iiANET: Inception Inspired Attention Hybrid Network for efficient Long-Range Dependency
Authors: Yunusa Haruna
Adamu Lawan
Abdulganiyu Abdu Yusuf
Venue: Accepted by TMLR ()

Abstract: The recent emergence of hybrid models has introduced a transformative approach to computer vision, gradually moving beyond conventional convolutional neural networks and vision transformers. However, efficiently combining these two approaches to better capture long-range dependencies in complex images remains a challenge. In this paper, we present iiANET (Inception Inspired Attention Network), an efficient hybrid visual backbone designed to improve the modeling of long-range dependencies in complex visual recognition tasks. The core innovation of iiANET is the iiABlock, a unified building block that integrates a modified global r-MHSA (Multi-Head Self-Attention) and convolutional layers in parallel. This design enables iiABlock to simultaneously capture global context and local details, making it effective for extracting rich and diverse features. By efficiently fusing these complementary representations, iiABlock allows iiANET to achieve strong feature interaction while maintaining computational efficiency. Extensive qualitative and quantitative evaluations on some SOTA benchmarks demonstrate improved performance.

Published in Transactions on Machine Learning Research (12/2025)
iANET:: Inception Inspired Attention Hybrid Network for efficient Long-Range Dependency
Yunusa Haruna NewraLab, Suzhou, China
guruso@k25buaa.eeu.co
Adamu Lawan
alawvnn&buace edu.ccm
Beihang University, Beijing, China NewraLab, Suzhou, China Beijing Goer Tek Alpha Labs
Abdulganiyu Abdu Yusuf Beijing Institute of Technology, Beijing, China
abdualy220@gmaillccom com
Reviewed on OpenReview: /ttps://openreeee net/ forum? HGSylgFod]
Abstract
The recent emergence of hybrid models has introduced a transformative approach to com- puter vision, gradually moving beyond conventional convolutional neural networks and vi- sion transformers. However, efficiently combining these two approaches to better capture long-range dependencies in complex images remains a challenge. this paper, we present iANET (Inception Inspired Attention Network), an efficient hybrid visual backbone de- signed to improve the modeling of long-range dependencies in complex visual recognition tasks. The core innovation of iiANET is the siABlock, unified building block that inte- grales a modified global r-MHSA (Multi-Head Sell-Atiention) and convolutional layers in parallel. This design enables iiABlock to simultaneously capture global context and local details, making it effective for extracting rich and diverse features. By efficiently fusing these complementary representations, iiABlock allows iANEET to achieve strong feature in- teraction while maintaining computational efficiency. Extensive qualitative and quantitative evaluations on some SOTA benchmarks demonstrate improved performance.
1 Introduction
From aulonomous drones to urban planning, understanding complex visual scenes is more critical than ever, yet traditional models struggle to capture such complexity. Over the last decade, deep Convolutional Neural Network (CNN) architectures have emerged as the de facto standard for solving most computer vision (CV) tasks, including image classification He et al. (2016); Tan & Le (2019). object detection Ren et al. (2055): Redmon & Farhadi (2017) and segmentation Long et al. (2015) with compelling results. The prevalence of CNN architectures
coincidental, as they excel at capturing spatial features and patterns in images. However, the dominance of CNN architectures is being challenged by the emergence of ViT (Vision in Transformer) Dosovitskiy et al. (2020) presenting a transformative approach to solving CV tasks. Interestingly, this groundbreaking model outperforms SOTA CNN-based models on ImageNet benchmark Dosovitskiy et al. (2020) and emerges as a competitive alternative Han et al. (2022). Practically, ViT works exactly like the text-based Natural Language Processing (NLP) transformers but with patch embedding. It divides the input image into patches, projects them into high-dimensional feature space through a lincar projection layer, adds positional embedding, passes them through a transformer encoder, and finally maps the output to fixed-length vector for classification tasks.

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Significantly, the key component of ViT is the self-attention mechanism Dosovitskiy et al. (2020) within the encoder, which enables the model to capture long-range dependencies by allowing each element. in the input sequence to attend to all other elements, considering their relative importance Dosovitskiy et al.] (2020). While this capability allows the model to selectively focus on distantly related pixels, facilitating the cffi- cient capture of contextual information across the entire input sequence, it encounters limitations such as increased computational complexity, reduced interpretability, data. hungry, and challenges handling spa- tial information effectively compared to CNNs. In contrast, CNN-based models, while effective at capturing local features through parameter sharing and local receptive fields, struggle with capturing long-range de- pendencies, limiting their ability to integrate distant pixel relationships. These limitations have led to the development of hybrid models, which combine their strengths to improve performance Haruna et al. (0025)
Specifically, previous hybrid designs have aimed to enhance capturing long-range dependencies for various CV tasks Guo et al. (2022); Srinivas et al. (202T]: Dai et al. (2021a). However, the design of hybrid models introduces additional design complexities Haruna et al. (2025). and computational costs compared to
monolithic models Khan et. al. (2023), while also potentially leading to information loss due to feature fusion
of distinct models Haruna et al. (2025] Lastly, more effort is needed to design efficient hybrid models capable of capturing long-range dependencies in complex images, a challenge that remains largely unaddressed.
