WavePolyp: Video Polyp Segmentation via Hierarchical Wavelet-Based Feature Aggregation and Inter-Frame Divergence Perception
Authors: Yuhua Zhang
Guilian Chen
Yuanqin He
Huisi Wu
Jing Qin
Venue: ICLR 2026 Poster (2026)
Keywords: Video Polyp Segmentation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Automatic polyp segmentation from colonoscopy videos is a crucial technique that assists clinicians in improving the accuracy and efficiency of diagnosis, preventing polyps from developing into cancer.
However, video polyp segmentation (VPS) is a challenging task due to (1) the significant inter-frame divergence in videos, (2) the high camouflage of polyps in normal colon structures and (3) the clinical requirement of real-time performance.
In this paper, we propose a novel segmentation network, WavePolyp, which consists of two innovative components: a hierarchical wavelet-based feature aggregation (HWFA) module and inter-frame divergence perception (IDP) blocks.
Specifically, HWFA excavates and amplifies discriminative information from high-frequency and low-frequency features decomposed by wavelet transform, hierarchically aggregating them into refined spatial representations within each frame.
This module enhances the representation capability of intra-frame spatial features, effectively addressing the high camouflage of polyps in normal colon structures.
Furthermore, IDP perceives and captures inter-frame polyp divergence through a temporal divergence perception mechanism, enabling accurate polyp tracking while mitigating temporal inconsistencies caused by the significant inter-frame variations across frames.
Extensive experiments conducted on the SUN-SEG and CVC-612 datasets demonstrate that our method outperforms other state-of-the-art methods.
Codes are available at \url{https://github.com/FishballZhang/WavePolyp.

Published as a conference paper at ICLR 2026
WAVEPOLYP: VIDEO POLYP SEGMENTATION VIA HI- ERARCHICAL WAVELET-BASED FEATURE AGGREGA- TION AND INTER-FRAME DIVERGENCE PERCEPTION
Yuhua Zhang', Guilian Chen', Yuanqin Hc', Huisi Wu' Jing Qin²
'College of Computer Science and Software Engineering, Shenzhen University *Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University 100.10ma100mmmmmm is. 1s. szu. edu. cn, hswuêszu. edu. cn
ABSTRACT
Automatic polyp segmentation from colonoscopy videos is crucial technique that assists climicians in improving the accuracy and efficiency of diagnosis, pre- venting polyps from developing into cancer. However, video polyp segmentation (VPS) is a challenging task due to (1) the significant inter-frame divergence videos, (2) the high camouflage of polyps in normal colon structures and (3) the clinical requirement of real-time performance. In this paper, we propose a novel segmentation network, WavePolyp, which consists of two innovative components: a hierarchical nnvelet-based feature aggregation (HWFA) module and inter-frame divergence perception (IDP) blocks. Specifically, HWFA excavates and ampli- fies discriminative information from high- frequencycc and wwwfrreueecyy features decomposed by wavelet transform, hierarchically aggregating them into refined spatial representations within each frame. This module enhances the represen- tation capability of intra-frame spatial features, effectively addressing the high camouflage of polyps in normal colon structures. Furthermore, IDP perceives and captures anter-frame polyp divergence through a temporal divergence perception mechanism, enabling accurate polyp tracking while mitigating temporal inconsis- tencies caused by the significant inter-frame variations across frames. Extensive experiments conducted on the SUN-SEG and CVC-612 datasets demonstrate that our method outperforms other statc-of-the-art methods. Codes are available at httrss://github.cco/iiitttt 12aagg Waw.aaoyg..aeePP.....omm.
INTRODUCTION
Colorectal cancer (CRC), the third most common cancer worldwide, may have survival rate ex- ceeding 90% in early stages but drops below 5% in advanced stages (Ji et al., (022). Colonoscopy is widely used in clinical practice to detect precancerous polyps, which are precursors to CRC, and remove them, and thus improve CRC survival rate (Ahn al. 2012). However, manual screening of polys in colonoscopy is labor-intensive, to high polyp miss rate in diagnosis. Therefore, highly demanded to develop automatic polyp segmentation methods to improve the accuracy and efficiency of diagnosis.
consumin,,,, and error-prone (Li et al.) (2025), leading
However, automatically segmenting polyps from colonoscopy videos (also called VPS) is a very challenging task for the following reasons. First, significant variations in polyp size, location, and shape among consecutive frames pose great challenges to track and identify polyps, as shown in fig. (c-e). Second, polyps often originate from the surrounding diseased tissues, leading a high inherent similarity with the background (also regarded as the high camouflage of polyps), which impedes precise delineation of polyps, shown in fig. (a-b). Third, it is crucial for VPS to achieve real-time performance to provide timely assistance to physicians during colonoscopy examination.
Recently, many deep learning based approaches have been proposed for image polyp segmentation (IPS). Most IPS methods either leveraged the local feature extraction capabilities of convolutional
"Coreeponningg Author

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Published as a conference paper at ICLR 2026
(a)
(b)
(c)
(d)
(c)
Figure 1: Challenges in VPS: (a) and (b) the high camouflage of polyps, (c) position variation, (d)
shape variation, (e) size variation. Note that (a)-(e) represent two strictly adjacent frames.
neural networks (CNN) (Wang al. 2022a Zhang et al.) 2020 Zhong al. 2020 harnessed the global context modeling strengths of transformers (Dong et al. 2023 He et al. 2023 Zhang
al. 202T) to segment polyps within static frames. However, these methods demonstrate short- comings when applied to colonoscopy videos, as they are incapable of learning the temporal depen- dencies from video sequences. To improve the performance of VPS, is crucial to model temporal consistency between consecutive frames to improve segmentation performance across frames. In this regard, some VPS approaches propose to integrate hybrid 2D/3D CNN architectures (Puyal et al. 2020) or employ normalized attention mechanisms (Ji et al. 2022 2021 capture temporal dependencies. On the other hand, some studies (Hu et al. 2024) leveraged long-term and short- term receptive fields to improve the reliability and stability of spatial-temporal features. However, these methods primarily enhance the intra-frame spatial representations by incorporating inter-frame temporal information, largely ignoring the exploration of discriminative features within individual frames and the perception of inter-frame divergence. As a result, they struggle to comprehensively address VPS challenges due lack of discriminative features for learning frame-wise polyp details and inter-fratt frame divergent information to recognize dynamic changes across video frames.
In this paper, we propose a novel VPS model, aiming at excavating intra-frame discriminative fea- tures from the frequency domain and enhance the perception of inter- inter-frame divergence; we call WavePolyp. Our model has two innovative components: hierarchical wavelet-based feature aggregation (HWFA) module and inter-frame divergence perception (IDP) blocks. In HWFA, multi- level features are decomposed into high-freyuency (HF) and low-frequency (LF) components using wavelet transform. These frequency components then enhanced through dedicated HF and LF calculation unit, which refines their representations to improve feature discrimination. Sub- sequently, an ascending frequency-guidee frequency cues across levels, enabling WavePolyp to effectively capture mne-grained spatial details within individual frames. In IDP, we design temporal divergence perception mechanism to improve the tracking of polyp targets among consecutive frames, mitigating the interference caused by inter- frame temporal inconsistencies. We conduct extensive experiments on two benchmark datasets: SUN-SEC SEG (Ji et al., (022) and CVC-612 (Bernal et al.) 2015), providing comprehensive compar- isons with ofate-of-the-ar art (SOTA) methods and demonstrating the effectiveness of our WavePolyp. Our major contributions are summarized as follows:
aggregation unit hierarchically integrates the enhanced
We propose novel network for VPS by focusing on the excavation intra-frame discrim- inative features and the perception of inter-frame divergence.
We propose a HWFA module to enhance spatial representations within individual colonoscopy frames based frequency domain. Additionally, employ IDP blocks to temporally perceive inter-frame divergence, enabling accurate polyp tracking in colonoscopy videos.
Our method significantly outperforms other SOTA methods two polyp video datasets: SUN-SEG and CVC-612, achieving better balance between segmentation accuracy and real-time efficiency.
