DAMamba: Vision State Space Model with Dynamic Adaptive Scan
Authors: Tanzhe Li
Caoshuo Li
Jiayi Lyu
Hongjuan Pei
Baochang Zhang
Taisong Jin
Rongrong Ji
Venue: NeurIPS 2025 poster ()
Keywords: State Space Model
Image classification
Object detection
Image segmentation
Primary area: deep_learning

Abstract: State space models (SSMs) have recently garnered significant attention in computer vision. However, due to the unique characteristics of image data, adapting SSMs from natural language processing to computer vision has not outperformed the state-of-the-art convolutional neural networks (CNNs) and Vision Transformers (ViTs). Existing vision SSMs primarily leverage manually designed scans to flatten image patches into sequences locally or globally. This approach disrupts the original semantic spatial adjacency of the image and lacks flexibility, making it difficult to capture complex image structures. To address this limitation, we propose Dynamic Adaptive Scan (DAS), a data-driven method that adaptively allocates scanning orders and regions. This enables more flexible modeling capabilities while maintaining linear computational complexity and global modeling capacity. Based on DAS, we further propose the vision backbone DAMamba, which significantly outperforms popular vision Mamba models in vision tasks such as image classification, object detection, instance segmentation, and semantic segmentation. Notably, it surpasses some of the latest state-of-the-art CNNs and ViTs.

DAMamba: Vision State Space Model with Dynamic Adaptive Scan
Tanzhe Li1.2,3, Caoshuo Li Jiayi Lyu', Hongjuan Pei', Baochang Zhang", Taisong Jin 12.33 Rongrong Ji
Key Laboratory of Multimedia Trusted Perception and Effcient Computing,
Ministry of Education of China, Xiamen University, China
School of Informatics, Xiamen University, China
Fuujan Ocean Innovation Center, China
School of Engineering Science, University of Chinese Academy of Sciences, China
School of Artificial Intelligence, Beihang University, China {litanzhe, caoshuoli Cstu. xmu. edu. en, , jintaisongGxmu edu, cn
Abstract
State space models (SSMs) have recently garnered significant attention in computer vision. However, due to the unique characteristics of image data, adapting SSMs from natural language processing to computer vision has not outperformed the state-of-the-ard convolutional neural networks (CNNs) and Vision Transformers (ViTs). Existing vision SSMs primarily leverage manually designed scans to flat- ten image patches into sequences locally globally. This approach disrupts the original semantic spatial adjacency of the image and lacks flexibility, making it dif- ficult capture complex image structures. To address this limitation, we propose Dynamic Adaptive Scan (DAS). data-driven method that adaptively allocates scanning orders and regions. This enables more flexible modeling capabilities while maintaining linear computational complexity and global modeling capacity. Based on DAS, we further propose the vision backbone DAMamba, which signifi- cantly outperforms popular vision Mamba models in vision tasks such image classification, object detection, instance segmentation, and semantic segmentation. Notably, it surpasses some of the latest tfate-of-the-an CNNs and ViTs. Code is available at /https://gi thub. com/ http///ttooo/aaaaa
Introduction
In recent years, to tackle the limitations of traditional convolutional neural networks (CNNs) 33 in modeling long-range dependencies, Transformer [51] have been introduced into computer vision, achieving state-of-the-art performance in image classification task. However, the self-attention mechanism within Transformer, due to its quadratic computational complexity, faces limitations when applied to high-resolution vision downstream tasks such as object detection and image segmentation. To address this issue, researchers have proposed various sparse attention mechanisms [54] [58] 69 27 67 44. These mechanisms reduce complexity by introducing sparsity into attention computations, but this usually comes at the expense of the model's global modeling capability, limiting their performance some vision tasks.
State space models (SSMs) [13], represented by Mamba [12,. have recently garnered significant attention from researchers. The core module, the S6 block, selectively retains discards information
'Equal Contribution
CCoresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a) Sweeping Scan
(b) Continuous Scan
(c) Local Scan
(d) Dynamic Adaptive Scan
Figure Illustration of different scanning methods vision state space models. As showed in
Figure (a). (b), and (c): previous methods such as Vim [70]. VMamba (35], PlainMamba [6]], and LocalMamba [25] relies on manually designed global or local scanning methods. These fixed processing approaches lack flexibility and struggle to capture complex image structures. In the Figure (d). we propose novel scanning method that adaptively allocates scanning order and regions through data-driven approach. This not only achieves more flexible modeling capabilities but also maintains Mamba's linear computational complexity and global modeling capacity.
based on the relevance of each element in sequence. By incorporating a selective mechanism for parallel computation alongside hardware-awan optimization, the S6 block not only offers comprehensive global receptive field but also attains a computational complexity that scales linearly with the sequence length. This characteristic enables Mamba to outperform popular Transformer models in natural language processing tasks. Inspired by Mamba, some research efforts, such as Vim [70] and VMmmba (35), have extended its application computer vision. These approaches divide 2D images into patches and adopt specific scanning strategies to flatten the images into multiple ID sequences from different directions. This successfully integrates the Mamba model into vision tasks, achieving promising performance and showcasing the potential of SSMs in computer vision.
Unlike one-dimensional sequential language data, visual data typically exhibits two-dimensional spatial structure. One of the core challenges in adapting the Mamba model for vision tasks lies in designing an appropriate scanning strategy for an image. Scanning strategy enables SSMs, which are designed for 1D sequence processing, to effectively accommodate the 2D spatial structure of images. Currently, scanning strategies for vision SSMs can be broadly categorized into three types: sweeping scan, continuous scan, and local scan. Vim and VMamba adopt the sweeping scan strategy, simulating a row-by-row scan from left right and top to bottom, allowing Mamba to adapt to the 2D spatial structure of images. However, PlainMamba [61] argues that sweeping scan overlooks the importance of spatial continuity within images and thus introduces the continuous scan strategy to ensure the correlation between adjacent patches. Mcanwhile, LocalMamba (25) proposes the local scan strategy, aiming to capture the local spatial relationships within images.
Although the aforementioned methods have proven effective in practice, they rely on manually designed scanning patterns that are independent of the input data, which may not optimal. For instance, sweeping and continuous scan can cause spatially close patches to become distant in SSM computations, resulting in loss of local information. On the other hand, local scan can capture local spatial relationships. limiting the model's ability to capture long-range dependencies. Clearly, there need for a more flexible scanning strategy that can dynamically adjust the scanning regions based on the characteristics of each input data instance. For example, in the case of an image of dog, an idcal scan strategy should adaptively focus on the dog's ' body while filtering out irrelevant background information. However, such dynamic adjustment is beyond the capabilities of cxisting manually designed scanning approaches.
To address the aforementioned issues, we propose a flexible and efficient scanning strategy, named Dynamic Adaptive Scan (DAS). Unlike traditional manually designed scanning methods, DAS dynamically learns and adjusts scanning regions and their sequences during training, enabling smarter and more precise feature extraction. Specifically, DAS starts by defining set of learnable positions, with initial values corresponding to the original locations of each patch. Then, through a learnable offset prediction network (OPN), set of offset values generated for each patch. By combining these offset values with the original patch positions, the predicted patch positions are computed.

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Using bilinear interpolation, these predicted positions are gradient- to the feature map, allowing the offsets to be adaptively optimized during training. The predicted patches are arranged from top to bottom and left to right based on their original positions, dynamically forming new sequence order according to the input data. Through this mechanism, DAS focuses on more critical regions, capturing important features and complex spatial structures with greater flexibility.
Based on the proposed DAS, we develop a powerful vision Mamba model, termed DAMamba. DAMamba can serve as a versatile vision backbone for various vision tasks. For instance, our DAMamba-1 achieves an image classification accuracy of 83.8%, 48.5 APb in object detection, 43.4 APT in instance segmentation, and 50.3 mloU in semantic segmentation. These results surpass the previous state-of-the-an Vision Mamba, VMamba, by 1.2% in classification accuracy. 1.2 AP,, 0.7 APm, and 2.3 mloU. Moreover, DAMamba also outperforms some recent state-of-the-as ViTs and CNNs these vision tasks, demonstrating superior performance and various applicability.
2 Related Work
2.1 Vision State Space Models
Although the Transformer [51 has achieved remarkable success in natural language processing, its quadratic complexity poses challenges when handling long sequence structures. To address this issue, state-space models [ (SSMs), represented by Mamba [12,, have gradually emerged as an alternative to Transformers. In visual tasks, the quadratic complexity of the standard self-attention mechanism similarly presents challenges for processing high-resolution images. Thus, the Vim [70] and VMamba (35) attempt to incorporate Mamba into computer vision tasks. However, inputting images into SSM models remains a critical challenge. Vim and VMamba address this by employing bidirectional and diu--diretiooal scamning strategics to transform image patches into one meedimensional sequences. Building on this, subsequent research introduced continuous scamning [6]] and local four-directional scan 25 to better align with the two-dimensional structure of images. Despite the significant achievements of Mamba models in computer vision, existing scanning methods heavily rely on manual design, making it difficult to dynamically and flexibly adapt to input variations. This limitation hinders the model's s ability capture complex two-dimensional structures. Therefore, our goal is to propose vision Mamba model capable of adaptively and flexibly adjusting scanning paths based on input image, further enhancing its performance in vision tasks.
2.2 Vision Transformers
The Transformer 51 model was first introduced in 2017 for natural language processing (NLP) tasks. With its powerful global modeling capabilities and excellent parallelism, the Transformer quickly gained popularity in the NLP. By the end of 2020, Vision Transformer [10] (ViT) successfully extended the Transformer model to large-scale image classification tasks, achieving state-of-the-art performance. Subsequently, DeiT [49] improved ViT by introducing knowledge distillation [20]
and more efficient training strategies, enabling effective training even on relatively small datasets such ImageNet- LK [43]. Following this development trajectory, researchers proposed numerous hicrarchical Transformer models that reduce computational complexity for high-resolution images through various sparse attention mechanisms. Notable examples include the Swin Transformer (36)
introduced various sparse attention mechanisms strike balance between global modeling capability and computational complexity. However, the global modeling capabilities of these improved sparse attention mechanisms still fall short of the standard self-attention mechanism.
and PVT [54]] 55.. Subsequent research [54
55 58
69
27
67
44
2.3 Convolutional Neural Networks
Convolutional Neural Network (CNN) BO was initially proposed for handwritten digit recognition, but it wasn't until the introduction of AlexNet (9] in 2012, which triggered the "ImageNet moment," that the full potential of CNNs was realized. This breakthrough led to a rapid development in computer vision, driven by the resurgence neural networks, with CNNs becoming the standard architecture for computer vision tasks. During this period, many representative CNN models emerged, such as VGG [45), GoogLe [46), ResNet [18], DenseNet (33, DCN 66 53), and EfficientNet [48). These models focused on different aspects, including accuracy, efficiency, and scalability, while promoting
3