In this work, we propose a novel architectural block, the iiA Block (Inoeption-Isspreed Attention Block), which serves as the core component of our model, HiANET. The iiABlock is a carefully designed hybrid module that integrates parallel convolutional layers for efficient local feature extraction, and global 2D Multi- Head Self- Attention (MHSA) mechanism with Registers to effectively model long-range dependencies. The outputs from these branches are then fused via concatenation and feature shuffling, enabling rich interaction between local and global features. By leveraging the complementary strengths of CNNs and transformers in a lightweight design, iiANET offers simple yet powerful solution for understanding complex visual scenes with long-range dependencies. For example, on the AID (Aerial Image Dataset) Xia et al. (017). HiANET-B and HANEET- achieve an accuracy of 80.57% and 83.11% respectivcly, outperforming ResNet-50 (71.93%), ViT-B/224 (69.93%), and DiNAT-B (79.12%). These results highlight. iiANET's eflectiveness in modeling long-range dependencies in challenging datasets. The contributions of this paper are summarized as follows:
From a methodological perspective, we identify that existing vision models struggle to efficiently capture long-range dependencies and global context in complex scenes, creating a gap in robust visual understanding.
We introduce iiAllock, a novel hybrid module that integrates parallel convolutional branches with global rMHSA, enabling efficient capture of long-range dependencies in complex vision tasks.
Extensive experimental results on commonly used benchmarks demonstrate that iiANET outper- forms some existing SOTA methods.
2 Related Work
CNN-based methods have seen various attempts to enhance their ability to capture LRD in images. Don- ahue et al. (2015) introduced the Long-term Recurrent Convolutional Network (LRCN) by fusing CNNs with LSTMs, while Yu et al. (2017) proposed the Dilated Residual Network (DRN) using multiple dilation rates to expand receptive fields, and Yu & Koltun (2015) designed a Dilated Convolution (DC) model to improve global context in semantic segmentation. ADRnet augments CNN with advection, diffusion, and reaction terms enable non-local feature transport and improve LRD spatio-temporal modeling Zakariaei et al. 20244). These approaches advanced CNN capacity for LRD but face limitations: LRCN increases com- putational complexity due to recurrent connections, DRN can lose fine- grained spatial details from varying dilation rates, and DC suffers from gridding artifacts. The emergence of ViT Dosovitskiy et al. (2020); Liu et al. (2021a) offered a breakthrough in capturing LRD via attention mechanisms, achieving SOTA perfor- mance. However, their quadratic complexity, high data requirements, and weaker inductive bias compared to CNN demand substantial computational resources. To address these trado-offs, hybrid methods combine
CNN feature extraction with ViT global dependency modeling Haruna et al. (2025). ZZhag et al. 2022) pro-
posed ELAN, using grse multi-scale self-attention for super-resolution Guo et. al. 2022) introduced
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Viaduct
Mountains
Storagetank
River
Input
RosNet-50
ViT-B B/1 /6
HANET
Figure 1: HANET Grad-CAM Selvaraju et al. (0177) comparison and other state-of-the-art models, e.g-- (a) shows aerial image of viaduct, mountain, storage tanks, and river featuring complex infrastructure consisting of multiple spans, roads, and surrounding landscapes. The primary objective is to accurately detect and classify various elements to facilitate efficient maintenance, safety management, and infrastructure planning. Consequently, capturing long-range dependencios in this sccnario is crucial for comprchending the spatial layout of different viaducts, mountains, storage lanks, and rivers, their interactions, and potential structural issues. (b) ResNet-50 highlights strong local features but fails capture range dependencies. (c) ViT-B/16 demonstrates limited interpretability, primarily focusing small, scattered regions. (d) The proposed iAAETT (hybrid model) exhibits enhanced ability to capture long-range dependencies, global context, and improved interpretability.