RELATED WORK
2.1
POLYP SEGMENTATION
With advancements in deep learning, various IPS have been introduced to detect the pixel-level polyp regions from colonoscopy images. Methods (Akbari al. 2018) based fully convolution net-
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works (FCN) and those (Zhou al. 2020 Zhang al. 2020) based U-Net (Ronneberger al. have been proposed to extract precise semantic information. Certain methods (Fang et al,
2013
2019
Wang 2022a incorporate boundary constraints achieve Noundary-award segmentation
results. Furthermore, in order to enhance global information perception capabilities, researchers (He) 2024) combine transformer (Vaswani et al., 2017) and CNN simultane- ously model global and local information, thereby attaining superior performance. SAMAug (Zhang al. 2023) leverages SAM (Kirillov et al., (023) augment polyp images without fine-tuning its parameters, Polyp-SAM (Li et al. (0024) instead retrains SAM directly on polyp-speciff datasets to improve segmentation performance.
ett al.
2023 Wu et al.
However, these IPS methods overlook the temporal cues between adjacent frames in colonoscopy videos. Therefore, some researchers have devoted integrating spatial-temporal features across consecutive frames. For example, a hybrid 2/3D CNN framework (Puyal et al. 2020) was introduced to account for the aggregation of spatial-temporal correlation. PNS+ (Ji et al. (022) implemented global-to-local learning strategy and a normalized self-attention module to model and balance short-term and long-term dependencies. SALI (Hu al. 2024) leveraged both long-term and short- term receptive fields to model temporal coherence to improve the stability and reliability of spatial- temporal features. VP-SAM (Fang et al. (024) enhances SAM with a parallel opatio-tempont side network that explicitly models inter-frame motion dynamics using deformable convolutions and cross-attention, thereby improving polyp tracking across video frames. Diff-VPS (Lu et al.] (024) integrated multi-task supervision into diffusion models and incorporated a temporal module using generative adversarial self-supervised strategy to mitigate the high camouflage of polyps in videos. However, these methods fail to capture the fine-grained mntra-frame discriminative feature of highly camouflaged polyps and the divergent information between adjacent frames.
2.2 WAVELET TRANSFORM IN DEEP LEARNING
Wavelet Transform (WT) which is essential for signal analysis, has been gradually applied in com- puter vision tasks. SDWNet (Zou et al. 2021). which utilizes the frequency domain information generated by WT as complement to the spatial domain information, is applied to the image deblur- ring task. LAAT at 2023) employs wavelet fusion module to combine shallow structures and deep details recover realistic images in the frequency domain for face super-resolution. WaveDiff (Phung et al.) as well as improve computational performance. FEDER (He al. 2023) addresses the intrinsic similarity of the foreground and background by decomposing the features into different frequency bands using learnable wavelets. Different with above methods, we apply discrete wavelet trans- form to decompose multi-level features into HF and LF components. The decomposed features are hierarchically enhanced and aggregated to enable our model to effectively distinguish fine-grained intra-frame details.
2023) uses WT in generative models to enhance the visual quality of generated images
METHOD
3.1 OVERVIEW
The architecture of our proposed method is illustrated in fig. 2 which is novel VPS framework that imitates the behavior of a physician diagnosing highly camouflaged polyps when observing colonoscopy videos. It mainly consists of feature extractor, HWTA, and decoder composed of IDPs. Specifically, the high camouflage of polyps in colonoscopy videos poses a great challenge for accurate segmentation. We propose the HWFA to solve the challenge, which decomposes features into different frequency bands and then mines subtle clues that can be used to distinguish polyps and the surroundings of polyps. Due to the dynamic nature of video, characterized by temporal changes between frames, polyp segmentation often suffers from temporal inconsistencies. Therefore, we employ the IDP in the decoder to perceive inter-frame polyp divergence, thereby mitigating the interference caused by fnter-frame temporal inconsistencies and improving inter-frame polyp targets tracking.
Given video clip with T frames I E Xx inspired by ZoomNeXt (aag et al. 2024 WavePolyp first zooms in and out two auxiliary scales 0.75x and 1.25x, denoted as 20.75 and I 1.22 This approach is based on the idea that different zoom levels provide distinct and complementary
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7 7.77
Inpor
Inter frame Divergence Perception (IDP)
Prodections P
HW
Tempoonl Shift
Keybalie
Vaiue
DIFF
Z
HWxT x
TxT
HW X
Poly
Quirry
71:35
Extractor
Decoder
IDP
Scale Merging Sahnework
Triplet Encoder
AFA
HFC
fa
DWY
X2
IDP
AFA
LFC
IDP
IDP
Aggregation
Up-ompllng
Down-cempling
Concolamation
Add
Multiplication
Transpose
Sabimction
Figure 2: Overview of WavePolyp. which mainly introduces hierarchical wavelet-based feature ag- gregation (HWFA) and inter-frame divergence perception (IDP).
information, similar to how humans zoom in and out to gain different perspectives. These video clips of three different scales will be fed into shared feature encoder extract three sets of multi- level features. To aggregate the features of different scales at the 1.0x scale, these features are passed through the multi-scale feature merging Net (Pang al. 2024), which outputs the aggregated features { E IR T'xxxx ZE1T 2F1T A 3,2,3,4 The resulting features will be fed into HWFA
If
module, decoder, and polyp predictor. Ultimately, a clip of predictions P |2'xIxXW will be generated by the polyp predictor.
3.2 HIERARCHICAL WAVELET-BASED FEATURE AGGREGATION
The high camouflage of polyps presents challenges for conventional feature extractors VPS. These extractors fail to extract fine-grained discriminative features within individual frames, resulting in suboptimal performance. Existing VPS approaches focus primarily on leveraging temporal correla- tions between consecutive endoscopic frames enhance intra-frame spatial representations. How- ever, in scenarios with irregular polyp motion or low-quality video frames, such inter-frame depen- dency may degrade model robustness and introduce error accumulation, especially when adjacent frames are noisy or misaligned.
Discriminative features of camouflaged polyps are primarily distributed across both HF compo- nent (e.g., texture and edge), and LF component (e.g., color and illumination). This frequency- domain characteristic makes parametric free and computationally efficient discrete wavelet trans- form (DWT) particularly suitable for capturing subtle variations in visually similar lesion regions. Motivated by this observation, we propose the HWFA, which enhances intra-frame feature discrimi- nation through frequency-domain analysis. We decompose the extracted spatial features using DWT and selectively enhance the most informative components of HF and LE.
To excavate discriminative information from the decomposed features, design HF calculation (HFC) unit and a LF calculation (LFC) unit, as shown in fig. 3 Simultaneously. we design an ascending frequency guiided aggregation (AFA) unit hierarchically integrate the decomposed fea- tures across different levels, rather than simply using concatenation operation based on up- sampling.

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CA-SA
Iapul Festure
Enhazced Feauuee
HPC
W
M4
CAISA
LFC
Channel Attcation Spatial Attcation (CA +
APA
Figure 3: Details of the proposed HFC, LFC and AFA. The HFC and LFC units are designed to ex- cavate discriminative information from the decomposed features. The AFA is designed to aggregate higher-level feature f and a lower-level feature k+1 under the guidance of W.T.P k+11 The yellow square in the AFA illustrates the application of a window-base basee filtering strategy that dynamically moves both vertically and horizontally.
HFC unit HFC accentuate those texture-rich regions for the excavation of subtle discriminative features. Specially, HFC receives a HF feature JFf as input, which is fed into a convolution layer, a batch normalization (BN) layer, and a residual layer for texture preservation. JHF enhanced by these layers are able provide richer discriminative representations. We then employ channel attention and spatial attention layers to highlight noteworthy parts in spatial and channel domains. Therefore, the HFC unit is defined as:
W CSAA J
where CSA denotes the layer of channel attention and spatial attention. Conv3 represents 3x3 convolution.