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valuable design principles. In recent years, inspired by ViTs, some CNNs [37 57 34 have incorporated large kernel convolutions capture long-range dependencies, achieving performance competitive with VIT. At the same time, CNNs have been widely integrated into various ViT's and vision Mambas to enhance local modeling capabilities, creating a complementary synergy between the two approaches. These advancements have driven the diversification and convergence of model design in vision tasks.
Methodology
3.1 Preliminaries
Linear
Bilntar Interpdaatinn
LN
Reference Point
Prodicted
SSM
OPN
Offfeet Adding Scan Path
(n) Dynamic Adaptive Scan
(b) OlTset Prediction Network
(OPN)
Figure Illustration of the proposed Dynamic Adaptive Scan (DAS), For clarity, only four reference points are shown. Left: each initial reference point represents the original position of patch, with its offsets learned by an Offset Prediction Network (OPN). Features of important regions are sampled based on the predicted 2D coordinates using bilinear interpolation. Right the detailed structure of the OPN revealed. The query feature map is first transformed through depthwise convolution [2] to integrate local information. Then, another linear layer, after layer normalization and GELU [19 activation, converts the feature map into offset values.
State Space Models (SSMs) 13 are a class of sequence modeling methods commonly used in deep learning, capable of representing dynamic systems through an intermediate latent state h(tt) E R.V. Their core equations are as follows:
h'(t) = Ah(t) + Bx(t), v(t) Ch(t),
where the system matrices A E R. NNN, B E NNI, and C E IRIN govern the dynamic evolution and output mapping.
To implement continuous-tins models in practice, discretization techniques are required. The commonly used Zero-Order Hold (ZOH) method keeps the input constant within each time interval, transforming the continuous-time parameters (A, B) into their discrete forms as follows:
A B =
- I)AB,
where A represents the sampling time scale. The resulting discretized model can then be expressed
h = Ah 1 + Bxg, Ye = Ch,
This method not only supports efficient parallel computation but also directly generates sequence outputs through convolution operations:
= x K, K= =CB,CAAM ... CA
y
where K E R is the SSM kernel, and x denotes the convolution operation. This parallelization significantly enhances computational efficiency and scalability.
Although traditional SSMs (such as $4 (3) achieve linear time complexity, their static parameteriza-
tion limits their ability to capture the sequence context. To overcome this limitation, the Mamba [2]

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ConviFNN
BN
DiMambba
Dounsample
DAMambe
Dovnsample
Dinalaabb
Downsomph
DAbiamba
Sleen
Block
Block
Block
DLS DET SEG
LN
XM
XX
X.N
Input
Stage
Stage 3
Stage
Output
DAMamba Block
Figure Left: The overall architecture of the proposed DAMamba, refer to Table for configurations. Right: Details of an DAMamba Block.
introduces dynamic and input-dependent parameterization mechanism. Unlike traditional models that use constant transition parameters A, B, the Mamba model dynamically computes the parameters B 3XLXN C' IR BxLN and A IR BxLD from the input sequence R _ This allows for richer and more sequence-aware dynamic modeling.
In Mamba, continuous- -ie parameters adaptively adjusted to input-dependent parameters through selective functions based on the input sequence 2;:
A 8 (xz), B₁ = (
=== ===so sc())),
and the input-dependent discrete parameters A, and B, can be calculated accordingly. The discrete state transition and observation equations are follows:
h A B Vt = C,h+-
The dynamic parametcrization the Mamba model not only improves sequence modeling perfor- mance but also demonstrates strong competitiveness in language modeling and vision tasks. For instance: Vim [70] combines bidirectional Mamba blocks to replace traditional Transformer blocks for visual modeling. VMamba [35] constructs hierarchical structure by introdueing 2D selective scanning, akin to the design of the Swin Transformer [36]. These advancements expand the ap- plication potential of SSMs in foundational vision tasks, further driving the development of SSM models.
3.2 Dynamic Adaptive Scan
we propose Dynamic Adaptive Scan (DAS), which effectively models the relationships between image patches under the guidance important regions in the feature map. These focused regions are determined by multiple sets of learnable sampling points, which are learned by an offset prediction network (OPN) from the input image feature map. After obtaining two-dimensional coordinates predicted by the OPN, we use bilinear interpolation to sample features from the feature map, and then input the sampled features into the SSM for feature aggregation. Furthermore, the positions of DAS provide stronger relative positional biases to facilitate the learning of the SSM.
As shown in Figure 2
Specifically, we first input the feature map into an OPN to predict the wwo-dimensional coordinate offsets Ap E I. H* x2 of the interested patches relative to the original patches:
Ap = OPN ([=1, 22, ,IN)),
(I)
Then, these offsets added to the positions of the original patches to determine the sampling locations of the interested patches:
p'(h, w. = plh, w + Ap(h, w.:).
where p' and p represent the sets of two-dimensional coordinates of the original patch and the interested patch on the feature map, respectively. p' and take values between -1 and 1, (-1,-1) denotes the upper left corner, and (1,1) denotes the lower right corner. h and w denote the coordinates of the image patch in the height and width directions.
Next, we leverage position p' feature sampling, which is performed through bilinear interpolation function. By establishing relationship between the image patch features and the offsets, the proposed OPN can adaptively learn.
X' (h, w| 6(p' [h, w), X),
5

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o(a, b) E g(a rws ay, Tyybly Tz,
(4)
(x2)gg)
e, max le- d) x
(1),
(5)
where g(c, d, f) represents the bi-linear sampling weight function, az = p' [h, w. 0l, ay p'[h, w, 11. and (f2)Ty) indexes all locations on X E "X takes a nonzero value only the four lattice points closest to the input location.
After obtaining the sampled feature vectors of interest, X', we arrange them in the order of their original patches from top to bottom and left to right, and then input them into SSM for feature extraction.
3.3 Architecture Design of DAMamba
Table I: Configurations for different DAMamba variants.
Models
Channels
Blocks
DAMamba-F
[48, 96, 192, 256]
[2, 2, 10,
DAMamba-T
[80, 160, 320, 512]
[3, 4, 12, 5]
DAMamba-S
[96, 192, 384, 512]
[4, 8, 20, 6]
DAMamba-B
[112, 224, 448, 640]
[4, 8, 25, 8]
we propose a novel vision backbone named DAMamba base on DAS, and develop model variants at four scales: DAMamba-F, DAMamba-T, DAMamba-S and DAMamba-B. First, the input image x E R. xW x3 is processed through a stem module, consisting of 4 overlapping x convolutions, to generate a 2D feature map of size through four stages of continuous operations, forming multi-scale hierarchical representation with W H W. and x 32 respectively. Each stage composed of multiple stacked DAMamba blocks, with resolution progressively downsampled by factor of 2 and the feature dimension increased. Additionally, the DAMamba architecture integrates convolutional and convolutional FFN (ConvFFN) (55) further enhancing the ability to extract local features. Throughout the architecture, DAMamba blocks are flexibly stacked across different stages adapt to various downstream tasks. Finally, the model's output undergoes batch normalization, global average pooling, and a linear classification head to produce the final image classification task feature representation. The configurations for different model scales are provided in Table
As shown in Figure 3
XC. These features are then processed
resolutions of 8 8. 16 16.
32
positional encoding 4 26 31
4 Experiments
4.1 Image Classification on ImageNet-1IK
Experimental settings: We conducted image classification experiments based on the ImageNet- I dataset [43. which consists of 1,281,167 training images and 50,000 validation images spanning 1,000 categories. The implementation of our experimental methods relied on the PyTorch [40] and Timm (56) libraries. To ensure fair comparison, we adopted the commonly used experimental settings from DeiT [49. The optimizer used was AdamW [88, with cosine decay learning rate schedule and linear warm-up over the first 20 epochs. The models were trained for 300 epochs on images with a resolution of 2242. For data augmentation and regularization, we employed techniques such RandAuugmentation (6). Repeated Augmentation (21). Mixup (65], CutMix [64], Random Erasing [68], weight decay, label smoothing [47], and stochastic depth [24]. For the small and base versions of the model, we also used layerscale [50] and mesa ||] to help train better. During testing, center cropping was applied to the validation images to generate input images with a resolution of 224 The experiments were conducted on 16 RTX 3090 GPUs. Notably, exponential moving average (EMA) [42] did not significantly improve the final model performance and was therefore not used in the experiments.
Results: As shown in Table
we compare the proposed DA DAMamba with several ofate-of-the-art models. The proposed DAMamba consistently outperforms VIT. CNN, and SSM models. Specifically,