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CMT (CNN Meet ViT) to integrate attention into CNN blocks; and Srinivas et al. (021 developed BoTNet by replacing the final ResNet block with MHSA. While effective, these methods often apply attention at later stages with smaller spatial dimensions, limiting effectiveness, and face challenges such as memory constraints
(CMT-L), structural complexity, and information loss from fusing distinct mothods Peng et al. (2021): l Dai
et al. 202 Hassani & Shi (2022); Wu et al. (202T). CNN excel at local detail caplure but struggle with
LRD Haruma et al. (2025): Khan et al. (2023) RNNS handle such dependencies but lack parallelism and
train slowly [Banerjee et al. 2019), and ViT capture them efficiently but require more memory Dosovitskiy et al. (2020). However, it remains challenge to efficiently combine CNN and ViT architectures due to design complexity, higher computational costs, feature fusion losses, and interpretability issues. This paper addresses this gap by proposing a hybrid model that efficiently captures LRD in complex images.
3 Method
3.1
Our Approach: iiABlock
The iiABlock the core component of the proposed iAANET, designed to capture both local and global fea- tures in complex visual scenes. It combines parallel convolutional layers for efficient extraction localized features with global 2D r-MHSA mechanism augmented by Register tokens to model long-range depen- dencies. The convolutional branch leverages parameter sharing and local receptive fields, while 2D r-MHSA attends to distant pixel relationships across the input, with Register tokens to enhance interpretability. To merge these complementary features, we introduce lightweight fusion strategy using feature concatenation followed by channel shuffiing enabling rich local global interactions with minimal computational cost. This balanced design offers a favorable trade-off between speed and accuracy, providing a robust backbone for downstreamn visual recognition lasks. Figure
2
illustrate the iiABlock architecture.
BloBlokk
coavft
split
MHSA
1 x
6
EW
W. 1x
MINA
cony(1 1]
Query
enghtiers
softmax
conn(1 3)
C.x C.H. W
H
coovil x
R
rugleturs
micrchhe stoge
concatenate
Bottleneek
ECANET
Atrous block
Chane't 1)
Cocor (8 St
Cons Et 33
CAP
Cons
Signsit
HtWWxC
conx(5> 3), -
1x1NC 181x0 W
1//2
Juwnsemplo,
Figure 2: BiABLOCK design showing r-MHSA, inverted bottleneck, ECANET and Atrous block
3.2 Local Details
To extract fine-grained features and spatial patterns from complex images, iiABlock introduces components for modeling local details. This is important for recognizing textures, edges, and region-specific patterns.
Inverted Bottleneck (Efficient Convolutional Block) iiABlock utilizes the inverted bottleneck in parallel to improve computational efficiency and enhance local feature extraction, consisting of 1x1 convolutions for dimensionality reduction, 3x3 wisth-wise separable convolution for spatial information extraction, and 1x1 convolution for projection. Notably, this block is limited to capturing local context with fixed kernel,
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making it less effective in understanding prevalent global context in complex images Dai et al. (022a e.g., road, viaduct, bridge. Given the depth-wise operation in equation
ys =
Σ JEC(()
Wi-j 23
(1)
Vi calculates the output at position by taking a weighted sum of the input 2;, where Hi, E RR, The weights Wi-j determine the contribution of each input Ij to the outpul, and L(i) represents a local neigh- borhood, typically a 3 3 grid centered around i. The small size [(i) limits the receptive field's ability to capture intricate dotails, particularly in complex images with prevalent long-range dependencies. To miti- gate the limitation of the inverted bottleneck in capturing long-range dependencies, we also introduce atrous convolution into the iiABlock.
Atrous Convolution (Expanding Receptive Field) In iiABlock, a single 3 x 3 atrous convolution expands the receptive field without increasing parameters. Unlike standard convolution, it applies a dilation rate r to space kernel elements, covering a wider area while preserving resolution and computational efficiency (See 2 Given an input feature map x and filter w, the output at location is:
equation
K-1
v
Σ 0=
sli +r w[A]
(2)
This captures mid-range dependencies and enriches contextual understanding, bridging local and global rep- resentations. However, it remains insufficient for fully modeling global, long-range dependencies in complex scenes, which are further addressed by integrating a global r-MHSA module into iiABlook
3.3 Global Details
r-MHSA (Capluring global context and long-range dependencies). To capture global context and long-range dependencies complex images, iiABlock integrates a modified global 2D r-MHSA mechanism. Unlike CNN layers limited to local receptive fields, r-MHSA allows each spatial location to attend to all others, effectively modeling contextual relationships across the entire image. Given 2D input feature X E RCx reshaped into X E HW (where d is the feature dimension), linear projections generate queries Q XWQ, keys K = XWK, and values V = XWy. Attention Z with h heads is computed as shown in Equation enabling each token to atlend to all others and capture long-range dependencies.