LFC unit. LF features contain more global information, which inevitably brings redundant infor- mation and interference information. Therefore, compared to HFC, the BN layer in LFC is replaced by the instance normalization (IN) (Ulyanov et al., (017)) layer, and there an additional symmet- ric operation of position normalization (PN) and inverse position normalization (IPN), Specifically, LFC receives a LF feature _ as input, which will be fed into the same layers like HFC. Note that f is fed into the PN layer before being fed into the convolution layer. Mean l and variance a of f are then passed through a convolution layer update. The IPN layer is used restore f% f.F passed by the residual layer back into the original data scale according to the updated μí and o'. Such design of normalization strategy (Li et al. 2019) can make the subsequent attention opera- tion focus on cleaner global information and hightlight those abnormal lesion regions from a global perspective. Therefore, the LFC unit can be formulated as follows:
W CSA CSA(TPPBBN( 1 + ()
(2)
AFA unit. AFA is designed to incorporate inter-feature dependencies, highlighting subtle discrimi- native cues. Using AFF hierarchically, aggregated features of the lowest level f&f and ftF cover multi-level enhanced feature discrimination, which enable our model to effectively distinguish fine- grained intra-frame spatial details. Specifically, taking high frequency as an example, as shown FFF in fig. 3 AFA receives three inputs, lower-level features f! higher-level featuresf f+11 and the guidance matrix WHF of PHF. and outputs lower-level aggregated discriminative feature f that
combines deep semantic information of FHF and abundant spatial details of f First, the down- sampled aggregated discriminative feature fill is generated by a window- based linear model (Liu
al. 202T):
Ah Down (F Mws Vi Sw
(3)
where Down(-), 8 and i represent down-sampling, local window, and pixel point i. The terms (awe } are the linear transformation coefficients for the pixels within the window Sw, which can be acquired by optimizing the subsequent objective function:
HFF (UTT): (UE)))) + +
min co Maw
where is responsible for constraining ow. Considering that pixel is covered by multiple windows, compute the average of those window-specific coefficients to obtain the particular transformation coefficients (a,, }4} for pixel i. By arranging all {a,, Hr} into a matrix form (or, (..). Equation can be reformulated as follows:
pdh az ② Down (F + pi
(5)
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where is the Hadamard product. We then up-sample {or (48) to obtain for, ph and generate f to enhance spatial details. Therefore, we can define AFA unit as:
f AFA(JER JHE W oh C JFF + 4b
(6)
Building on the aforementioned units, we propose HWFA, as shown in 2 Specifically, we perform a DWT on fs to obtain the HF feature $3 fHF and LF feature f3 which can be formulated as:
f3 HH = (WT f.. = DfT((fss)
(7)
where DWT and DWT are the selection of the HF and LF sub-bands in the decomposed sub- hands obtained by applying DWT. For f2 and f1, higher-level frequency feature will be aggregated into their responding lower-level frequency feature, which can be expressed as like:
SHFF AFAUE HFC(FHF) (WT,(f))) SEP AFA DIT((T)) (8)
Then, JHF and J.F are generated, containing multi-level aggregated discriminative information from the HF and LF components, respectively. The features fa and ft are concatenated to enhance perception of global information, thereby preventing ignorance of excessive background information in higher-level features. The features fi and f are concatenated to enhance fi ability to capture fine details, such small objects, detailed edges, and intricate textures. Finally, the enhanced features Ti, Z2, I3. and 24 will be obtained, which can be written as:
f1. 23 conv
(.
(9)
22 f2. 21 convix c (Conncauuu where Concat(-), Up(.) and convixi (+) denote concatenation, up-sampling, and x convolution.
(10)
3.3 INTER-FRAME DIVERGENCE PERCEPTION
Polyps in colonoscopy videos may have significant inter-frame divergence in shape, location, size, and boundary, which manifests as temporal inconsistencies between frames. In addition, polyp tissues are prone to non-rigid deformations caused by intestinal peristalsis and camera jaterajitter. Relying solely on intra-frame feature often fails to distinguish artifacts caused by non-rigid deformations from realistic polyp structural changes, and neglects valuable temporal cues present across frames.
To enhance the model's ability to capture temporal dynamics and distinguish meaningful inter-frame variations, we introduce IDP blocks. These blocks improve the tracking of polyps targets among consecutive frames by leveraging our proposed inter-frame divergence perception mechanism. The core idea is to explicitly model the temporal divergence between adjacent frames. Specifically, as shown in the IDP block of fig. 2 given the visual features Q and KV, which are obtained from Ta through convolution followed by flattening, After shifting, the feature map of the starting time step in the video clip is moved to the end, while the remaining feature maps move forward by one position. Consequently, we derive the inter-frame divergence Diff = Shift (V) V, and modulate it via a learnable projection matrix Wv, allowing the network to focus on regions with significant temporal changes. To enhance the motion cues of the object of interest across frames, we apply an inter-frame divergent attention operation, given by (Wy(Shift
first perform the temporal shift (Lin et al.) 2019).
Sofftma) KJ x"@) ). Notably, the attention mechanism operates along the VITW
time dimension. The resulting weight matrix, denoted as Att, is a TxT matrix, representing the interactions and comparisons between each time step and all others. The product of Att and Diff serves to identify the most significant information within the time series, thereby highlighting the inter-frame divergence essential for tracking polyp locations. Since the computation is conducted along the time dimension rather than the channel dimension, the computational complexity remains relatively low. Furthermore, two 3 x 3 convolutional layers are applied to the temporal cues to fully diffuse the inter-frame divergence information, which is then added to the original visual features Q, which we call it frter- divergence diffusion. The final output computed as:
KTQ
O Conv Conv(WW - V)Softaax(
+ +Q)
(11)
VHW
where Q, E XT, Conv(-), Wy. Shift()) are two x 3 x convolutional layers, a T learnalle matrix and the temporal shift operation like (Lin al. 2019

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Table 1: Quantitative comparison with different SOTA methods on SUN-SEG and CVC-612.
SUN-SER SEG-Easy
SUN-SEG-Hard
CVC-612
Model
Backbone
S.
Eyen
Fy
Dice
5
E
Fg
Dice
S,
Egg
F3
Dice
SLT-Net
PVTv2-BB BS
90.39
93.75
84.35
87.15
88.06
92.05
80.31
83.55
94.61
97.70
92.06
92.96
ZoomNeXt
PVTv2-BB
84.64
82.21
92.73
AutoSAM
VIT-B
86.28
91.69
78.25
81.27
83.59
89.91
73.08
77.25
90.56
96.19
87.68
88.34
WeakPalyp
PVTv2-BS B5
90.51
93.72
84.89
87.57
90.19
93.77
83.74
86.73
91.51
95.18
88.74
89.07
PNS+
Res2Nct-50
85.75
86.11
76.14
81.91
83,98
85.68
72.75
79.32
89.05
92.54
MAST
PVTv2-BB B2
87.91
92.87
81.40
84.44
87.44
92.82
80.27
83.79
93.54
96.07
89.93
90.12
SALI
PVTv2-B5
89.45
93.01
83.73
86.07
89.19
93.21
83.05
85.54
94.41
97.12
92.16
93.01
VP-SAM
VIT-B
90.49
93.68
85.66
88. 19
93.74
83,
86
94.68
83
92.51
93.45
Ours
PVTv2-B5
90.93
94.15
86.74
88.96
90.28
93.96
85.17
87.55
95.24
98.61
93.57
94.36
Frame
wil both
W/e HWWA (a)
Wib IDP
both
wie HWFA
HIWFA
Figure 4: (a) Visualization of module ablation test set SUN-SEG-Hard, (b-c) I-SNE visualization of features. Green represents lesion regions, while yellow represents the opposite.
The decoder stacks four IDP coarse-to-fine cascade: starting from the deepest feature 24, each IDP temporally enhances the current feature via IDP, upsamples it, and residually fuses it with the feature of the next finer scale; after four such iterations the final representation is obtained and fed to the polyp predictor to generate prediction result.
3.4 Loss FUNCTIONS
2022) to guide the convergence process of the model. Moreover, the uncertainty awareness loss Luat like (Pang et 2024) becomes another part of our loss in order to force the model to increase "confidence" in decision making and aggravate penalty for fuzzy prediction. We can formulate total loss with two components as follows:
We apply binary cross-entropy loss Lice like (Ji et
Liotal Loce (Pr, Gt) AL (Pss Gt)
(12)
where À is the balance coefficient, and its adjustment strategy like (Pang al. 2024), P. and G, is the prediction and the corresponding ground truth (GT).