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Table 2: Results of DAMamba and the current state-of-the-art backbones on ImageNet- -.. All the models are trained and tested at 224 224 resolution.
Type
(M)
Top-1 (98)
ConeNeXi V2-F 571
CNNs
0.8
78.0
Vim- XX
SSMs
1.5
76.
Ellkicm//Mambrr
111
SSM=
76.5
DAMambo (ours)
SSMs
1.3
79.1
SLaK-T
IncppiioNccI-T
CNN:
28
4.2
82.
MuniuCut-Tiny
CNNA
4.5
82.
Un:RepLKNe-TT
Internlmape-T
CNN:
Swin-T 140
VITS
29
4.5
81
CSwin-T
VITs
82.7
Apent-Suin-T
VITs
VITS
PVTv2-B2 55
VITS
26
1.0
82.0
ClosterFomme-
R7
VITs
81.5
NAT-T
VITs
83.2
QFommer
VITs
29
4.6
82.5
PartialForme-DD
53
VITs
83.0
Vim-S
SSM=
5.1
80.5
VMamba
83
SSMs
30
4.9
82.6
PlainMambs-L2 L2
61
SSMs
8.1
81.6
EllicmnVMambaa1
SSM=
81.8
FractalMamba--1
59
SSMs
31
4.8
83.0
DAMamba (ours)
SSMs
83.8
SLaK-S
CNNs
InceptionNXX-SS
63
CNN:
19
8.1
83.5
ManitaOut-Su Sunal
32
CNNs
84.1
UniRepl
Internlmage-S
CNNs
81.2
Swin-S [6
VITs
30
8.7
83.0
Agont-Sain-S
VITA
8.7
83.7
PVTv2-B4
VITs
10.1
83.6
DAT-S 58
VITS
50
9.0
K3
ClustetFoemer-Suu Small
37
VITs
83.4
@Fumer,
VITs
BiFommer-B
29
VITs
84.3
PartialFormes-B B1
VITs
61
6.8
83.9
SirucViT------1
VITs
12.0
84.3
VMambu-
SSMs
83.6
PlinMMamba-1.3
SSMs
30
14.4
82.
LocalVManbs:
25
SSMs
83.7
(ouns)
ConeNeXi V2-8
17
CNNs
89
15.4
84.3
SLak-B 14
CNNS
17.1
84.0
NecptionnNeXtBB
+3
CNNs
84.0
MunioOut-Base
CNN:
84.2
Internfmige-H 51
CNNS
97
160
84.9
Swin-B 36
VITs
15.4
83.5
Apont-San-B
VITs
NAT-B [6
VITs
90
13.7
81.3
PVTy2-B5 55
VITs
11.8
83.8
VITS
DAT-B
VITs
84.0
QFormer -B
VITs
90
15.7
84
TransNeXt- Base
VITs
81.8
VMambu-B
DAMamba-B (ours)
SSMs
DAMamba-B achieves accuracy of 85.2%, which is 1.3% higher than the current state-of-the- art SSM model (VMamba-B). Compared to the state-of-the-ar CNN (ConvNeXt V2) and VIT (TransNext), the proposed DAMamba-T shows significant improvement in accuracy. Even when the model is scaled to approximately 50M. 30M and 6M parameters, DAMamba achieves top-1 accuracies of 84.8%, 83.8% and 79.1% 1%, maintaining its excellent performance.
4.2 Object Detection and Instance Segmentation COC02017
Experimental settings: We conducted object detection and instance segmentation experiments on COCO 2017 dataset. The COCO 2017 dataset 33 consists of approximately 118K training images and 5K validation images and serves as commonly used benchmark for object detection

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Table 3: Comparison of object detection and instance segmentation performance on COCO with Mask R-CNN detector. FLOPs are calculated with input resolution of 1280 800.
Mask R-CNN 1x schedule
Backbone
APh
AP
AP
75
APm
AP 50
AP
#Param.
FLOPs
Swin-T
42.7
65.2
46.8
39.3
62.2
42.2
48M
267G
DAT-T
44.4
67.6
48.5
42.4
66.1
45.5
48M
272G
CSWin-T
46.7
68.6
51.3
42.2
65.6
45.4
42M
279G
ConvNeXi-T
44.2
66.6
48.3
40.1
63.3
42.8
48M
262G
PVTv2-B2
66.1
49.6
41.2
64.2
44.4
45M
309G
QFormer, TT
68.5
50.3
41.5
65.2
44.6
49M
PartialFormer-B3
45.0
40,9
54M
248G
BiFormer-S
47.8
69.8
52.3
43,2
66.8
46.5
MambaOut-T
45.1
67.3
49.6
41.0
64.1
44.1
43M
262G
VMamba-T
47.3
69.3
52.0
42.7
66.4
45.9
SOM
271G
Mamba-M
46.7
68.7
50.8
42.2
65.7
45.5
45M
291G
FractalMamba-T
46.8
68.7
50.8
42,4
41M
266G
DAMamba-T
48.5
70.3
53.3
43.4
67.2
46.7
45M
284G
Swin-S
44.8
68.6
49.4
40.9
65.3
44.2
69M
354G
Agent-Swin-S
47.2
69.6
52.3
42.7
66.6
45.8
364G
DAT-S
47.1
69.9
$1.5
42.5
66.7
45.4
69M
378G
CSWin-S
47.9
70.1
52.6
43.2
67.1
46.2
54M
342G
ComvNcXt-S
45.4
67.9
50.0
41.8
65.2
45.1
70M
348G
PVTv2-B3
47.0
68.1
51.7
42.5
65.2
45.7
63M
397G
BiFormer-B
48.6
70.5
53.8
43.7
67.6
47.1
MambaOut-S
47.4
69.1
52.4
42.7
66.1
46.2
65M
354G
VMamba-S
48.7
70.0
53.4
43.7
67.3
47.0
70M
349G
LnamlaMamba
4844
69.9
52.7
43.2
66.7
46.5
69M
414G
DAMamba-S
71.2
54.7
6SM
395G
Swin-B
46.9
69.2
51.6
42.3
66.0
45.5
8RM
496G
CSwin-B
70.4
53.9
43.9
88M
496G
ConvNcXi-B
47.0
69.4
51.7
42.7
66.3
107M
486G
PVTv2-B5
47.4
68.6
51.9
42.5
65.7
46.0
102M
557G
ViT-Adapter-B
47.0
68.2
$1.4
41.8
65.1
44.9
102M
557G
MambaOut-B
47.4
69.3
52.2
43.0
66.4
46.3
100M
495G
VMamba-B
49.2
71.4
54.0
44.1
68.3
47.7
108M
485G
DAMamba-B
50.6
71.9
55.5
105M
520G
and instance segmentation tasks. To evaluate the performance of the proposed model downstream vision tasks, we selected DAMamba as the backbone network and embedded it into a detector to extract object and instance features from images.DAMee DAMamba was integrated into the classic Mask R-CNN [17] detector and initialized with weights pre-tres tre-trained on the ImageNet- IK dataset for 300 epochs. For the object detection and instance segmentation tasks, we trained the model for 12 epochs (1x) and 36 epochs (3x). All experiments were conducted using the MMDetection framework.
Results: The object detection and instance segmentation results of DAMamba the COC02017 dataset are shown in Table 6 In terms of bounding box and mask average precision (APo and AP DAMamba demonstrates outstanding performance. Using a (1x) fine-tuning schedule, DAMamba-T/SII achieves object detection mAPs of 48.5/49.888 5/49.8/50.6 outperforming VMamba-T/SA by 1.2/1.1/1.4 mAP, Swin-T/S/B by 5.8/5.0/3.7 mAP, and ConyNeXt-T/S/BB by 4.3/4.4/3.6 mAP. Under the same configuration, the instance segmentation mAP of DAMamba-T/SSS also significant outperform VMamba-T/SS T/S/B, Swin-T/S/B and Con/NeXt-T/S/BB
4.3 Semantic Segmentation ADE20K
Experimental settings:
We conducted semantic segmentation experiments using the ADE20K dataset and performed a comparative analysis of DAMamba and other models within the UperNet [60] framework. In the UperNet framework. the backbone network was initialized with weights pre- trained on the ImageNet- 1K dataset, while the remaining parts were randomly initialized. The model optimization employed the Adam W optimizer with batch size of 16. To ensure a fair comparison, all models were trained for 160k iterations within the UperNet framework. All experiments were conducted using the MMMSegmentttion [5) framework.
Results: Table 4 presents the semantic segmentation results DAMamba under the UperNet 66) framework. The experiments show that DAMamba-T/SS achieves mloU scores of 50.3%, 51.2%,