QUKTT a
T, = softmax
(3)
Here, Q, interacts with all keys in K across the entire sequence, unlike the standard MHSA, which attends within a limited context window. This enables the model to consider the relationships between all tokens regardless of their positional distance, capturing the global context and long-range dependencies prevalent in complex images. The sof tmax operation normalizes these scores and produces attention weights, which, when applied the value matrix V, compute the final attended values. However, this interaction is order-agnostic and doesn't capture positional relationships in the input sequence. Therefore, in image data where spatial information is essential, integrating positional encodings is necessary to effectively complement MHSA.
Relative Position Encoding Srinivas et al. (2021) MHSA is permutation equivariant with no positional encoding. This characteristic limits its representations power, particularly for vision lasks involving highly structured data like images. Notably, is added to the input image representation before the MHSA is applied, and it used to guide the attention weights to focus on relevant pixels based on their relative positions in the input image.
QiKT + + V
Zn(Q,, K, V) softmax
(4)
Vo
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Published in Transactions on Machine Learning Research (12/2025)
Where r is trainable matrix. Lastly, reshape 2(X),, back to its original spatial shape of X R This addresses MHSA's 's order-agnostic nature, enhancing its representational power.
Registers (Improcing interpretability). While the self-attention mechanism significantly improves the net- work's ability to capture long-range dependencies, it struggles with poor interpretability Darcet et al. (2023 We instead add additional learnable tokens to mitigate prevalent artifacts in the attention mechanism caused by high norms in image areas with low information during inference or training, similar the implementa- tion by Darcet et al. (2023). In this case, it is a global MHSA where the attention mechanism has single input image, in contrast to having several patches. We initialize the register tokens for queries and keys as ROK E XNXxWWXH where N is the number of register tokens and HW is the spatial dimension, then head xHW where D is the dimension. The operations expand both RoK and Ry to BxNxxxX and RN benn xHW respectively (cquation 5 and ( effectively creating B copics of the register tokens for each batch, where is the batch size. This step ensures that each batch has its own set of register tokens, facilitating batch-wise parallel processing in the attention mechanism.
value register tokens as Ry R head
RoK repeat(H'gs nhw + bnhw', b b= B)
(5)
Ry = repeat (Ry, nhw + bnhw', b= bBB)
(6)
Then integrate the register tokens into the attention mechanism as Q:Kk Q + +RoK and VR= V Ry before computing the attention, where Zn is the final 2D-MHSA output with registers. After computation, the register tokens are discarded (See equation
Q.K7 + +QR VaL
1Q K, V) = softmax
VR
(7)
3.4 Features Recalibration
ECANET (Channel-wise Recalibration and Down-Sampling). While long-range dependencies in computer vision span both spatial and channel dimensions, traditional CNNs and attention mechanisms often empha- size spatial adaptivity, neglecting channel-wise adaptability. To address this, iiABlock integrates ECANET Wang et al. (2020g, which efficiently computes channel attention weights by modeling inter-channel relation- ships. Compared to SENET Hu et al. (2018), ECANET offers improved efficiency, scalability, and accuracy. Given input X E Cx adaptive average pooling reduces spatial dimensions to P RCxxx1 followed by a 1 x 1 convolution and sigmoid activation to produce attention weights 2 E [0, 1]. These weights mod- ulate channel significance through element-wiss multiplication: ECA = X @ Z. Feature maps are then downsampled via a x 1 convolution with stride after each stage.
3.5 Feature Interaction Fusion
iiABlock enables efficient multi-scale feature learning through a structured feature interaction mechanism that splits input channels into three branches: atrous convolution, inverted bottleneck, and r-MHSA. Each branch processes its feature subset independently and the outputs are fused via concatenation, followed by channel shuffling to enhance cross-branch interaction. This design captures short-range, long-range, and channel-specifie dependencies in parallel while maintaining simplicity and low computational cost, outper- forming complex fusion strategies such as heavy cross-attention Chen et al. (021a)) multi-level stacking Li et al. (2019). Empirically, we found the channel ratio r (1:6:1) optimal. For input X E Cx channels are split as æ1, x2, F3 with dimensions approximately [C/8], [3C/4], and [C/8], respectively. Corresponding functions f1, f2, and fa process each branch, whose outputs are concatenated (Figure 3). Finally, channel shuffling is applied to strengthen inter-branch interaction, defined in equation
X shufftc(f(x)) E CxHWW
The fused output X effectively combines diverse receptive fields and attention mechanisms with efficiency and expressiveness. Table [ presents the schematic details of tiABlock.