4 EXPERIMENT
4.1 EXPERIMENT SETUP
Datasets. We evaluate our method on two public video-based datasets: SUN-SEG (Ji et al. 2022
and CVC-612 (Bernal et al. 2015). (1) SUN-SEG includes 49, 136 frames from 285 clips. which consists of three subsets: training set (112 clips / 19, 544 frames), test set SUN-SEG-Easy (119 clips / 17,070 frames) and test set SUN-SEG: SEG-Hard (54 clips 12,544 frames). (2) CVC-612 contains 612 frames from colonoscopy clips with a resolution of 384 288.
Evaluation Metrics. For comprchensive and fair comparison. employ four metries to evaluate the results, including Dice, structure-measure (5.) (Pan et al. (207)), enhanced-alignment measure (E (Fan al. (02T), and weighted F-measure (F ()) (Margolin et al. 2014). similar previous
works (J 2022)
Implementation Details. Our proposed method is implemented using PyTorch. All frames are uniformly resized to x52x352, while clip length T and channel dimension are respectively set to 5 and 64. The entire model is trained for 30 epochs with a batch size of 2 in an end-to-end manner on single RTX 3090 GPU, using Adam with betas (0.9, 0.999). The learning rates of backbone

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Table Ablation studies conducted on the SUN-SEG test evaluate the two core components of WavePolyp and their internal designs. Norm: Normalization strategy of LFC. IDA: Inter-frame divergence attention. IDD: Inter-frame divergence diffusion.
HWFA
IDP
SUN- SUN-SEG-Easy
SUN-SEG-Hard
HFC
LFC
Norm
AFA
Shift
IDA
IDD
Sa
Dice
Sa
Dice
89.12
87.51
88.84
86.12
90.07
87.69
90.09
86.65
90.31
88.06
89.75
87.12
V
89.81
87.29
89.79
86.85
90.21
87.76
89.96
87.01
V
87.92
87.39
87.75
85.55
90.03
87.62
89,45
86.76
90.26
88.01
89.74
87.11
90.57
88.36
90.06
87.12
90.93
88.96
90.28
87.55
(3)
Figure 5: Visual analysis of frequency-based feature disentanglement. From left to right: (a) Origi- nal Frame, (b) Prediction Mask, (c) High-Frequency Features PfHP), and (d) Low-Frequency Fea- tures (fL LFF)
and the rest are initialized 1e-5 and 2e-5, and both decrease by 2% every epoch. Unless otherwise specified, PVTv2-b5 (Wang al. 2022b) is used as our backbone for all experiments, which is pre-trained on ImageNet. For SUN-SECI we separate 20% from the training set as the validation set. For CVC-612, we split the training set, validation set, and test set with a ratio of 6 2: 2.
4.2 ABLATION STUDIES
Effectiveness of Components. All ablation experiments are conducted on SUN-SEG. As shown in table 2 the performance degradation observed when removing either HWFA or IDP suggests that both components HWFA and IDP are applied individually, the improvement in the Dice metric more pronounced on the hard test set, indicating their strong adaptablity to challenging samples. Moreover, the perfor- mance gain from using each component separately is less significant than when they are combined, confirming the complementary nature of intra-frame discriminative feature excavation and inter- frame divergence perception. In addition, the individual units HFC, LFC and AFA also demonstrate improved robustness against difficult cases. The standardization strategy of LFC has demonstrated its effectiveness. Significant performance drops are observed when AFA is excluded, even falling below the baseline. This because HFC and LFC strongly enhance the high-frequency textures and low-frequency structures. Without the adaptive spatial alignment provided by AFA, naively fusing these sharpened features across different seales amplifies the inherent spatial misalignments, intro- ducing noise and artifacts that are more detrimental than the unenhanced features in the baseline. To explicitly validate the effectiveness of the HWFA module, we visualized the final aggregated frequency features, fHF and f/ in fig. 5 These features represent the culmination of hierarchical aggregation and are used to refine the main features fi and f3. respectively. As observed in the visualization, H (HF output) sharply highlights the polyp's 's boundaries and surface textures. This
essential for improving segmentation performance in VPS. Notably, when
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Figure 6: Impact of IDP on temporal stability. Row 2: w/o IDP; Row 3: Ours. The IDP module
ensures consistent tracking and suppresses flickering. (Red: GT, Green: Pred, Yellow: Overlap).
STL-Net
Zoom>NeX
AutoSAM
WeakPPlyp
VP-SAM
MAST
Figure 7: Visual comparison of our proposed method with different SOTA methods on the SUN- SEG-Easy and SUN- SEN-SeEG-ard test sets. Red, green and yellow represent the GT, prediction and their overlapping regions, respectively.
aggregated high-frequency cue is fused into the shallow layer (21) to enhance the detail preserva- tion of the camouflaged object. f(f (LF output) captures the holistic lesion body and structural context. This aggregated low-frequency cue is fused into the deep layer (xa) to prevent the loss of global semantic information. This complementary behavior confirms that HWFA successfully disentangles and aggregates discriminative cues for targeted feature refinement. Lastly, the three distinct components of the IDP have demonstrated their indispensability. Meantime, we perform frame-by-frame visualization in fig. which shows that excluding the IDP module leads mask flickering and unstable boundaries (second row). while our IDP-equipped model ensures smooth and temporally consistent segmentation (third row). For more extensive visualization results, please refer to section A.5
Furthermore, provide visual comparison illustrate the contributions of key components in WavePolyp. As shown in fig. IDP effectively alleviates segmentation discontinuities caused by drastic variations in polyp size, shape, and position. Meanwhile, HWFA addresses the challenge of high camouflage in polyps, leading to clearer boundaries and higher confidence. When HWFA and IDP are combined, the network achieves superior segmentation performance, characterized by sharper boundaries, more precise localization, accurate size and shape estimation, and higher confi- dence in predictions.
Effect of Clip Length. As shown in fig. perform comprehensive evaluations of varying clip lengths, with experimental results demonstrating optimal performance at a clip length 5. We analyze that shorter clips inadequately capture inter-frame divergence, leading to localization failures during rapid polyp morphological changes. contrast, longer clips introduce excessive divergence between temporally distant frames that degrade model decision making.

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Published as a conference paper at ICLR 2026
Table 3:
Performance vs. efficiency on SUN-SEG-Hard based on an RTX 3090 GPU and batch size equal to 1.
SUN-SEG-Lay
SUN-SLGllard
59.91
21.53
Methods
Sa
Dice
Params
GFLOPs
FPS
2.
SLT-Net
90.39
83.59
82.39M
87.81
5.08
ZoomNeXt
89.81
85.22
84.78M
102.32
22.48
PNS+
85.75
79.32
9.79M
46.01
75.21
4
5
6
7
5
6
MAST
87.44
83.79
25. 25.69M
21.02
21.69
Clip Langth
SALI
89.45
85.54
82.73M
58.15
7.93
Figure 8: Ablation studies of clip length on SUN-SEG.
VP-SAM
90.03
86.94
140.27M
156.86
12.74
WavePolyp
90.93
87.55
86.63M
114.88
23.04
4.3 COMPARISON WITH STATE-OF-THE-ARY METHODS
We compare WavePolyp with some SOTA methods, including SLT-Net (Cheng et al. 2022). Zoom- NeXt (Pang et al., 2024), AutoSAM (Shaharabany et al. 2023). WeakPolyp (Wei et al. 2023),
PNS+ (Ji et al. 2022), Diff- VPS (Lu et al. 2024), MAST (Chen et al. 2024), SALI (Hu et al. 2024)
and VP-SAM (Fnng et al. 2024). These methods include two natural video segmentation models (SLT-Net and ZoomNeXt), two IPS methods (AutoSAM and WeakPolyp), and four VPS methods. To ensure fairesss, obtain the segmentation results of these methods using their publicly available implementations.