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Table 4: Comparison of semantic segmentation on ADE20K with UPerNet segmentor. FLOPs are calculated with input resolution of 512 2048. 'SS' and 'MS' represent single-scale and multi-scale testing, respectively.
Method
mioU (SS)
mloU (MS)
#Param.
FLOPs
UniRepl.KNetT
61M
946G
ConvNeXt-T
46.0
46.7
60M
939G
Swin-T
44.4
45.8
60M
945G
Agent-Swin-T
46.7
61M
954G
NAT-T
47.1
48.4
58M
934G
QFormer, -T
46.9
48.1
61M
PartialFormer-B3
47.0
65M
923G
BiFormer-S
49.8
50.8
MambuOut-T
47,4
48.6
54M
938G
VMamba-T
48.0
48.8
62M
949G
LocalVMamba-T
47.9
49.1
57M
970G
FractalMamba-T
48.0
48.9
53M
942G
DAMamba-T
50.3
51.2
55M
937G
UniRepLKNNe--S
50.5
$1.0
86M
Swin-S
47.6
49.5
81M
1039G
Agent-Swin-S
48.1
8IM
G043G
ConvNeXt-S
48.7
49.6
82M
1027G
NAT-S
48.0
49.5
82M
1010G
Q
48.9
50.3
82M
PartilFormer-BB
48.3
95M
1005G
BiFormer-B
51.0
51.7
MambaOut-S
49.5
50.6
76M
1032G
VMamba-S
50.6
51.2
82M
1028G
LocalVMamba-S
50.0
51.0
8IM
1095G
DAMamba-S
51.2
$2.0
7SM
1050G
Swin-B
48.1
49.7
121M
1188G
Agent-Swin-B
48.7
121M
1966
ConvNcXt-B
49.1
49.9
122M
1170G
NAT-B
48.5
49.7
123M
1137G
QFormery-R -B
49.5
50.6
123M
MambaOut-B
49.6
$1.0
112M
1178G
VMamba-B
51.0
51.6
122M
1170G
DAMamba-B
51.9
52.3
117M
1178G
Table 5: Ablation studies DAMamba-F for module designs.
Module design #Param. (M) FLOPs (G) Top-1 acc (%).
Baseline
5.31M
1.20G
77.7
DAScan
5.41M
1.23G
78.3
Conypos
5,43M
1.24G
78.6
ConvFFN
5.52M
1.26G
79.1
and 51.9%, respectively, significantly outperforming other types of models. The performance improvement of our DAMamaba is also evident when using multi-scale tests. These results further validate the exceptional generalization capability of DAMamba in downstream tasks.
4.4 Ablation Study
To validate the effectiveness of our method, we conducted image classification ablation experiments on ImageNet- 1k using DAMamba-F Table Compared the baseline model with Sweeping Scan, our proposed Dynamic Adaptive Scan (DAS) improves the top-1 accuracy by 0.6% while consuming only small amount additional FLOPs and parameter overhead. Furthermore, the vision SSMs, which excels at global modeling, we observe that using convolutional positional encoding (Convpos) 26 31 and ConvFFN
55 local modeling can improve accuracy by 0.3%
and 0.5%, respectively.

---

5 Conclusion
In this paper, we have proposed a novel vision state space model, termed DAMamba. DAMamba significantly enhances the flexibility of modeling in vision SSMs and improves the ability to capture complex image structures, while maintaining both local and global contextual understanding. Specifi- cally, Dynamic Adaptive Scan mechanism is proposed to adaptively allocate scanning order and regions based on the input image. Extensive experiments on various datasets and popular vision tasks demonstrate that the proposed DAMamba significantly and consistently outperforms the state-of- the-art vision SSMs as well as popular ViT and CNN architectures, establishing new benchmarks for image classification, object detection, instance segmentation, and semantic segmentation. Our findings underscore the importance of the scanning mechanism in vision SSMs and highlight the tremendous potential of SSMs vision backbone.
Acknowledgements
The work was Supported by the National Science Fund for Distinguished Young Scholars NNo.62255633 and National Natural Science Foundation of China (Nos. supported by Fujian Provincial Natural Science Foundation
62320106007, (0000000002222866666 of China (No. 2025101003), Supported by Fujian Ocean Innovation Center (No. 25FVOCZ107), supported by Yunnan Provincial Major S&T Special Plan Project (No. 202402AD080001), Beijing Natural Science Foundation (No. L254018). 8). Joint Fund of the Science and Technology R&D Program of Henan (No. 23520081003 1). Jing-Jin-Ji Incorporation Project of the Natural Science Foundation of Hebei Province (No. H2024202009), supported by the Open Foundation of Key Lab of Oracle Information Processing of MOE (No. OIP2024E002).
References
[1] Jimmy Ba, JamieRyan Kiros, and GeoffreyE. Hinton. Layer normalization, Jul 2016.
[2] Kai Chen, Jiaqi Wang, Jiangmiao Pang. Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, et al. Mmdetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906. 190..07655,55 2019.
[3] François Chollet. Xception: Deep learning with depthwise separable convolutions. In CVPR, pages 1251-1258, 2017.
Xiangxiang Chu. Zhi Tian, Bo Zhang. Xinlong Wang, and Chunhua Shen. Conditional positional encodings for vision transformers. ICLR, 2023.
[5] MMSegmentation Contributors. Mmsogmentation: Openmmlab semantic segmentation toolbox and benchmark, 2020.
[6] Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with reduced search space. In CVPRW, pages 702-703, 2020.
[7] Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding. Scaling up your kernels 31x31: Revisiting large kernel design in cans. In CVPR, pages 11963-11975. 2022.
[8] Xiaohan Ding, Yiyuan Zhang, Yixiao Ge, Sijie Zhao, Lin Song, Xiangyu Yue, and Ying Shan. Unireplknet: A universal perception kerell convnet for audio video point cloud time-series and image recognition. In CVPR, pages 5513 5524, 2024.
[9] Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, and Baining Guo. Cswin transformer: A general vision transformer backbone with cross-shaped windows. In CVPR, pages 22124-12144 2022.
[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at seale. In ICLR, 2021.
[11] Jiawei Du Daquan Zhou, Jiashi Feng, Vincent Tan, and Joey Tianyi Zhou. Sharpness- aware training for free. NeurlPS, 35:23439 2232111223451111..111 2022.
10

---

[12] Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint urXiv:2312.00755, 2023.
[13] Albert Gu, Karan Goel, and Christopher Ré, Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00300 2021.
[14] Dongchen Han, Xuran Pan, Yizeng Han, Shiji Song, and Gao Huang. Flatten transformer: Vision transtormer using focused lincar attention. In ICCV, pages 5961-5971, 2023.
[15] Dongchen Han, Tianzhu Ye, Yizeng Han, Zhuofan Xia, Siyuan Pan, Pengfei Wan. Shiji Song, and Gao Huang. Agent attention: On the integration of softmax and linear attention. In ECCV, pages 124-140. Springer, 2024.
[16] Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi. Neighborhood attention transformer. In CVPR, pages 6185-6194, 2023.
[17] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In ICCV, pages 2017.
[18] Kaiming He, Xiangyu Zhang. Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.
[19] Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arXiv preprint 2016.
[20] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in neural network. arXiv preprint arXiv: 3003v:/5500022 503.02531, 2015.
[21] Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, and Daniel Soudry. Augment your batch: Improving generalization through instance repetition. In CVPR, pages 8129-8138, 2020.
[22] Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv: 1704.04861, 2017.
[23] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected 2017.
convolutional networks. In CVPR, pages
[24] Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger. Deep networks with stochastic depth. In ECCV, pages 646-661. Springer, 2016.
[25] Tao Huang, Xiaohuan Pei, Shan You, Fei Wang, Chen Qian, and Chang Xu. Localmamba: Visual state space model with windowed selective scan. arXiv preprint arXiv:2403.09338 2024.
[26] Amirul Islam, Sen Jia, and Neil D. B. Bruce How much position information do convolutional neural networks encode. arXiv preprint arXiv:2001.0822 0882488000888..... 2020.
[27] Jiayu Jiao, Yu-Ming Tang, Kun-Ye Lin, Yipeng Gao. Jinhua Ma, Yaowei Wang, and Wei-Shi Zheng. Dilateformer: Multi-scale dilated transformer for visual recognition. TMM, 2023.
[28] Manjin Kim, Paul Hongsuck Seo, Cordelia Schmid, and Minsu Cho. Learning correlation structures for vision transformers. In CVPR, pages 18941-18951, 2024.
[29] Alex Krizhevsky, 1lya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In NeurlPS, pages 1097-1105, 2012.
[30] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(1 (6(11))2278-2324 1998.
[31] Yawei Li, Kai Zhang, Jiezhang Cao, Radu Timofte, and Luc Van Gool. Localvit: Bringing locality vision transformers. arXiv preprint 10t.:200400507,, 2021.
[32] James Liang, Yiming Cui, Qifan Wang. Tong Geng, Wenguan Wang, and Dongfang Liu. Clusterformer: Clustering as universal visual learner. NewrIPS, 2023.
[33] Tsung- Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, pages 740-755. Springer. 2014.
11