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Table Schematic of spatial sizes and channel allocations across branches and fusion.
Branch
Input Size
Operation r-MHSA
Output Size
T-MHSA
C x H x W
X H x W
Inverted Bottleneck
MC
X H X W
Convolution Atrous Conv concat(y), U2, Uz) - shuffle
SC
X H X W
Atrous Conv
H X W
X H X W
8 C X W
Fusion
(C/8 3C C/8) X
3.6 Computational--ffiiieees
iiABlock processes visual inputs X RCXW*H (with M W H) using r-MHSA, dilated, and inverted bottleneck convolutions. Given expansion factor F. = 2C, their computational complexities are:
?(MHSA) 4MC² 2MCC
(9)
((DilatedConv) = 18MC2
(10)
((Bottleneck) = MC²
( م2
2 9
(11)
where is the channel reduction ratio. To enhance global modeling, iiANET adds Register Tokens to the r-MHSA branch. For N register tokens per head, the cost becomes:
Q(MHSA+ 4(M + NJC² + 2(M + NNCC,
(12)
where N & M, making the added cost negligible while improving stability and representation quality. Overall, r-MHSA captures global context but scales quadratically with M, whereas dilated and bottleneck convolutions scale linearly, offering efficient local feature extraction. iiABlock balances both for scalable high-resolution performance.
3.7 Memory-Efficiency
To reduce memory consumption and computational cost, we allocate the r-MHSA branch only 1/8 of the input channels. Additionally, r-MHSA is placed deep in the network where the spatial dimensions are low, mitigating the quadratic scaling with sequence length M. This design ensures that global attention is captured effectively without excessive memory usage, allowing iiABlock to process high-resolution images efficiently.
3.8 iANET Architectural Overview
iANET is hybrid visual recognition backbone architecture designed enhance capturing long-range de- pendencies in complex images while maintaining computational efficiency. At each stage, iiANET stacks ilABlock in parallel across four stages. Each iiABlock captures both short-range and long-range depen- dencies while incorporating global context, enabling the model to process intricate patterns effectively. The architecture also non-isotropic as it down-samples spatial features at every stage, allowing for a progressive abstraction of features. By stacking iiABlock in parallel at each stage, iiANET efficiently captures both fine- grained local details and global context, making it well-suited for complex vision tasks. Figure 3 illustrates iAAETT's architecture.
Stem (Initial stage) Given the higher resolutions of complex images, this component serves to compress the computational costs of iiANET by shrinking the spatial dimensions of the input image to half, trading off spatial details for improved model efficiency and basic feature extraction Bello et al. (2021). Let the input image be < R we apply two sequential 3 x 3 convolutional layers, each followed by a batch normalization and ReLU activation function, with the initial layer using a stride 2.
iiABlock (Building Blocks) The HANNET-B and iAANET-L variants stack iiABlocks in non-isotropic config- urations of (,,3,5, and [2,3, 10, across four stages to capture multi-scale features while reducing spatial

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H
W
HANET
H
16
Input
stem
HABLOCK
BCA
HABLOCK
ECA
RABLOCK
ECA
HABLOCK
PC
head
beaad
HX
F Figure+ 3: HANET architectural overview.
resolution. At each stage, input feature maps are down-sampled, yielding spatial dimensions of H/4 x W/4, H/8x w/8, H/16 x W/16, and H/32 W/32. Inspired by Inception designs Szegedy et al. (2015), multiple iiABlocks run in parallel per stage, enabling simultaneous processing of features with varied receptive fields. These branches are fused via concatenation followed by channel shuffling to promote cross-pate interac- tion. This hierarchical, multi-branch structure allows iiANET to efficiently capture short and long-range dependencies while maintaining a compact, efficient architecture suited for complex vision tasks.
Output Layer After the final stage, Adaptive Pooling reduces the feature map to fixed size, which is then passed through Fully Connected layer for classification.
4 Experimental Results and Comparisons
We evaluate iANETT qualitatively and quantitatively on several widely used benchmark datasets, comparing it with some stale-of-the-art CNN, ViT, SSM and hybrid models. The evaluation covers classification performance and effectiveness as a backbone for object detection and segmentation, focusing on iiANET's ability to capture long-range dependencies in complex images.