The quantitative comparison between method and above SOTA methods SUN-SEG and CVC-612 is presented in table The results demonstrate that our method outperforms other SOTA methods in all metric, illustrating its robustness. The VP-SAM appears close to method but relies on a single point prompt for each frame, which is impractical to obtain in clinical applications. In addition, we provide qualitative comparison with SOTA methods. As shown in fig. 7 our method yields more accurate segmentation results when dealing with highly camouflaged polyps and significant inter-frame divergence. Compared other methods, it achieves superior polyp lo- calization and clearer boundary delineation, demonstrating the effectiveness of HWFA in excavating frame discriminative features and IDP perceiving inter-frame polyp divergence.
4.4 DISCUSSIONS AND LIMITATIONS
Although our experiments are conducted solely on colonoscopy video datasets, we believe that method general enough to be applied to other datasets with similar challenges. Furthermore, shown in table we conduct performance-efficiee. comparison with video-based SOTA methods. Our method achieves significant performance improve- ments near the real-time reasoning speed (23.04 FPS). However, our method still has some limitations. distorted lighting and focus (c-d) and overlapping intestinal walls (e-f) may limit our method.
(b)
(c)
(d)
(e)
Figure 9: Failure cases. Red, green and yel- low represent the GT, prediction, and their overlapping regions, respectively.
As shown in fig, g missing details (a-b),
5 CONCLUSION
In this paper, we propose a novel network WavePolyp. consisting of two innovative components: a HWFA module and IDP blocks, which improves the segmentation performance from the perspective of intra-frumm frame and inter-frame The HWFA allows WavePolyp to identify highly camouflaged polyps by excavating intra-frame discriminative features. Additionally, the IDP assists in accurate polyps tracking to address temporal inconsistency. Extensive experimental results on SUN-SEG and CVC- 612 demonstrate the effectiveness of our proposed method.
ACKNOWLEDGMENTS
This work was supported partly by National Natural Science Foundation of China (No. 62273241), Natural Science Foundation of Guangdong Province, China (No. 2024A 1946), the Shenzhen
10

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Published as a conference paper at ICLR 2026
Research Foundation for Basic Research, China (No. JCYI20055000118144000333 and grant under Collaborative Research with World-leading Research Groups scheme of The Hong Kong Polytech- nic University (project no. G-SACF).
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A APPENDIX
A.1 DETAILS OF CHANNEL AND SPATIAL ATTENTION (CSA)
In the proposed HFC and LFC units, we employ a sequential channel attention (CA) and apatial at- tention (SA) module, denoted CSA, selectively emphasize informative features in both channel and spatial dimensions. The process can be formulated as:
F' Mc(F) ② F, F" MM(F))
(13)
where CC the input feature, M, and M, denote the channel and spatial attention maps, respectively, and @ represents element wise multiplication.
Channel Attention (CA). Different from the standard MI.P-based approach, we adopt a more eff- cient ID convolution strategy capture local cross-channel interactions. Furthermore, considering the distinct characteristics of wavelet- wavelct-dccomposed features, we employ adaptive pooling strategies. For LFC, which represent smooth global structures, use Global Average Pooling (GAP) to ag- gregate background information. For HFC, which contain sharp edges and textures, we use Global Max Pooling (GMP) to preserve the most discriminative texture signals.
The channel attention map Mc(F) E Rex1xl is computed as:
Mc(F) = (ConvIDy (Pool(F))),
(14)
where a is the Sigmoid activation function, and Conv D2 represents ID convolution with a kernel size of & = 3. Pool(-) denotes GAP for LFC units and GMP for HFC units.
Spatial Attention (SA). The spatial attention module focuses on highlighting where the informative regions are. We employ bottleneck convolutional structure to compute the spatial attention map (,"FF) E x1x The refined feature F" first reduced in channel dimension to extract local spatial contexts and then mapped to a single channel:
M,(F') (Conv2 (ō (Conv, (FF)))
(15)
where Conv is 3 x 3 convolution reducing channels from C to C/4, 5 is the ReLU activation function, and Conv is x convolution projecting channels from C/4 to This design effectively suppresses background noise while highlighting the polyp regions.
A.2 MORE DETAILS ON AFA
In low- low-frequency components, AFA receives three inputs, lower-level features f.F higher-level fea- eLF tures f/4 and the guidance matrix W.L of fk+1, and outputs lower-level aggregated features f!, which hicrarchically integrates the enhanced discriminative features highly camouflaged polyps. The aggregated feature f/ can be generated by minimizing the subsequent objective function:
min Σ [Ww (UT)); ((EE))))) + e ous
(16)
where
(1 - Down (ft Pan Vi E Sn
(17)
where Down(.), s. and i represent down-sampling, local window centered by pixel w and pixel point i. is responsible for constraining ow- Similar to high-frequency components, by averaging.
14

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Published as a conference paper at ICLR 2026
matrixing and up-sampling the window-basee coefficients, get the final transformation coeffi- cients (o%= p. Therefore, the aggregated feature fg can be obtained as follows:
f AFA Sk W
(18)
= oh & fh +
where the Hadamard product.
A.3 MORE ABLATION STUDIES
Design of HWFA. As shown in fig. 2 our HWFA design begins with hierarchical ag- choice is supported by experimental results.
gregation starting from fs. This design In table 4 we present the results of ablation experiments with four different design varia- tions. Experiment
Table 4:
Ablation studies conducted the SUN- SEG test set to evaluate the design of HWFA.
applies DWT, HFC, and LFC only to S1, omitting AFA. Experiment 2 employs level-wise fusion starting from f2. Ex- periment uses lesel-wise fusion starting from fs. Experiment starts level-wise fusion from f4. with the note that this experiment was con- ducted at a resolution of 384x384, as DWT re- quires even resolution. In the 352x352 set- ting, the resolution of f4 is 1 1xx11. Therefore, we chose the design from Experiment 3 as the final HWFA design.
SUN-SEG-Fasy
SUN-SEGE SEG-Hard
Design
Sa
Dice
Sa
Dice
1
88.91
86.53
88.67
85.94
2
90.14
88.52
89.75
87.03
3
90.93
88.96
90.28
87.55
4
90.65
88.84
90.13
87.47
Channel Dimension.
We compared different channel dimensions, and shown in table [ the best performance is achieved when C = 64.
Learning Rate. We also tried different base learning rates (baselr) of backbone. The base learning rates of the rest components is twice that of the former. The results are shown in table 6 When baselr = le-5, the segmentation performance the best.
Table 5: Performance of different base learning rates on the SUN-SEG test set.
Table 6: Performance of different channel di- mensionS on the SUN-SEG test set.
SUN-SEG-Fasy
SUN-SEG-Hard
JUN-SEG-Easy
SUN-SEG-HH
baselr
S.
Dice
Sa
Dice
Sa
Dice
S.
Dice
5e-6
90.75
88.12 12
89.54
87.17
32
90.64
88.05
89.17
87.01
le-5
90.93
88.96
90.28
87.55
64
90.93
88.96
90.28
87.55
5e-5
90.65
88.45
89.75
87.16
128
90.77
88.69
89.95
87.41
le-4
90.27
87.76
89.37
86.97
256
90.35
88.71
90.03
87.44
A.4
MORE QUANTITATIVE COMPARISON RESULTS
We employ six metrics to evaluate the results, including Dice, structure-measure (S.) Fan et al. 2017) enhanced-alignment measure (F Fan al. (2021). sensitivity (Sen). F-measure Achanta al. 22099), and weighted F-measure (Fş Margolin et al. (2044) similar to b Hu et al. (2024). The test set SUN-SEG-Fasy has two subsets: where Seen represents the visible case that di- vides one case into two parts for training and testing. Similarly, the test set SUN-SEG-Hard has two responding subsets. The more detailed quantitative comparison results are shown in table 7 which demonstrates that method outperforms other state-of-the-art methods in most metries, illustrating the robustness and superiority of our method.
previous works Ji et al. (2022);
SUN-SEG-Fasy-Seer and SAN-SS--EEayy-Unee
In addition, to verify the cross-dataset generalizability of proposed method, conducted an additional experiment where the model was trained on the SUN-SEG dataset and directly evaluated on the CVC-612 dataset (including train and value set) without any fine-tuning. As shown in table
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Table 7: More detailed quantitative comparison with different SOTA methods on SUN-SEG test sets.