---

[34] Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Tommi Kärkkainen. Mykola Pechenizkiy, Decebal C Mocanu, and Zhangyang Wang. More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity. In ICLR, 2023.
[35] Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu. Lingxi Xie, Yaowei Wang. Qixiang Ye, and Yunfan Liu. Vmamba: Visual state space model. NewrIPS, 2024.
[36] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021.
[37] Zhuang Liu, Hanzi Mao. Chao- Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In CVPR, pages 11976-11986, 2022.
[38] Ilya Loshchilov and Frank Hutter. axXiv//11.05101,, 2017.
Decoupled weight decay regularization. arXiv preprint
[39] Xuran Pan, Tianzhu Ye, Zhuofan Xia, Shiji Song, and Gao Huang. Slide-transffrmer: Hierarchical vision transformer with local self-altention. CVPR, pages 2082-2091, 2023.
[40] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al, Pytorch: An imperative style, high-performance deep learning library. NeurlPS, 32, 2019.
[41] Xiaohuan Pei, Tao Huang. and Chang Xu. Efficientymamba: Atrous selective scan for light weight visual mamba. In AAAI, volume 39, pages 6443-6451, 2025.
[42] Boris T Polyak and Anatoli Juditsky. Accelcration of stochastic approximation by averaging. SIAM Journal on Control and Optimization, 30(4):838-8)) 03558888885,5111 1992.
[43] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. IJCV, 115:211 1-252, 2015.
[44] Dai Shi. Transnext: Robust foveal visual perception for vision transformers. In CVPR, pages 17773-17783, 2024.
[45] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recogni- tion. arXiv preprint 14X9 2014.
[46] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In CVPR, pages 1-9, 2015.
[47] Christian Szegedy, Vincent Vanhoucke, Sergey loffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, pages 2818-2826, 2016.
[48] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In ICMI., pages 3105-6114, 14. PMLR, 2019.
[49] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers distillation through attention. In ICML, pages 10347-100 10357. PMI.R, 2021.
[50] Hugo Touvron, Matthicu Cord, Alexandre Sablayrolles, Gabriel Synnacve, and Hervé Jégou. Going deeper with image transformers. In ICCV, pages 32 32-42, 2021.
[51] Ashish Vaswani, Noam Shazeer. Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurlPS, 30, 2017.
[52] Xuan-Thuy Vo. Duy-Linh Nguyen, Adri Priadana, and Kang-Hyun Jo. Efficient vision transformers with partial attention. In ECCV, pages 398-317. Springer, 2024.
[53] Wenhai Wang. Jifeng Dai, Zhe Chen, Zhenhang Huang, Zhiqi Li, Xizhou Zhu, Xiaowei Hu, Tong Lu, Lewei Lu, Hongsheng Li. et al. Internimage: Exploring large-scale vision foundation models with deformable convolutions. In CVPR, pages 4440-11419, 2023.
[54] Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song. Ding Liang, Tong Lu, Ping Luo, and Ling Shao. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In ICCV, pages 578.578,00 2021.
12

---

[55] Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song. Ding Liang, Tong Lu, Ping Luo, and Ling Shao. Pvt v2: Improved baselines with pyramid vision transformer. CVM, 433)415-424, 2022.
[56] Ross Wightman et al. Pytorch image models, 2019.
[57] Sanghyun Woo, Shoubhik Debuath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, and Saining Xie. Convnext v2: Co-designing and scaling convnets with masked autoencoders. In CVPR, pages 16133-16142, 2023.
[58] Zhuofan Xia, Xuran Pan, Shiji Song. Li Erran Li, and Gao Huang. Vision transformer with deformable attention. In CVPR, pages 4794-4801 2022.
[59] Haoke Xiao, Ly Tang, Peng-tao Jiang, Hao Zhang, Jinwei Chen, and Bo Li. Boosting vision state space model with fractal scanning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 8646 8654, 2025.
[60] Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun. Unified perceptual parsing for scene understanding. In ECCV, pages 418-434, 2018
[61] Chenhongyi Yang. Zehui Chen, Miguel Espinosa, Linus Briesson, Zhenyu Wang, Jiaming Liu, and Elliot Crowley. Plainmamba: Improving non-hierarchical mamba in visual recognition. arXiv preprint arXiv:2403.76695, 2024.
[62] Weihao and Xinchao Wang. Mambaout: Do we really need mamba for vision? arXiv preprint axXv:2405.07999. 2024.
[63] Weihao Yu, Pan Zhou, Shuicheng Yan, Xinchao Wang. Inceptionnext: When inception meets convnext. In CVPR, pages 5672-5683, 2024.
[64] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon You. Cutmix: Regularization strategy to train strong classifiers with localizable features. In ICCV, pages 6023-6032, 2019.
[65] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint 17X0:0/710.0912. 2017.
[66] Li Zhang, Xiangtai Li, Anurag Amab, Kuiyuan Yang, Yunhai Tong, and Philip HS Torr. Dual graph convolutional network for semantic segmentation. arXiv preprint 10Xiv:190..06/11, 2019.
[67] Qiming Zhang, Jing Zhang, Yufei Xu, and Dacheng Tao. Vision transformer with quadrangle attention. TPAMI, 2024.
[68] Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random crasing data augmentation. In AAAI, pages 13001-13008, 2020.
[69] Lci Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, and Rynson WH Lau. Biformer: Vision transformer with bi-level routing attention. In CVPR, pages 10323-10333, 2023.
[70] Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. In ICML, 2024.
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A Technical Appendices and Supplementary Material
A.1 Model Efficiency Comparison
As shown in Figure 4 it can be seen that under the same inference throughput or accuracy, the accuracy or inference throughput of the proposed DAMamba significantly outperforms the SSMs, ViTs and CNNs, indicating that the proposed DAMamba achieves state-of-the-ar performance and efficiency.
85.0
84.5
T0p-1 Accuracy (%)
84.0
83.5
83.0
82.5
ConvNext
Swin
82.0
BiFormer
TransNext
VMamba
81.5
DAMamba (ours)
200
400
600
800
1000
Inference Throughput (image/s)
Figure 4: The trade-off between ImageNet-IK top-1 accuracy and inference throughput. All the models trained under the DeiT training hyperparameters. The inference throughput is measured on an NVIDIA RTX 3090 GPU with batch size 128.
A.2 Visualization
As shown in Figure we visualize the scanning results of DAMamba on ImageNet-I 1K images to verify the effectiveness of the proposed dynamic adaptive scanning method. For ease of visualization, we selected the final stage with fewer patches and removed prediction points outside the region of interest. For 2D positions of floating-point type, visualize them by mapping each position the nearest patch. We present three examples from the ImageNet- K dataset. We observe that the proposed dynamic adaptive scanning primarily focuses on target objects, adapting the input image by dynamically focusing on the foreground regions of interest. Additionally, the scanning areas can be adaptively adjusted.
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WANZ
(a) Input image.
(b) Scan
Figure 5: Visualization of the Dynamic Adaptive Scan, where the blue pentagram represents the start of the scan and the blue circle represents the end of the sean.
A.3 Object Detection and Instance Segmentatio with 3x schedule
As shown in Table 66 when using 3x training configuration. DAMamba still maintains large performance improvements. These results indicate that DAMamba has the potential to achieve state-of-the-art performance in dense prediction downstream tasks.
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Table 6: Comparison of object detection and instance segmentation performance on COCO with Mask R-CNN detector. FLOPs are calculated with input resolution of 1280 x 800.
Mask R-CNN 3x
MS schedule
Backbone
APh
APh
50
APt
AP"
AP
AP
#Param.
FLOPs
Swin-T
68.1
50.3
41.6
65.1
44.9
48M
267G
PVTv2-B2
47.8
69.7
52.6
43.1
66.8
46.7
45M
309G
ConvNeXt-T
46.2
67.9
50.8
41.7
65.0
44.9
48M
262G
NAT-T
47.7
69.0
52.6
42.6
66.1
45.9
48M
258G
-oormer,-T
47.5
69.6
52.1
42.7
66.4
46.1
49M
VMamba-T
70.4
53.5
43.7
67.4
47.0
50M
271G
LoamaaMa
70.1
53.0
45M
291G
DAMamba-T
50.4
71.4
55.5
44.8
68.6
48.6
45M
284G
Swin-S
48.2
69.8
52.8
43.2
67.0
46.1
69M
354G
PVTv2-B3
48.4
69.8
$3.3
43.2
66.9
46.7
65M
3976
ConvNeXt-S
47.9
70.0
52.7
42.9
66.9
46.2
70M
348G
NAT-S
48.4
69.8
53.2
43.2
66.9
46.5
70M
330G
OFormer SS
49.5
71.2
54.2
44.2
68.3
47.6
70M
G
VMamba-S
70.9
54.7
70M
3490
LocalVMamba-S
70.5
54.4
67.8
47.4
69M
414G
DAMamba-S
51.2
72.1
56.1
45.1
69.2
49.1
65M
395G
ConvNeXt-B
48.5
70.1
53.3
43.5
67.1
46.7
108M
486G
Swin-B
48.6
70.0
53.4
43.3
67.1
46.7
107M
496G
PVTv2-B5
48.4
69.2
52.9
42.9
66.6
46.2
102M
557G
DAMamba-B
51.4
72.3
56.4
45.3
69.5
48.9
105M
520G
23