Datasets and Metrics. Experiments are conducted on diverse datasets: AID Xia et al. (017 (10,000 images, 30 scene classes), Oxford-11IT Parkhi et al. (2012) (4,978 images, 37 cat and dog breeds), and RLD Sothy et al. (2020) (5,932 images, 4 discase categorics) Additionally, ImageNet1K Deng et al. (2009)
(1.28M images, 1,000 classes), COCO-2017 Lin el al. (2014) (118K training images, 80 object categories).
and ADE20K [Zhou et al. (007)) (25K images, 150 semantic categories) are employed to assess generalization and robustness. Metrics include top-1/top-5 accuracy, Average Precision (AP), FLOPs, and throughput, providing comprehensive performance insights.
Experimental Setup. Training was performed on a Limux system with an Intel Core 17-8700K CPU, 2 NVIDIA Titan XP GPUs (12GB), and 32GB RAM. Models were trained for 90 or 150 epochs with batch size 16 using the AdamW optimizer, an initial learning rate 0.0001, and a decay rate of 0.05. For fair comparison, some baseline models were re-trained on AID, Oxford-1l1, and RLD using the authors' default settings, while models with reported results were taken from the original publications to ensure fairness and consistency, as full re-training under unified configuration would be computationally infeasible.
4.1 Qualitative Evaluation and Comparison:
iiANE Visual Inspection
We applied Grad-CAM Selvarajue al. (2017) on the final layer of iiANET and several stale-of-the-art models
ResNet-50/1100 He et al. (2016), Efficient.ettB4//10 Tan & Le (2019) DenseNet-169) 16999/20 Huang et al. (2017).
VIT-B/L-16 Dosovitskiy et al. (2020), Coat.Net-3 Dai et al (222aa), and BoTNet Srinivas et al. (2221) with
visualizations shown in Figure 4 CNN-based models generate localized heatmaps around objects due to their
limited receptive fields, while ViT-based models produce seadd altention spots, indicating interpretability
challenges. Hybrid models better capture long-range dependencies and improve interpretability. Notably, iANET excels at precisely outlining complex objects with minimal background noise, suggesting enhanced accuracy and reliability tasks reliant on long-range dependencies, such as medical imaging, autonomous driving, remote sensing, and security surveillance.
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4.2 Qualitative Results Detection and Segmentation
Figure presents qualitative results on object detection, instance segmentation, and semantic segmentation
tasks. Specifically, evaluate iAANET backbone within Faster R-CNN Ren et al. (2015) and Mask
R-CNN He et al. (2117) on the COCO val2017 dataset Lin et al. 2014), and UperNet Xiao et al. (0018)
for semantic segmentation on ADE20K Zhou et al. (017)). The visual results demonstrate that iiANET
effectively generalizes across different dense prediction tasks, capturing both fine-grained object boundaries and global contextual cucs. This shows that iAANET can be seamlessly integrated into standard detection and segmentation frameworks while maintaining quality visual predictions.
Object Detection
Instance Segmentation
Semantic Segmentation
Qualitative results of object detection and instance segmentation COCO val2017 Lin et al. 2014hh and semantic segmentation on ADE20K Zhou et al. (217)). From left to right, the results are obtained using iAANET as the backbone Faster R-CNN, Mask R- R-CNN, and UperNet for semantic segmentation.
Figure 5:
4.3
Quantitative Evaluation and Comparison
4.3.1
Classification performance
Table shows iAANET classification performance across three standard benchmark datascts. The quantita- live evaluation involves comparing HAANET-B and iANNET- with several recent stale-of-the-arl models in terms of both accuracy and computational cost on ImageNet-IK, AID and Oxford-III.
ImageNet-1K:: iAANET-L achieves a competitive lop-1 accuracy of 84.9% while maintaining com- putational efficiency with only 13.45 GFLOPs and 50.9M parameters. This demonstrates a decent accuracy-efficiency tradcoff, outperforming larger and more computationally expensive models such as ViT-L/16 (76.5%. 59.69 GFLOPs) and CoAtNet-3 (84.5% 32.53 GFLOPs). These results show iANET's ability to learn robust, discriminative features with minimal complexity.

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AID Dataset
Input
ResNet-50
ResNet-111
EffNet- B4
EffNet-B5
Densel 69
Dense201
CoAtNet
ViT-B/16
VIT-L/16
Bo
нANET
OXFORD-III Dataset
Input
ResNet-50
ResNet-101
EffNet-B4
EffNet-B55
Dense169
Dense201
CoAtNet
VIT-B/16
VIT-L/16
BotTN
ПANEТ
RLD Dataset.