SUN SEG-Easy
SUN-SEG-Han
Method
F
Fr
Dice
5
Fi"
Sen
Dice
SLT-Net
93.46
96.23
89.11
91.35
89.94
91.20
90.28
93.90
84.01
87.02
85.81
87.0s
ZoomNeXi
94.00
90.86
92.84
92.40
90.78
93.72
88.77
84.72
AutoSAM
90.19
94.22
83.20
85.77
89.08
86.49
85.99
91.42
76.84
80.26
85.69
81.30
Seen
WeakPolyp
93.64
96.61
89,42
91.24
91.52
91.61
92.22
95.68
86.78 78
89.51
88.35
89.49
PNS+
91.35
92.40
84.72
87.88 86
83.83
88.34
88.46
92.06
80.29
84, 84.93
78.07
85.14
MAST
92.58
96.29
87.84
90.91
88.18
89.98
89.24
94.28
83.29
87.76
83.24
86.49
SALI
93.86
96.52
90.51
92.14
91.06
92.16
90.59
94.08
85.65
88.17
86.58
87.68
VP-SAM
94.35
96.94
90.82
92.77
90.74
92.77
92.00
95.54
86.51
88.59
87.74
89.87
Ours
94.47
97.26
91.89
93.72
90.91
93.05
92.32
95.78
88.46
91.14
87.69
90.31
SLT-Net
87.32
91.27
79.59
83.59
79.07
83.10
90.20
76.61
80.64
77.59
80.06
ZoomNeXt
85.62
88.16
78.42
82.99
82.70
86.24
88.96
78.35
82.41
77.14
82.35
AutoSAM
82.37
89.16
73.30
77.78
74.74
76.05
81.19
88.40
69.32
73.41
75.75
73.20
Unseen
WeakPolyy
87.38
90.81
80.36
84.41
78.33
83,53
88.16
91.86
80.70
84.34
81.35
83.97
PNS+
80.15
79.82
67.56
73.05
63.04
75.48
79.50
79.30
65.21
70.99
62.33
73.50
MAST
83.24
89.45
74.96
80.96
73.97
78.90
85.64
91.36
77.25
82.12
79.04
81.09
SALI
85.04
89.50
76.95
81.11
75.56
79.98
87.79
92.34
80.45
83.87
82.18
83.40
VP-SAM
86.63
90.42
80.46
83.64
77.65
83.61
88.06
91.94
80.09
82.94
81.71
84.01
Ours
87.39
91.04
81.59
85.87
78.45
84.87
88.24
92.14
81.88
85.63
81.41
84.79
Table 8: Cross-dataset generalization performance. directly CVC-612.
The model was trained on SUN-SEG and tested
CVC-612
Model
Backbone
S.
E
F3
Fgae
Sen
Dice
SLT-Net
PVIv2-B5
89.58
91.62
84.85
88.35
81.12
86.29
ZoomNeXt
PVTv2-B5
89.51
91.57
84.72
88.21
80.97
86.24
AutoSAM
ViT-B
85.35
89.85
78.92
83.56
75.12
80.45
WeakPolyp
PVTv2-B5
88.74
91.09
82.77
86.24
81.45
84.79
PNS+
Res2Net-50
87.52
90.45
81.24
85.10
78.50
83.65
MAST
PVTv2-B2
85.89
90.21
79.39
84.28
75.93
81.46
SALI
PVTv2-B5
89.62
91.14
84.91
87.54
81.07
86.32
VP-SAM
VIT-B
89.68
92.25
85.42
88.85
81.55
86.95
Ours
PVTv2-B5
89.74
93.07
85.89
89.31
81.87
87.36
our method achieves competitive performance even on unseen data distributions, demonstrating its strong generalization capability.
A.5 MORE VISUAL COMPARISON RESULTS
We demonstrate a qualitative comparison between our method and other state-of-the-an the-art methods on the CVC-612 dataset, shown in fig. TO To comprehensively demonstrate the effectiveness of the Inter-frame Divergence Perception (IDP) module, particularly its ability to maintain temporal consistency under rapid motion, provide two additional visual ablation examples in this section. As shown in fig. [1 and fig. [2] the IDP module significantly reduces segmentation jitter and en- sures robust tracking compared the bascline. In addition, demonstrate the superior temporal consistency of our method, we provide visual comparisons of two video sequences, each consist- ing of seven consecutive frames, as shown in fig. [3] and fig. [4] The results demonstrate that our method excels in both distinguishing polyps from the background and ensuring excellent temporal consistency.
16

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Published as a conference paper at ICLR 2026
AutoSAM
Figure 10: More visual comparison results of our method with other state-of-the-art methods on the CVC-612 test set. Red, green and yellow represent the GT, prediction and their overlapping regions, respectively, Please zoom in for more details.
time
Figure 11: More visual ablation of IDP SUN-SEG. Row 2: w/o IDP; Row 3: Ours. (Red: GT, Green: Pred, Yellow: Overlap).
time
Figure 12: More visual ablation IDP on SUN-SEG. Row w/o IDP; Row 3: Ours. (Red: GT, Green: Pred, Yellow: Overlap).
17

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Published as a conference paper at ICLR 2026
WeakPolyp
VI-SAM
Figure 13: Frame-by-fraid visual comparison of 7-frame video sequence selected from the SUN-
SEG dataset. (Red: GT, Green: Pred, Yellow: Overlap).
STL-Net ZoomiNeX
WeakFPlyp
MAST
VP-SAM
Figure 14: More frame-by-frame visual comparison of 7-frame video sequence on the SUN-SEG test set. (Red: GT, Green: Pred, Yellow: Overlap).
18


Reviews:
[{"review_id": "MrndmfjFOd", "reviewer": "Reviewer_1y5o", "summary": "The paper presents a video polyp segmentation framework composed of two main modules: Hierarchical Wavelet-based Feature Aggregation (HWFA) and Inter-frame Divergence Perception (IDP) blocks. In the encoder, the HWFA module amplifies information from both high- and low-frequency features to address the camouflage property of polyps. The IDP module in the decoder captures inter-frame divergence to enhance temporal consistency.\nMain contributions:\n- The framework considers both intra-frame discriminative features and inter-frame divergence for improved segmentation performance.\n- It accounts for both segmentation accuracy and real-time efficiency by evaluating GFLOPs and FPS.\n- An extensive ablation study is conducted to validate the effectiveness of each component.", "questions": "- It should be explained how evaluating the model on CVC-612, a static dataset without temporal relationships, effectively reflects the video-specific contributions of the proposed framework.\n- The train-test split for CVC-612 should be provided to clarify how the dataset was used for training and evaluation.\n- Frame-wise qualitative results or visualizations should be provided to demonstrate the effectiveness of the IDP module in capturing inter-frame divergence.\n- Description for the CA+SA module should be provided, as it is shown in Fig. 3.\n- Justification should be provided for why performance drops below the baseline in Table 2 when all modules except AFA are used.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Extensive experiments are conducted across multiple metrics, including GFLOPs and FPS, along with visualizations such as t-SNE plots for clearer interpretability.\n- A thorough ablation study is performed, analyzing the impact of the presence or absence of different modules.\n- Results are comprehensively compared against recent SoTA methods.", "weaknesses": "- The author mentions that they compared their results on two video polyp datasets, one of which is CVC-612. However, it is a static dataset. This dataset contains individual frames extracted from colonoscopy videos, which are static images and do not include temporal relationships. Therefore, validating a video model on such a static dataset may not effectively evaluate the video-specific contributions of the proposed model.\n- The train-test split for CVC-612 is not described and should be specified.\n- Since the IDP module is designed to capture inter-frame information, some frame-wise qualitative results are needed to validate its effectiveness.\n- In Fig. 3, the CA+SA module is shown, but it is not discussed in detail in the text.\n- In Table 2, it appears that when all modules except AFA are used, performance drops even below the baseline (top row). This behavior requires justification.\n\nMinor:\nIn Section 3.1, the sentence “Due to the dynamic nature of video” should be rephrased, as it seems incomplete or unclear.", "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": "kag5AVDtAQ", "reviewer": "Reviewer_YTma", "summary": "This paper proposes WavePolyp, a method for video polyp segmentation (VPS) in colonoscopy videos. The model targets two key challenges: (i) high camouflage of polyps within surrounding tissue and (ii) significant inter-frame divergence (shape, size, position, boundary changes) that causes temporal inconsistency. WavePolyp contains two main components:\n\n•\tHierarchical Wavelet-based Feature Aggregation (HWFA): Applies discrete wavelet transform (DWT) to multi-level encoder features, splits into low-frequency (LF) and high-frequency (HF) components, refines them via bespoke LF/HF calculation units (LFC/HFC) with attention and normalization strategies, and aggregates them across scales by an Ascending Frequency-guided Aggregation (AFA) unit. The goal is to excavate fine-grained intra-frame discriminative cues (edges, textures in HF; illumination/color in LF).\n\n•\tInter-frame Divergence Perception (IDP): A temporal module that computes frame-wise differences via a temporal shift (TSM-like) operation, modulates them with a learnable projection, and applies a time-only attention to emphasize regions with meaningful temporal changes. A two-layer temporal convolution diffuses the divergence signal, and features are fused in a coarse-to-fine decoder.