Reviews:
[{"review_id": "QDfKLoeR3p", "reviewer": "Reviewer_e5U2", "summary": "This paper proposes a new scanning path for recurrent Mamba-based vision processing. The authors claim that manually designed scans that flatten image patches into sequences disrupt the original semantic spatial adjacency of the image and lack flexibility. To address this, they propose a learnable method termed \"dynamic adaptive scan\" that adaptively allocates scanning orders and regions. Using this in Vision Mamba (based on a VMamba-like architecture), the authors show improved performance on classification, object detection, and segmentation tasks.", "questions": "1) On Line 181, it is mentioned that sample feature vectors of interest are arranged in the order of top to bottom and left to right. This is unclear in the sense that you are doing 4-way scanning, or it's just one scan path with flattening in the mentioned order? \n\n2) The effect of DAScan, Convpos, and ConvFFN is only discussed on the smallest version of the model. It would be helpful to see the effect of all three on at least DAMamba-S or DAMamba-B. \n\n3) Other questions are discussed in the weakness section above.", "limitations": "The limitations description is missing. Authors could talk about the effect of the number of reference points required for the dynamic adaptive scan and what will happen if very few points are used.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1) The proposed offset prediction network, designed to predict offsets for data-driven dynamic adaptive scan, is lightweight and can be easily plugged into any vision framework. It uses a simple depthwise convolution and a linear layer to predict the offsets. \n\n2) The experiment section is exhaustive, covering 4 model sizes across 3 standard datasets and corresponding 4 tasks. \n\nWeaknesses:\n\nWhile the paper mentions that their major contribution is a dynamic scan path, there is an issue when directly comparing with VMamba or LocalMamba. The DAMamba method diverges from the VMamba in two directions: 1) Number of Channels and Blocks (Table 1), and 2) Use of additional ConvFFM and Convpos. Because of this, it is not clear how much improvement actually comes from the proposed dynamic scan version. \n\nFor point 1, since the architecture of DAMamba reduces the channels in the last layer and instead increases the number of blocks, it is not clear whether performance improvement is coming due to that over VMamba and LocalMamba. I strongly suggest authors try to run a DAMamba-S with the same channels ([96,192,384,768]) and blocks ([2,2,27,2]) configuration as VMamba-S, and then show the comparison with VMamba-S and LocalMamba-S. If not possible due to a compute issue, at least DAMamba-T with VMamba-T channel and blocks configuration needs to be conducted. \n\nFor point 2, in the current DAMamba-S configurations, I suggest the authors try the following two experiments: 1) Remove the DAScan and just use a simple 4-way VMamba scan while keeping all other components and configurations of the proposed DAMamba-S. 2) Remove DAScan and replace it with LocalVMamba-based 4-way local scanpath while keeping other components consistent. This experiment will clearly show if the dynamic scan is signifcantly effective over vanilla or local scanpath. \n\nIf possible time considering, also transfer these models for segmentation tasks and demonstrate the performance of above requested models on that task too.", "was_revised": true, "final_justification": "I thank the authors for the rebuttal. Most of my concerns are addressed. I will raise the score.", "rebuttal": "We sincerely appreciate your valuable and encouraging comments on our technical contributions and performance.\n\n**Q1:** On Line 181, it is mentioned that sample feature vectors of interest are arranged in the order of top to bottom and left to right. This is unclear in the sense that you are doing 4-way scanning, or it's just one scan path with flattening in the mentioned order?\n\n**R1:** Thank you to the reviewer for pointing out the ambiguity in terminology. In line 181 of the paper, what we meant is: the sampled patches in DAS are ordered from top to bottom and left to right based on their original positions. This ordering is solely used to construct a one-dimensional sequence for SSM processing, which is scanned only once—not a four-way scan in the traditional sense.\n\nUnlike the 4-way scan used in VMamba, our method employs a single scan path for flattening. However, the patch order is dynamically predicted and non-fixed, and then sorted according to their original coordinates (top to bottom, left to right). This preserves local adjacency and enhances semantic continuity. We will further clarify the definition of the scan path in the final version and emphasize that our method does not use multi-directional scanning.\n\n**Q2:** The effect of DAScan, Convpos, and ConvFFN is only discussed on the smallest version of the model. It would be helpful to see the effect of all three on at least DAMamba-S or DAMamba-B.\n\n**R2:** We agree with the reviewer’s point. Due to limited computational resources and time constraints during the rebuttal period, we will conduct additional ablation studies on DAMamba-S and DAMamba-B in the final version.\n\n**Q3:** Other questions are discussed in the weakness section above.\n\n**R3:** Due to the limited time and computational resources during the rebuttal period, we conducted experiments on DAMamba-T using the same channel and block configurations as VMamba-T. The results are shown below, demonstrating that our model still significantly outperforms both VMamba and LocalMamba:\n\nMethod | #Params(M)|FLOPs(G)|Accuracy (%)|\n------  | ------ |  ------ |------ |\nDAMamba-T\t|  26|4.8 |83.8 |\nDAMamba-T(VMamba config)\t|  30|5.3 |83.5 |\nVMamba-T | 30 |4.9 |82.6 |\nLocalMamba-T |26|5.7 |82.7 |\n\n\nDue to the same constraints of time and computational resources during the rebuttal, we also performed a comparative study on different scan methods using DAMamba-P. To ensure fairness, we disabled both convpos and convfffn, used DAMamba-P as the baseline, and replaced its scan mechanism with the 4-way VMamba scan and 4-way Local scan. The results, shown below, clearly demonstrate the superiority of our DAS method over the other two scanning strategies.\n\nMethod  |Accuracy (%)|\n------  | ------ | \nDynamic Adaptive Scan(Ours)\t|  78.3|\nSweeping Scan (VMamba)|  77.7|\nLocal Scan (LocalMamba) | 77.9 |"}, {"review_id": "004blDSop4", "reviewer": "Reviewer_xcKP", "summary": "This paper investigates the adaptation of State Space Models (SSMs) for use as backbone architectures in computer vision tasks. The authors identify limitations with previous SSM approaches, especially the inflexibility and inefficiency of manually-designed image patch scanning schemes. To address these issues, they propose Dynamic Adaptive Scan (DAS), a data-driven method that learns to allocate scanning order and regions adaptively based on each input image. The Dynamic Adaptive Scan is integrated into a new vision SSM backbone, DAMamba, which demonstrates strong empirical results across classification, detection, instance segmentation, and semantic segmentation on standard benchmarks, consistently outperforming prior visual SSMs (e.g., VMamba) and in several cases surpassing popular CNN and Vision Transformer architectures.", "questions": "please see weaknesses.", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1. DAMamba is thoroughly evaluated on multiple standard benchmarks, including ImageNet-1K for classification, COCO2017 for object detection/segmentation, and ADE20K for semantic segmentation. In each case, DAMamba variants outperform comparable SSM baselines (e.g., VMamba, Vim) and are competitive with, or superior to, many top CNN and ViT architectures at similar parameter/FLOP budgets.\n\n2. By providing different model scales and integrating with widely-used frameworks, the work has practical value and reproducibility.\n\nWeaknesses\n\n1. Limited Theoretical Discussion: The paper’s justification for DAMamba’s improved spatial modeling remains primarily empirical. While the practical benefits of DAS are evident, the work does not rigorously dissect or quantify how much spatial adjacency (or other higher-order image structure) is preserved versus prior scan methods, beyond anecdotal visualizations and gains in accuracy—this limits mechanistic insight.\n\n2. I believe that DAMamba’s performance may stem from overfitting to a fixed resolution. I’m genuinely curious—what would happen if the authors extended DAMamba to different resolutions? If it fails to maintain strong performance across varying resolutions, then I would consider the overall architectural design to lack sufficient insight.\n\n3. While major design elements are described, there are some notational ambiguities—e.g., in the formalism in Section 3.2 around offset prediction and feature sampling, more explicit pseudocode or implementation details would aid reproducibility for non-experts.\n\n4. While Figure 5 in the supplement visually contrasts regions of attention, the main methods for inspecting what is actually learned by the scanning process are limited to qualitative renderings, lacking systematic quantitative evaluation (e.g., are certain classes of object consistently mis-scanned?)", "was_revised": true, "final_justification": "The author rebuttal has addressed my concerns.", "rebuttal": "We sincerely appreciate your valuable and encouraging comments on our technical contributions and performance.\n\n**Q1:** Limited Theoretical Discussion: The paper’s justification for DAMamba’s improved spatial modeling remains primarily empirical. While the practical benefits of DAS are evident, the work does not rigorously dissect or quantify how much spatial adjacency (or other higher-order image structure) is preserved versus prior scan methods, beyond anecdotal visualizations and gains in accuracy—this limits mechanistic insight.\n\n**R1:** We appreciate the reviewer’s suggestion for a deeper theoretical exploration. At present, the paper primarily demonstrates the advantages of Dynamic Adaptive Scan (DAS) through experimental accuracy and visualizations. To further enhance the understanding of its mechanism, we will incorporate the following improvements in the final version:\n\nIntroduction of quantitative evaluation metrics: We will introduce a metric based on spatial position reconstruction error to quantitatively assess DAS’s ability to preserve the original spatial structure, in comparison with other scanning strategies such as Sweep Scan or Local Scan.\n\nInter-class comparative analysis: We will analyze the overlap between DAS scanning regions and ground-truth object boundaries across different image categories to quantify how effectively DAS learns to focus on target regions.