Input
Nes-
ResNet-101
EffNet- B4
EANett-5
Densel 69
Dense201
CoAtNet
VIT-B/16
VYT-I//16
BoTNet50
iANNE
Figure Visual inspection of iANET compared to some SOTA models using Grad-CAM Selvaraju et al. (2017) highlights the model's ability to focus on complex object regions. The heatmaps demonstrate HANETT's improved capacity to capture long-range dependencies and provide more interpretable attention relevant spatial structures compared to ResNet-500, BoTNet, ViT-B/16, and CoAtNet.
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AID Dataset: Contains complex spatial structures and long-range dependencies, which challenge traditional models. liANET-B shows good performance, achieving 80.57% top-1 accuracy using only 8.22 GFLOPs. This surpasses some established CNN-based backbones such as ResNet-101 (68.93%) and DenseNet-201. (71.60%), as well as some transformer-based models like ViT-B/16 (69.93%), indicating iiANET's enhanced ability for modeling spatial complexity in aerial imagery.
Oxford-IIIT Pets: In the fino-grained Oxford-IIIT Pets classification task, which requires distin- guishing subtle visual differences between classes, iiANET-L achieves the highest top-1 accuracy of 76.23%, outperforming some strong baselines including CrossViT-F (73.1%) and DeïT-B (71 (7100)). The smaller variant iiANET-B, achieves 74.04%, outperforming ViT-B/10 (51.23%) and ResNet--11 (59.25%), demonstrating strong generalization and fine-graie ine-grained discriminative power.
Analysis. We observe that iANET outperforms some CNN and transformer-based baselines across these datasets, achieving better balance between accuracy and computational cost. Notably, VIT variants underperform on smaller datasets like AID and Oxford-IIIT Pets, likely due limited training samples and the need for large-scale data to learn robust representations. In contrast, iiNNT''s hybrid backbone effectively captures both local details and long-range dependencies, providing improved generalization and fine-grained discriminative power.
4.3.2 Object Detection
We evaluate iAANET as a backbone for YOLOv8 on the COCO val2017 and test2017 datasets (Table 3 Both HiANET-(B, L) show good performance, achieving bounding box mAP of 62.6% and 63.1% on val2017, and 63.0% and 64.4% on test2017, respectively. Compared to backbones like ResNet-50 and Swin variants, iAANET achieves comparable efficiency in capturing multi-scale features and long-range dependencics. These results indicate that iAANET effectively balances precision, recall, and computational efficiency for object detection in complex scenes.
better accuracy with slightly lower computational cost, highlighting its
4.3.3 Instance Segmentation
We evaluate iANEET on COCO val2017 for instance sogmentation using Mask R-CNN (1x schedulo) (Table Both iANNE-- and iANEE-- achieve better results compared to some CNN and transformer backbones, with Ab of 45.3% and 45.8% and Apm of 39.5% and 42.1%. The results show iiANET ability to capture long-range dependencies and accurately segment instances in images with complex structures and occlusions.
4.3.4 Semantic Segmentation
Table 5 compares semantic segmentation (SS) performance on the ADE20K dataset across various backbones using the UPerNet framework. Traditional CNN-based models such as ResNet-101 with DeepLab v3+ or UPerNet. achieve mloUs below 45%, while transformer-based and hybrid architectures show clear gains. Swin-B and Focal-B improve SS to 49% mloU but at the cost of over 120M parameters. In contrast, the proposed HANETT models achieve better accuracy with notably fewer parameters: iiANET-B attains 48.5% mIoU with 66M parameters, and iiANET-L achieves 49.2% mloU with 92 M, outperforming some heavier transformer baselines. This demonstrates iiANET's strong balance between efficiency and SS performance.
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Table Classification result comparison on ImageNet-1K, AID, and Oxford-III datasets.
Backbone
Size
Train
Params
FLOPs
Top-1
Top-5
ResNet-101
He et al.
2016
224
90
44.5M
14.58G
78.0%
94.0%
IMAGENET -KKKKKK Deng et al. (2009)
EffNet-B5
Tan & Le
2019
224
90
30.4M
4.49G
83.6%
96.7%
Dense201
Huang et al.
