouraged to investigate carefully designed fusion module capable of bridging the spatial isolation of window partitioning, thereby enabling better understanding of large objects and distant visual contexts.
F STATEMENT ON LARGE LANGUAGE MODELS USAGE
In the interest of full transparency and adherence to the ICLR 2026 policy, we report that large language model (LLLM) was leveraged assist in the refinement this manuscript.
Scope of Use. The LLM's function was exclusively that of a writing assistant. Its usage was limited to:
Correcting grammatical errors and refining word choices improve precision and concise- ness.
Restructuring sentences to enhance clarity and logical flow.
Assisting with the positioning of tables and figures within the KTm₃ environment.
18


Reviews:
[{"review_id": "l3NeqmvthW", "reviewer": "Reviewer_N61Y", "summary": "The paper presents a parameter-efficient framework that integrates a window-based intermediate fusion neck (HMF) and lightweight adapters (MTA, MSA, and emphasis parameters) to enhance vision–language alignment for referring image segmentation.", "questions": "*  Could you clarify the **task decoder design**?\n\n*  In Table 1, which IoU metric is used—**mIoU** or **oIoU**? ETRIS reports oIoU from the original paper, but DETRIS uses mIoU.\n\n* In Table 2, please clarify metric issue and the RISCLIP issue mentioned in W1-B.\n\n* What are the **inference speed** and **GFLOPs** of the proposed model?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper introduces a Hierarchical Mamba Fusion (HMF) block, which performs intermediate vision–language fusion by aggregating multi-scale features and applying a window-based Mamba module (WMF).\n- A parameter-efficient tuning (PET) strategy is presented, consisting of a Mamba Text Adapter (MTA) for modeling textual priors, a Multi-Scale Aligner (MSA) with RFMixer and cross-attention for visual–text alignment, and learnable emphasis parameters for adaptive layer weighting.\n- The overall framework, WIMFRIS, integrates these components and is experimentally compared against existing PET-based and full fine-tuning methods on multiple RIS benchmarks.", "weaknesses": "* Lack of Novelty\n\nThe paper shows limited novelty. The **PET part** closely follows DETRIS, essentially extending its parameter-efficient tuning framework with minor Mamba-based modifications. The **neck design** heavily overlaps with the fusion architecture in fixation phase in SaFiRe, both adopting window-based Mamba fusion for intermediate vision-language alignment. Overall, the work mainly integrates these existing ideas rather than introducing a substantively new contribution.\n\n\n* Incomplete Manuscript\n\nThe paper appears **incomplete**. Section 3.2 is unfinished, and the crucial description of the **task decoder** is missing. This omission disrupts the continuity between Sections 2.3 and 2.4. The authors should carefully verify whether the submitted version is the complete manuscript.\n\n\n* Unfair and Limited Comparison\n\nFor Table 1\n\n\n1. **Unfair Comparison :**\nTo ensure fairness, (1) the parameters of PET-based methods should be adjusted to achieve **comparable model sizes**, and (2) the **backbones of all compared methods** should be unified.\n\n2. **Limited Comparison with State-of-the-Arts:**\nMore PET-based approaches should be included, as previous works (e.g., ETRIS, DETRIS, RISCLIP) have done, especially those involving **backbone-side modality fusion** in RIS, such as **PWAM in LAVT**, **SDF in VLT**, and **CFE in RISCLIP**, as well as classical parameter-efficient tuning methods like **LoRA** and **Adapter**.\n\n3. **Marginal Improvement of the WMF Neck:**\nCompared with **DETRIS**, the improvements achieved by the proposed **WMF Neck** are quite marginal.\n\n4. **Insufficient Comparison :**\nA more comprehensive comparison is needed to substantiate the claimed advantages of the proposed neck method, including detailed analyses of **parameter counts**, **computational cost (GFLOPs)**, and **inference speed**, particularly in comparisons with **ETRIS/DETRIS necks**.\n\nFor Table 2\n\n1. **Inconsistent Metrics:**\n   Table 2 mixes **mIoU** and **oIoU** without clarification. While RISCLIP, DETRIS, and WIMFRIS use **mIoU**, most other methods report **oIoU**. In particular, for works like **CGFormer** and **Polyformer**, which provide both metrics, the authors still report their **oIoU** values. Since **mIoU** is generally higher than **oIoU** on the RefCOCO family datasets, this inconsistency makes the performance comparison **unreliable**.\n2. **RISCLIP Issue:**\n   According to the authors’ own definition (line 44, “…keeping the vast majority of the backbone parameters frozen”), RISCLIP also freezes its CLIP backbone and should be considered a parameter-efficient tuning method. Moreover, the results of **RISCLIP-L** are missing, which appear **significantly higher** than those of the proposed “Ours-L” model (trained on RefCOCO+, mIoU: **RISCLIP-L** 74.38 / 78.77 / 66.84 vs. **Ours-L** 71.9 / 76.2 / 67.2).