\n\n\n**Q2:** I believe that DAMamba’s performance may stem from overfitting to a fixed resolution. I’m genuinely curious—what would happen if the authors extended DAMamba to different resolutions? If it fails to maintain strong performance across varying resolutions, then I would consider the overall architectural design to lack sufficient insight.\n\n**R2:** We thank the reviewer for their attention to the generalization ability of our model. In our image classification experiments, we did use a fixed resolution of 224×224 for both training and evaluation, which is the standard practice adopted by most current methods.\n\nHowever, in downstream vision tasks such as object detection and instance segmentation (using the Mask R-CNN 1× setting), the image resolutions during both training and testing are dynamically varied. Specifically, the shorter side of each image is resized to 800 pixels, while the longer side is capped at 1333 pixels, with the original aspect ratio preserved.\n\nIn addition, we also adopted the more advanced Mask R-CNN 3× training strategy in our experiments. This setup introduces stronger data augmentation mechanisms, including multi-scale training. Concretely, during training, each image is randomly resized to one of 11 predefined scales, with the short side ranging from 480 to 800 pixels and the long side limited to a maximum of 1333 pixels. These dynamically varying input resolutions pose a higher challenge to the model's scale robustness.\n\nUnder such settings with dynamic input resolutions, our model still achieves performance significantly superior to mainstream baselines in both object detection and instance segmentation tasks. This strongly validates the robust generalization capability of our model when handling vision tasks across varying resolutions.\n\n**Q3:** While major design elements are described, there are some notational ambiguities—e.g., in the formalism in Section 3.2 around offset prediction and feature sampling, more explicit pseudocode or implementation details would aid reproducibility for non-experts.\n\n**R3:** We thank the reviewer for their concern regarding reproducibility. Although we have included implementation code in the supplementary materials, we acknowledge that this may not be sufficient. In the final version, we will provide the following additions:\n\nPseudocode for the DAS module: We will include PyTorch-style pseudocode that outlines the forward pass of the DAS module, covering key components such as offset computation, coordinate transformation, and bilinear interpolation sampling.\n\nAdditional implementation details: We will provide more comprehensive information on aspects such as the initialization of the offset learning range, the gradient flow path, and the interface between DAS and the SSM module.\n\n\n**Q4:** While Figure 5 in the supplement visually contrasts regions of attention, the main methods for inspecting what is actually learned by the scanning process are limited to qualitative renderings, lacking systematic quantitative evaluation (e.g., are certain classes of object consistently mis-scanned?)\n\n\n**R4:** We thank the reviewer for pointing out the limitations in our interpretability analysis. In the current version, we primarily rely on visualizations of DAS scanning paths on ImageNet samples as intuitive evidence. To enhance the systematic nature of our analysis, we plan to include the following additions in the final version:\n\nCategory-level statistical analysis: We will compute the overlap between DAS scanning regions and ground-truth objects across different ImageNet classes, analyzing how the model attends to objects of varying shapes and sizes.\n\nFailure case analysis: We will examine samples with degraded performance (e.g., misclassified images) to investigate whether the DAS scanning regions deviate from the key object areas."}, {"review_id": "xzNazqAfC2", "reviewer": "Reviewer_vgjh", "summary": "This paper introduces a dynamic scanning order for the vision mamba architecture, which needs to process an image in a sequence and cannot process it in parallel. They show that prior work was limited in their human defined scanning orders, and introduce a scanning order that can be learned and is therefore data-dependent. They show state-of-the-art performance on image classification.", "questions": "- It is unclear how the initial points are chosen, are they learned?\n- It is unclear why multiple points are derived from a single sample point and why they thus need to be ordered from left to right and top to bottom?", "limitations": "- The method is not pure Mamba, but rather a hybrid of a CNN and Mamba.", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- Scanning in a data-dependent order makes sense, this should be more effective than any human defined order.\n- The classification results are strong and indeed seem to be state-of-the-art for the models tested. \n- Visualisations of the adaptive scan clearly show that the scan focuses on the object of interest, confirming the efficacy of the method.\n\nWeaknesses:\n- The equations are improperly introduced/explained for anyone not familiar with how SSM's work.\n- The method section is insufficient, it is unclear how certain things work (see Questions).\n- The detection and instance/semantic segmentation results do not include the best competitors from the classification results (like TransNext). Looking at the TransNext paper for example, they seem to get higher performance for these tasks. Unless there is a good reason to leave them out, it should be clearly noted that on dense downstream tasks the proposed method is not state-of-the-art.\n- Although the papers mostly centers around the dynamic scan, the convolutional inductive biases seem to be more impactful in the ablation study (+0.8) than the dynamic scan (+0.6).", "was_revised": true, "final_justification": "My concerns have mostly been addressed in the discussion period, see below.", "rebuttal": "We sincerely appreciate your valuable and encouraging comments on our technical contributions and performance.\n\n**Q1:** It is unclear how the initial points are chosen, are they learned?\n\n**R1:** The reference points are not learned; they are predefined. Specifically, each patch in the image serves as an initial point, with its center position taken as the initial coordinate.\n\n\n**Q2:** It is unclear why multiple points are derived from a single sample point and why they thus need to be ordered from left to right and top to bottom?\n\n**R2:** Each reference point has only one offset point because scanning from top to bottom and left to right better aligns with human intuitive vision. Sorting the points according to their original coordinates—from top to bottom and left to right—helps preserve local adjacency and enhances semantic continuity. Moreover, the top-to-bottom, left-to-right scanning order is also a commonly used, simple, and effective approach in Vision Mamba models.\n\n**Q3:** The equations are improperly introduced/explained for anyone not familiar with how SSM's work.\n\n**R3:** We appreciate this feedback. Currently, the method section indeed assumes that readers have some background knowledge of State Space Models (SSM). To improve readability, we will, in the final version:\n\n1.Provide a more detailed introduction and explanation of the SSM working principles;\n2. Include pseudocode or clearer module descriptions covering offset generation, offset point feature computation, and sequence rearrangement.\n\n**Q4:** The method section is insufficient, it is unclear how certain things work (see Questions).\n\n**R4:** Thank you for your suggestion. In the final version, we will further improve the methodology section, especially by clarifying the data flow for initial point selection, offset calculation, offset point sampling, sorting, and aggregation processes. We will also introduce pseudocode and module diagrams for key components to reduce abstraction and enhance clarity.\n\n\n**Q5:** The detection and instance/semantic segmentation results do not include the best competitors from the classification results (like TransNext). Looking at the TransNext paper for example, they seem to get higher performance for these tasks. Unless there is a good reason to leave them out, .it should be clearly noted that on dense downstream tasks the proposed method is not state-of-the-art.\n \n\n**R5:** Thank you for your suggestion. Although the TransNext paper appears to achieve higher performance on these tasks, our method attains better accuracy in image classification. Additionally, for models of comparable size such as DAMamba-T, our inference speed is nearly twice that of TransNext-T. We will clearly state in the final version that our proposed method is not state-of-the-art on dense downstream tasks.\n\n**Q6:** Although the papers mostly centers around the dynamic scan, the convolutional inductive biases seem to be more impactful in the ablation study (+0.8) than the dynamic scan (+0.6).\n\n**R6:** Yes, for SSM models that excel at global modeling, convolutional local modeling is very important—especially for vision tasks where capturing local details is crucial."}, {"review_id": "yVhgOCitmW", "reviewer": "Reviewer_RoM8", "summary": "Existing visual state-space models typically scan image content using predefined paths, often breaking spatial relationships.\nThis work therefore produces a dynamic adaptive scanning method for adaptively learning scanning paths and regions.\nThe authors build a visual backbone network called DAMamba based on this, showing good performance.", "questions": "See weaknesses.", "limitations": "The authors did not discuss the limitations of the work and the social implications.\n\nI feel that the current setup of the ablation experiments has obvious shortcomings, as I mentioned in WEAKNESSES.\nThe scanning mechanism is the core of the paper.\nBut the authors focus only on the final performance of the model, but do not provide a sufficiently explicit baseline.\nOn the one hand the current baseline exhibits leading performance, on the other hand the authors neglect a full discussion with existing scanning strategies under the same baseline.\nThese are very important for the paper.\nIt is hoped that the authors can further complement and improve this work.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## Strengths\n\n- The authors propose a data-driven information scanning mechanism that adaptively learns scanning paths and regions of an image within the SSM framework.\n- Based on the proposed dynamic scanning strategy, the authors construct a dynamic visual SSM backbone network that exhibits better performance compared to other existing visual backbones.\n\n## Weaknesses\n\n1\\. Dynamic adaptation schemes for information aggregation paths are common in the development of deep neural networks nowadays. Examples include Deformable Convolution [1-4], Kernel [5], Transformer [6,7]. The authors have neglected the relevance of dynamic design in these important works to this paper, and lacked the necessary analysis and comparisons.