\nExperiments on SUN-SEG (Easy/Hard splits) and CVC-612 show improvements over a set of baselines, including SLT-Net, ZoomNeXt, SALI, VP-SAM, and others, with competitive FPS on RTX 3090. Ablations attribute gains to both HWFA and IDP, and to specific internal design choices (normalization in LFC, AFA, divergent attention/diffusion). The paper reports favorable Dice, S-measure, E-measure, and weighted F across datasets, and discusses failure cases.", "questions": "1.\tAFA specification and reproducibility:\n•\tPlease provide the complete mathematical definition of AFA, including the “window-based linear model” parameters, the guidance weighting using W^HF/LF, and how the downsampled aggregated feature f_k^dh is computed. Is it a learned linear filter per window guided by W? Any regularization?\n\n•\tIn Appendix A.1, the optimization objective for LF AFA is truncated. Can you provide the full objective and solution or implementation details?\n\n2.\tNormalization details in LFC:\n•\tThe text says BN in HFC and IN in LFC, but Eq. (2) shows BN in LFC. Which is correct? If IN is used, please provide the exact formula and where PN/IPN sit relative to IN. How are µ′, σ′ computed (per-position, per-channel), and with what kernel sizes/parameterization?\n\n3.\tIDP attention scaling and complexity:\n•\tYou use Softmax(K^T Q / sqrt(HW)). Why is sqrt(HW) the appropriate scale? Do you use linear projections to reduce dimensionality before attention? If not, what are the memory/time costs for HW × T tokens, especially with 352×352 inputs? Please include exact tensor shapes and a complexity analysis.\n\n•\tWV is T × T and appears to hardcode the clip length. How does the module adapt to different T at test time, or is T fixed? Would a content-dependent dynamic filtering (e.g., 1D temporal conv) be preferable?\n\n4.\tBaseline completeness and significance:\n•\tCould you include comparisons with: (i) per-frame SOTA polyp segmenter plus temporal smoothing (e.g., Savitzky–Golay, EMA), (ii) an optical-flow-guided refinement baseline, and (iii) a temporal transformer with time-only attention but without divergence computation? This would isolate the incremental value of your divergence design.\n\n•\tPlease report mean±std across at least 3 runs for key metrics to ascertain the significance of improvements, especially over VP-SAM and SALI.\n\n5.\tMulti-scale pipeline at inference:\n•\tDo you run the tri-scale encoder (0.75×/1.0×/1.25×) at inference? If yes, what is the added latency and memory compared to single-scale? If not, how is multi-scale training transferred to single-scale inference?\n\n6.\tWavelet choices and learned variants:\n•\tWhich wavelet family (e.g., Haar, Daubechies) is used? Have you explored learnable wavelet filters or alternative frequency decompositions (e.g., DCT/Fourier), and how do results compare?\n\n•\tHow sensitive is performance to DWT level, number of scales, and the balance between HF/LF weighting?\n\n7.\tGeneralization and robustness:\n•\tPlease include cross-dataset generalization results (e.g., train on SUN-SEG, test on CVC-612 without fine-tuning) and robustness to common perturbations (blur, noise, brightness shifts), to validate claims of robustness to camouflage and inter-frame divergence.\n\n8.\tClinical practicality:\n•\tVP-SAM requires a point prompt; your method does not. However, your reliance on multi-scale processing and wavelet decomposition adds overhead. Can you provide a detailed breakdown of latency per module and avenues for lightweight deployment (e.g., replacing PVTv2-B5, pruning, quantization), including any preliminary results?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Originality:\n•\tUsing DWT to explicitly separate HF/LF feature bands and tailor distinct processing (HFC/LFC) in a VPS context is a thoughtful design. While wavelets have been used in other CV tasks, positioning them to address polyp camouflage with a hierarchical aggregation (AFA) is a creative application.\n\n•\tThe IDP block frames temporal modeling as learning inter-frame divergence via a simple yet principled TSM-based difference, time-only attention, and diffusion. It departs from heavier 3D/transformer-temporal designs by focusing on divergence emphasis with relatively low complexity.\n\n•\tThe multi-scale zooming scheme via ZoomNeXt-style feature merging complements the frequency-domain design, potentially improving robustness to size variation.\n\nQuality:\n•\tComprehensive experimental protocol on standard VPS datasets (SUN-SEG with Easy/Hard splits; CVC-612), multiple metrics, and a reasonably broad set of recent baselines, including both image and video methods.\n\n•\tAblation studies that dissect both macro-components (HWFA vs. IDP) and micro-components (HFC/LFC normalization, AFA, IDP’s attention/diffusion), plus exploration of clip length.\n\n•\tEfficiency analysis (params, GFLOPs, FPS) against video methods provides a useful view of the accuracy-efficiency trade-off.\n\nClarity:\n•\tThe high-level motivation and module roles (camouflage handled by HWFA; temporal divergence by IDP) are clearly articulated and connected to the challenges illustrated in Fig. 1.\n\n•\tThe pipeline overview (encoder with zoomed scales, HWFA, IDP decoder, final predictor) is easy to follow.\n\n•\tEquations and data flow for IDP are described sufficiently to reproduce the idea. The HFC/LFC and AFA figures are helpful.\n\nSignificance:\n•\tVPS is a clinically meaningful and actively researched area; improvements that bring both accuracy and near-real-time speed are valuable.\n\n•\tThe approach offers a perspective shift: deliberately leveraging frequency-domain cues for medical temporal segmentation, which could transfer to other endoscopic or camouflaged lesion videos.", "weaknesses": "Novelty vs. prior art:\n•\tWavelet/decomposition-driven feature processing has been employed in medical segmentation (e.g., FEDER and related frequency-decomposition works) and image generation/denoising; the paper cites some but does not thoroughly differentiate its technical novelty beyond the specific arrangement (HFC/LFC/AFA) and application to VPS. More rigorous positioning against frequency-aware segmentation works (e.g., learnable wavelet bases, frequency-aware attention, Fourier-based filtering such as FFC/AFNO/FFT-based U-Nets) is needed to substantiate novelty.\n\n•\tThe IDP block conceptually resembles several recent “temporal difference + attention” modules: TSM/TDN-like frame differencing, TEA-style excitation of motion, and time-attention in transformer decoders. The paper cites TSM but not closely related temporal-difference attention models. A tighter comparative discussion is necessary to establish conceptual distinctiveness.\n\n\nMethodological specification:\n•\tThe AFA unit is central, but the mathematical description is incomplete. The appendix starts an optimization objective for LF AFA but cuts off; the main text mentions a “window-based linear model” inspired by Swin Transformer, yet does not concretely define the window filtering/weighting, parameterization, loss, or computations. Reproducibility of AFA as described is difficult.\n\n•\tThe LFC normalization stack mixes PN/IPN with BN in Eq. (2), although the text claims BN in HFC and IN in LFC; there is an inconsistency: the formula shows BN in LFC after PN, contrary to the stated IN replacement. If IN is truly used, the equation should reflect it. Moreover, the learnable µ′, σ′ details (how they are produced per position, per channel? with which conv kernel sizes? affine parameters?) are underspecified.\n\n•\tFor IDP, the shapes and exact axes for attention are a bit ambiguous: K, Q in R^{HW × T} suggests flattening spatial into tokens and attending over time only, but the normalization over sqrt(HW) is unusual (typically sqrt(d)). If d=HW, this is extremely large and may cause scale issues; justification or reparameterization (e.g., linear projections to lower d) is not provided. Complexity claims should be reconciled with potentially large HW × T multiplications.\n\n•\tThe learnable WV is a T × T matrix. If T is variable at test time, how is WV handled? If fixed T=5 always, this should be stated as a constraint. Also, the two T × 3 × 3 convolutions imply grouping or depthwise temporal convolution per frame with spatial 3x3 kernels, but the notation is nonstandard; more detail would help.