\n\n\n*  Efficiency Analysis\n\nAlthough this work emphasizes the **PET framework** and uses the **efficient Mamba architecture**, more detailed **efficiency analyses** should be provided—specifically **GFLOPs**, **inference speed**, and preferably **FPS**.\n\n\n* Minor Issues\n\nIn **Table 3(a)**, the content does not match the caption: *4×4* is **not** the smallest window size.\n\n\n\n***I would be happy to revise my score if the author addresses these points.***\n\n\n\n---\n\n**References:**\n\nDETRIS: Densely Connected Parameter-Efficient Tuning for Referring Image Segmentation AAAI2025\n\nSaFiRe: SaFiRe: Saccade-Fixation Reiteration with Mamba for Referring Image Segmentation NeurIPS 2025\n\nLAVT: Language-Aware Vision Transformer for Referring Image Segmentation CVPR2022\n\nVLT: Vision-Language Transformer and Query Generation for Referring Segmentation TPAMI2023\n\nRISCLIP:Extending CLIP’s Image-Text Alignment to Referring Image Segmentation NAACL2024\n\nLoRA: Low-Rank Adaptation of Large Language Models. ICLR2022\n\nParameter-Efficient Transfer Learning for NLP. ICML2019\n\nCGFormer: Contrastive Grouping with Transformer for Referring Image Segmentation CVPR2023\n\nPolyFormer: Referring Image Segmentation as Sequential Polygon Generation CVPR2023", "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": "F6qig8fkUO", "reviewer": "Reviewer_WGQk", "summary": "This paper introduces WIMFRIS, a framework for Referring Image Segmentationthat focuses on both a novel intermediate fusion neck architecture (the Hierarchical Mamba Fusion, or HMF, block) and a parameter-efficient tuning strategy. The HMF block leverages a Window Mamba Fuser module to effectively aggregate and fuse multi-scale vision and language features, using window partitioning to tackle the exponential decay in information typical of state-space models. The PET strategy employs adapters to efficiently align textual and visual representations and a learnable stage-wise emphasis mechanism. Extensive experiments are conducted on major RIS benchmarks, demonstrating state-of-the-art results for WIMFRIS compared to both PET-based and full fine-tuning methods.", "questions": "- Can the authors provide a rationale for the placement of PET adapters (MSA, MTA) at specific depths in the vision/text backbone? Have they considered or tested more adaptive/learned strategies for insertion, and can they provide ablations or guidelines for optimal selection?\n\n- How is the concatenation between text class tokens and visual patch windows actually handled in practice (e.g., with respect to normalization, possible channel mismatch, and possible overfitting due to repetitive text tokens)? Would normalization before SSM scans improve performance or stability?\n\n- Have the authors empirically measured the actual decay rate of long-range dependencies for varying window sizes in SSM, and if so, can those be reported? Is the optimal window size truly dataset/task dependent?\n\n- Are there notable scenarios where the windowed approach harms segmentation accuracy, e.g., in very small or oddly-shaped object instances, or when referring expressions are ambiguous or highly context-dependent?\n\n- Will the complete code (including all adapter implementations and ablation regimes) be released for reproducibility, and if so, under what license and conditions?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- WIMFRIS achieves state-of-the-art or highly competitive performance across all standard RIS benchmarks (RefCOCO, RefCOCO+, G-Ref), outperforming previous parameter-efficient and full-tuning baselines. Table 2 clearly demonstrates these gains, including mixed-data setups.\n- Multiple ablation tables systematically dissect the contributions of each module and architectural choice.\n- The schematic diagrams provide clear breakdowns of the model pipeline, supporting the text’s descriptions of modular design and the flow of visual and textual feature processing. The visualizations  offer compelling qualitative evidence for improved segmentation, especially in challenging situations (e.g., clutter, occlusion).\n- The paper carefully characterizes the underlying exponential decay issue in SSM-based fusion, and the model’s windowed approach is well justified both mathematically and empirically.\n- WIMFRIS demonstrates competitive results while tuning a very small fraction of backbone parameters, highlighting the value for practical deployment.\n- The explicit, detailed description of contrastive, dice, and alignment losses (and their weighting) makes reproduction feasible and testable.", "weaknesses": "- While MSA adapters and MTA are described and visualized in Figure 2, the specific methodology for choosing insertion layers for adapters in different backbones is only loosely justified. There is a missed opportunity for a principled, possibly automated or analytical policy for placement, and no ablation on layer choice is provided.