\n\n2\\. In the related work and background introduction, the authors have also neglected important elements such as Dynamic Neural Networks [8] which are closely related to this paper.\n\n3\\. As a visual model based on sequence modeling strategy, the development of other existing sequence modeling strategies such as [9-11] is also neglected in the discussion and analysis.\n\n4\\. In ablation experiments, the performance of ConvPos and ConvFFN can only be treated as a re-validation of the validity of the existing components, and cannot be used as a performance contribution point in the model design. So it seems somewhat unreasonable to treat them as additional independent components for ablation. It might be more appropriate to merge the whole into the baseline as a more standard baseline.\n\n5\\. For the BASELINE model in the ablation experiment:\n\n5.1 The performance of ConvPos and ConvFFN can only be taken as a re-validation of the validity of the existing components, and cannot be used as a performance contribution point in the model design. So it seems unreasonable to ablate them as additional independent components. It might be more appropriate to merge the whole into the baseline as a more standardized baseline.\n\n5.2 Why does the current baseline model perform so well? From the experimental results, it has surpassed the well-designed Vim-Ti, LocalVim-Ti and EffcientVMamba-T.\n\n6\\. The ablation experiment also lacks a comparison and discussion of the scanning strategies that have been proposed in the current Vision Mamba-related studies (e.g., those introduced in the reviews [12,13,14]).\n\n\nREF:\n\n1. Deformable Convolutional Networks, 2017    \n2. Deformable ConvNets v2: More Deformable, Better Results, 2019    \n3. InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions, 2023    \n4. Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications, 2024    \n5. Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation, 2020    \n6. Vision Transformer with Quadrangle Attention, 2023    \n7. DAT++: Spatially Dynamic Vision Transformer with Deformable Attention, 2023    \n8. Dynamic Neural Networks: A Survey, 2021    \n9. Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence, 2024    \n10. Retentive Network: A Successor to Transformer for Large Language Models, 2023    \n11. xLSTM: Extended Long Short-Term Memory, 2024    \n12. Visual Mamba: A Survey and New Outlooks, 2024    \n13. A Survey on Visual Mamba, 2024\n14. Vision mamba: A comprehensive survey and taxonomy, 2024", "was_revised": true, "final_justification": "The authors have addressed my concerns in their responses.\nCombined with the better model performance reported for their method, these improvements have led me to raise my overall score.\n\nNevertheless, the manuscript still suffers from an incomplete survey of related work, which is a weakness that regrettably pervades much of the current neural network design.\nSpecifically, the manuscript still neglects a substantial body of related work on\n- deformable operations,\n- dynamic network architectures,\n- recent sequence-modeling methods in vision, and\n- alternative scanning strategies within the Mamba family.\n\nA more complete discussion would help reviewers and readers better appreciate the proposed method’s advantages and situate it against existing approaches.", "rebuttal": "We sincerely appreciate your valuable and encouraging comments on our technical contributions and performance.\n\n**Q1:** Dynamic adaptation schemes for information aggregation paths are common in the development of deep neural networks nowadays. Examples include Deformable Convolution [1-4], Kernel [5], Transformer [6,7]. The authors have neglected the relevance of dynamic design in these important works to this paper, and lacked the necessary analysis and comparisons.\n\n**R1:** We thank the reviewer for raising this point. Our paper primarily focuses on the differences and design of scan strategies within Vision SSM architectures, and therefore does not provide a systematic analysis of the connections with Deformable Convolution, Kernel methods, and Transformers.\n\nIn fact, Dynamic Adaptive Scan (DAS) shares a similar core idea with these methods—that is, dynamically determining the feature extraction regions based on the input content—although the modeling mechanisms differ:\n\nDeformable Convolution, Kernel methods, and Transformers mainly learn dynamically aggregated receptive regions; in contrast, DAS reorganizes the sequence input at the scan level, learning not only the dynamic aggregation of regions but also their global ordering. This endows Vision SSMs with an enhanced ability to adapt to the 2D image structure.\n\nWe will supplement the final version with relevant citations (e.g., [1–7]) and provide additional necessary analysis and comparisons.\n\n\n**Q2:** In the related work and background introduction, the authors have also neglected important elements such as Dynamic Neural Networks [8] which are closely related to this paper.\n\n**R2:** Thank you for the reminder. Works on Dynamic Neural Networks (DNN) [8] indeed share conceptual connections with our proposed DAS method, especially regarding structural dynamicity. We will include this survey paper and some of its referenced works in the related work section.\n\n\n**Q3:** As a visual model based on sequence modeling strategy, the development of other existing sequence modeling strategies such as [9-11] is also neglected in the discussion and analysis.\n\n**R3:** We acknowledge that the current related work section primarily focuses on Vision Transformer and Mamba’s vision adaptation approaches, and does not systematically cover emerging sequence modeling architectures such as RWKV [9], RetNet [10], and xLSTM [11]. These methods have shown strong performance in language tasks and are gradually being adapted for vision applications, making them worthy of attention. We will expand the background introduction on sequence modeling to include these recent advances in the final version.\n\n\n\n**Q4:** In ablation experiments, the performance of ConvPos and ConvFFN can only be treated as a re-validation of the validity of the existing components, and cannot be used as a performance contribution point in the model design. So it seems somewhat unreasonable to treat them as additional independent components for ablation. It might be more appropriate to merge the whole into the baseline as a more standard baseline.\n\n**R4:** We agree with the reviewer’s opinion that ConvPos and ConvFFN are standard modules in popular vision models. Indeed, we should incorporate them into the “standard baseline configuration” for ablation studies. We will redo the ablation experiments accordingly in the final version as per your suggestion.\n\n\n**Q5:** Why does the current baseline model perform so well? From the experimental results, it has surpassed the well-designed Vim-Ti, LocalVim-Ti and EffcientVMamba-T.\n\n**R5:** This is because our DAMamba adopts a pyramidal hierarchical structure, whereas LocalVim and Vim use an isotropic structure (LocalVMamba and VMamba adopt a pyramidal hierarchical structure). In vision backbones, pyramidal structures typically outperform isotropic structures. For example, the pyramidal VMamba outperforms the isotropic Vim. Additionally, although EfficientVMamba-T also uses a pyramidal structure, under the same parameter size (6M), the baseline used in our DAMamba-P ablation study consumes more FLOPs (1.2G vs. 0.8G), which leads to better performance.\n\n**Q6:** The ablation experiment also lacks a comparison and discussion of the scanning strategies that have been proposed in the current Vision Mamba-related studies (e.g., those introduced in the reviews [12,13,14]).\n\n**R6:** To ensure a fair comparison, we did not use ConvPos and ConvFFN, taking DAMamba-P as the baseline and replacing its scan method with other scanning strategies for comparison. As shown below, our method clearly outperforms these alternatives. Due to limited time during the rebuttal, we only compared a few scanning strategies, but we will include more comparisons and discussions of scanning methods from surveys [12], [13], and [14] in the final version.\n\nMethod  |Accuracy (%)|\n------  | ------ | \nDynamic Adaptive Scan(Ours)\t|  78.3|\nSweeping Scan (VMamba)|  77.7|\nLocal Scan (LocalMamba) | 77.9|\nContinuous 2D Scan (Plainmamba) | 77.6 |\nEfficient 2D Scan(Efficientvmamba) | 77.3 |\n\n\nREF:\n\n1.Deformable Convolutional Networks, 2017\n\n2.Deformable ConvNets v2: More Deformable, Better Results, 2019\n\n3.InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions, 2023\n\n4.Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications, 2024\n\n5.Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation, 2020\n\n6.Vision Transformer with Quadrangle Attention, 2023\n\n7.DAT++: Spatially Dynamic Vision Transformer with Deformable Attention, 2023\n\n8.Dynamic Neural Networks: A Survey, 2021\n\n9.Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence, 2024\n\n10.Retentive Network: A Successor to Transformer for Large Language Models, 2023\n\n11.xLSTM: Extended Long Short-Term Memory, 2024\n\n12.Visual Mamba: A Survey and New Outlooks, 2024\n\n13.A Survey on Visual Mamba, 2024\n\n14.Vision mamba: A comprehensive survey and taxonomy, 2024"}]

Author rebuttal:

Meta-review: The paper proposes a dynamic adaptive scan for Mamba architectures. The approach is evaluated for several computer vision tasks like image classification, object detection, and instance segmentation. The reviewers appreciate the thorough evaluation and the practical value of the proposed approach. The reviewers, however, had also some concerns regarding the comparison of the proposed dynamic scan with other scanning approaches and a potential overfitting of the approach to a fixed resolution. The reviewers also asked to provide more technical details and improve the discussion of related work. The rebuttal resolved the questions regarding the experimental evaluation and overfitting issues. The rebuttal also promised to extend the discussion of related work and provide more technical details. After the rebuttal, the reviewers unanimously recommend the acceptance of the paper.

The AC agrees with the recommendation of the reviewers. While the novelty is limited and the paper needs to discuss the difference to recent related works that use dynamic adaptive scans for Mamba and other network architectures more in detail as raised by Reviewer RoM8, the results in the paper and the additional ablation studies in the rebuttal demonstrate the advantage of the proposed approach compared to other scanning techniques. The ablation studies regarding different scanning techniques need to be included in the paper.
Decision: Accept (poster)