\n\nExperimental rigor:\n•\tThe baseline list is decent, but misses some recent strong VPS/medical video temporal models (e.g., DINOv2-temporal adaptations, token mixers with long-range memory, or optical-flow-based medical segmentation), and does not compare to simple but strong baselines like “per-frame SOTA IPS + temporal smoothing/post-processing.” Including a naive temporal regularization baseline could calibrate the added value of IDP.\n\n•\tStatistical significance or variability is not reported (no std/CI across runs or seeds). Given close margins over VP-SAM and SALI on some metrics, significance analysis is warranted.\n\n•\tThe clip length ablation is limited to a single dataset and reports only a curve; numeric values and an analysis of inference-time latency/memory vs. T would be useful for practitioners.\n\n•\tThe speed benchmark uses batch size 1 on RTX 3090; ensuring comparable precision settings (FP32 vs. AMP), input size, and consistent pre- and post-processing is important. Also, the model depends on PVTv2-B5 and ZoomNeXt merging; an ablation on the backbone and the multi-scale merging would contextualize where the gains originate.\n\n\nGeneralization and data:\n•\tOnly two datasets (one large, one small) are used; cross-dataset generalization (train on SUN-SEG, test on CVC-612 without fine-tuning) would be informative.\n\n•\tRobustness to common endoscopic artifacts (specular highlights, motion blur, occlusions), lighting changes, and strong peristalsis is only qualitatively discussed; quantitative robustness tests (e.g., controlled corruption benchmarks) are missing.\n\n•\tThe method benefits from training on three zoom scales; at inference, is the multi-scale encoder always used (computational overhead)? The text implies that multi-scale features are merged at 1.0x; an explicit statement of the inference-time pipeline and cost would help.\n\n\nClarity/consistency issues:\n•\tSome figures/equations appear truncated or inconsistent (e.g., AFA math in appendix). Equation (8) references DWTh/DWTl applied to fk; it’s not entirely clear whether DWT is applied at each level to encoder features vs. features already processed by prior steps. A tidy pseudo-code or module-by-module dimensionality flow would resolve ambiguities.\n\n•\tThe reliance on ZoomNeXt’s multi-scale merging is nontrivial; a short recap or an ablation “w/o multi-scale merging” is needed to attribute gains correctly to HWFA/IDP vs. upstream feature enrichment.", "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": "BEGCN1AuG0", "reviewer": "Reviewer_bCRF", "summary": "This paper proposes WavePolyp, a novel framework for video polyp segmentation (VPS) that addresses two key challenges: intra-frame feature discrimination in highly camouflaged polyps and inter-frame temporal inconsistency. The method introduces two core components: a Hierarchical Wavelet-based Feature Aggregation (HWFA) module that decomposes and enhances multi-level features in the frequency domain, and Inter-frame Divergence Perception (IDP) blocks that explicitly model temporal changes between frames. Extensive experiments on SUN-SEG and CVC-612 benchmarks show that WavePolyp outperforms existing state-of-the-art methods while maintaining near real-time inference speed.", "questions": "See the weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Comprehensive Experiments and Ablations: The paper provides thorough ablation studies to validate the contribution of each component (HFC, LFC, AFA, IDA, IDD). Results show consistent improvements across multiple metrics and datasets, with particularly strong gains on the challenging SUN-SEG-Hard subset.\n2. Efficiency-Aware Design: The model achieves a good balance between accuracy and inference speed (23.04 FPS), making it suitable for real-time clinical applications.", "weaknesses": "1. Limited Conceptual Justification for Frequency-Based Design:\nWhile the HWFA module is motivated by the idea that polyps exhibit discriminative features in both high- and low-frequency domains, the paper does not provide strong empirical or theoretical evidence for this claim. For example, visualizations of frequency-domain feature maps or analyses of polyp characteristics in the frequency space are missing.\n2. Limited Conceptual Justification for Frequency-Based Design:\nWhile the HWFA module is motivated by the idea that polyps exhibit discriminative features in both high- and low-frequency domains, the paper does not provide strong empirical or theoretical evidence for this claim. For example, visualizations of frequency-domain feature maps or analyses of polyp characteristics in the frequency space are missing.\n3. Narrow Evaluation Scope: All experiments are limited to polyp segmentation. While the method is claimed to be general, no experiments on other video object segmentation benchmarks (e.g., DAVIS, YouTube-VOS) are provided to demonstrate broader applicability. The comparison with non-medical VPS methods is limited, making it difficult to assess the generalizability of the proposed components.\n4. Lack of Novelty in Wavelet-Based Design and Inadequate Related Work: \nThe related work in Section 2.2 fails to establish a clear and substantive distinction between the proposed HWFA module and prior wavelet-based methods in vision tasks (e.g., SDWNet, LAAT, FEDER). The claim of novelty appears overstated. The application of DWT for feature decomposition, followed by attention mechanisms and hierarchical aggregation, is a known paradigm. The paper does not convincingly answer: What is the fundamental architectural or methodological leap here? The HWFA module, while well-engineered, comes across as a straightforward application of existing frequency-domain concepts to a new dataset, rather than a conceptual breakthrough.\n5. The \"Divergence Perception\" in IDP is Unfocused and Potentially Noisy. The core mechanism of the IDP block is to compute a frame difference Diff = Shift(V) - V and modulate it via attention. However, this is a generic, low-level motion signal that captures all changes between frames, not just those pertaining to the polyp. This design is highly susceptible to noise from irrelevant background motion, such as camera jitter, fluid flow, or intestinal wall movements. The paper provides no evidence that the IDP block selectively focuses on polyp-specific divergence. Without an explicit mechanism to ground the divergence signal to the polyp region (e.g., using the current prediction as a guide), it is likely that the model is learning from a noisy signal, which could harm robustness in complex scenes. The failure cases in Fig. 7 (e-f) with overlapping intestinal walls might be a direct consequence of this issue.\n6. The Overall Architecture Lacks Conceptual Novelty. The combination of DWT for multi-scale frequency analysis and attention mechanisms for feature refinement is a well-established design pattern in contemporary literature. The paper does not demonstrate that the proposed HWFA or IDP blocks represent a significant departure from this pattern. The HWFA module can be largely viewed as a specific instantiation of a multi-scale feature refinement network that uses DWT as its decomposition tool, rather than a fundamentally new operator. The incremental nature of the architectural design is a significant drawback for a paper aiming for a top-tier conference.\n7.  Performance Gains Are Marginal and Not Compelling. A close inspection of Table 1 and Table 3 reveals that the performance advantage of WavePolyp is not decisive. On SUN-SEG-Hard, WavePolyp (Dice: 87.55) outperforms ZoomNeXt (Dice: 85.22) by 2.33 points, which is a solid but not groundbreaking improvement. More critically, on SUN-SEG-Easy, the gap is much smaller (WavePolyp: 88.96 vs. ZoomNeXt: 87.55, a 1.41-point difference).  When considering efficiency in Table 3, WavePolyp (23.04 FPS, 86.63M Params) and ZoomNeXt (22.48 FPS, 84.78M Params) are nearly identical in speed and model size, yet the performance delta is minimal. This suggests that the proposed complex modules (HWFA + IDP) offer only a marginal benefit over a much simpler and more general baseline, which severely undermines the claim of a significant advancement.", "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)