\n- Although Table 3 (a) explores performance trade-offs for window size, the choice of optimal $4 \\times 4$ is only empirically justified. There is little theoretical or dataset-specific reasoning for why this size generalizes, and exploring task- or scale-adaptive policies would strengthen claims of robustness.\n- There are several grammatical errors and awkward phrasings, as well as the use of slightly non-standard abbreviations in the tables (e.g., \"vol\", \"m/s/6\", \"m/sfI\" in Table 1), which may disrupt readability and hinder quick assimilation for a broad audience.", "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": "MinqdN4erx", "reviewer": "Reviewer_CZEK", "summary": "This paper proposes a novel parameter-efficient tuning (PET) method named WIMFRIS for referring image segmentation. In contrast to existing PET methods that primarily focus on layer-wise feature alignment and are struggle to aggregate multi-scale features, the proposed approach introduces a simple yet effective neck architecture based on the Mamba module. WIMFRIS achieves state-of-the-art performance on standard RIS benchmarks, demonstrating both efficiency and strong segmentation capability.", "questions": "Please provide your responses with reference to the weaknesses mentioned above.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "- The paper proposes a new efficient parameter-efficient tuning (PET)–based referring image segmentation (RIS) approach named WIMFRIS.\n- The proposed algorithm enhances efficiency by replacing conventional blocks with an HMF block that actively leverages the Mamba architecture. In addition, it introduces several novel components—an SSM-based MTA, an MSA robust to multiple receptive fields, and an RFMixer—which together contribute to more precise vision-language fusion.\n- The method achieves state-of-the-art performance on popular RIS benchmarks, demonstrating both effectiveness and robustness.", "weaknesses": "- Structural Issues in Writing\n   - In the Abstract, abbreviations such as HMF and WMF appear without their full names or descriptions, making it difficult for readers to understand them.\n   - Figure 1 lacks an explanation of the HMF module, requiring readers to infer that WMF is a sub-module of HMF only from context.\n- #Params of PET and Performance Comparison\n   - When comparing with existing PET methods, it would be fair to keep the number of PET parameters (#params) consistent across models. According to Table 1, when DINOv2-B/14 is used as the vision encoder, the proposed method shows only a slight improvement in performance compared to DETRIS, even though it uses more parameters. This raises concerns that the effectiveness of WIMFRIS may not be scalable.\n- Limited Novelty\n   - The paper proposes several modules (e.g., WMF, HMF, MSA, MTA), but the architectural novelty of each component seems limited. For instance, the HMF module appears to replace multiple cross-attention layers with a more efficient Mamba-based structure, but the use of Mamba itself is not novel. Similarly, the MSA and RFMixer are designed to handle multiple receptive fields, but this concept is not entirely new.\n   - The paper would benefit from additional discussion or evidence to substantiate the novelty of these architectural contributions.\n- Lack of Ablation Studies\n   - As mentioned above, the paper lacks experiments that demonstrate the effectiveness and novelty of the proposed modules. For example, it would strengthen the work to include comparisons between MSA/RFMixer and baseline or vanilla methods for handling multiple receptive fields.\n   - Table 3-(a) appears more like an engineering-oriented study rather than one providing clear scientific insight.", "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": "i5RjdUj9Fg", "reviewer": "Reviewer_UdVq", "summary": "WIMFRIS introduces a neck-heavy, parameter-efficient RIS framework that aggregates multi-scale DINOv2 features, fuses them with CLIP text via a windowed Mamba block, and adaptively re-weights each stage, setting new SOTA mIoU on RefCOCO/+/g with < 3 % trainable params.", "questions": "See weakness", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. First to plug a windowed SSM neck (WMF) into RIS; mitigates exponential decay of vanilla Mamba.\n2. Learnable emphasis per stage is simple yet novel for PET.\n3. Exhaustive ablations: window size, kernel configs, PET modules all explored.\n4. Plug-in HMF boosts ETRIS & DETRIS (Table 1), proving generic utility.", "weaknesses": "1. All results are fine-tuned; real-world deployment often lacks target-domain labels.\n2. WMF prepends text to windows, but vision never feeds back to text; may miss visual disambiguation cues.\n3. Parameter efficiency ≠ inference speed; window partitioning + SSM may hurt parallelism.", "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)