[2017
2242
20.0M
7.35G
77.42%
93.6%
VIT-L/16
Dosovitskiy
(2020)
224
150
304.3M
59.69G
76.53%
93.2%
MobileViT Mehta & Rastegari
Mehta
[2021
256
90
6M
2G
77.0%
94.6%
DilateFormer-B Jiao et al. (0023
224
120
48M
9.96G
84.9%
BoT50 Srinivas et al. 12021
256
90
25.6M
3.18G
84.4%
CoAtNct-3 Dai et al. 2021b
224
90
168M
32.53G
84.5%
Vim-S Zhu el. al. 2024
2242
90
26M
5.3G
80.3%
S4ND- VIT-B Nguyen et
2022
224
90
89M
17.1G
80.4%
VMamba-11 Liu al. 20025
224
30M
4.9G
82.6%
Swin- T Dai
2021b
224
300
29M
4.5G
81.3%
DeiT-B Touvron al.
2021
2242
120
86M
17.5G
81.8%
Cross-Vil VIT-B Chen al.
CCen
2021b
224
120
105M
20.1G
82.2%
CvT-21 Liu el al.] 2021
224
300
32M
7.1G
82.5%
Next-Vii Biott-ii--Bi Li et al.
(2022
224
300
44.8M
8.3G
83.2%
ÎAANET-
299
90
25.200 25.2M
8.22G
79.34%
94.71%
-iANET-L
299
120
50.9M
13.45G
84.9%
96.83%
ResNet-101
He et al.
2016
224
90
44.5M
14.58G
68.93%
93.37%
EB5
Tan & Le
2019
224
90
30.4M
4.49G
65.73%
92.07%
Dense201
Huang et al.
[2017
224
20.0M
7.35G
71.60%
94.37%
AID Xia et al. 20177
ViT-B/16
Dosovitskiy
2020
224
150
86.6M
16.86G
69.93%
93.27%
MobilcViT
Mchta & Rastegari
2021
256
90
6M
2G
66.76%
91.23%
DiNAT-B Hassani & Shi 2022
224
90
90M
13.7G
79.12%
93.27%
BoT50 Srinivas et al.
2021
256
90
25.6M
3.18G
72.50%
94.27%
CoAtNet-3 Dai et al. 20216
224
90
168M
32.53G
80.17%
94.93%
Vim-S Zhu ct al. 2024
224
90
26M
5.3G
74.2%
VMamba-
Liu et al. 2025
224
90
30M
4.9G
78.9%
Swin-T Dai
2021b
2242
300
29M
4.5G
78.0%
DeïT-B Touvron al.
2021
224
120
86M
17.5G
81.2%
Cross- VIT-B
Chen
2021b
2242
120
105M
20.1G
80.6%
ПANEET-
299
90
25.2M
8.22G
80.57%
95.67%
jiANET-L
299
90
50.9M
13.45G
83.11%
96.07%
ResNet-50 He et al. 2016
224
90
25.6M
7.71G
59.07%
86.61%
ResNet-10
He et a..
2016
224
90
44.5M
14.58G
59.25%
87.38%
OXFORD-II -ODRD-III Parkhi et al. 2012
E5
Tan &r Le
2019
224
90
30.4M
4.49G
47.34%
78.92%
Dense201
Huang et al.
[2017
224
90
20.0M
7.35G
62.55%
86.84%
ViT-B/16
Dosovitskiy
2020
2242
86.6M
16.86G
51.23%
82.31%
VIT-L/16
Dosovitskiy
2020
224
150
304.3M
59.69G
53.19%
83.28%
MobileViT
Mehta & Rastegari
2021
256
90
6M
2G
51.89%
84.52%
DiNAT-B Hassani & Shi 2022
224
90
90M
13.7G
69.37%
92.09%
DilateFormer-B Jiao ct al. 20223
224
120
48M
9.96G
64.85%
88.18%
Bo0 Srinivas et al.
2022
256
90
25.6M
3.18G
64.13%
90.00%
CoAtNet-3 Dai et al. 2021b
2242
90
168M
32.53G
67.57%
91.36%
VMamba- Liu et al.
2025
224
90
30M
4.9G
67.8%
Swin-T
Dai 202
2242
300
29M
4.5G
72.3%
DeîT-B
Touvron al. Chen 12021
224
120
86M
17.5G
71.0%
Cross-
2021b
2242
120
105M
20.1G
73.1%
HANET-B
2992
90
25.2M
8.22G
74.04%
93.98%
GANET-L.
2992
90
50.9M
13.45G
76.23%
94.54%
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Table 3: Object detection results on the COCO dataset.
Backbone
Object Detector
AP" val2017
AP" test2017
ResNet-50 Carion al. (2020]
Faster R-CNN
36.7
37.9
FD-SwinV 2 Wei ct al. [2022
HTC++
64.2
Florence-CoSwin-
uan et al.
2021
DyHead
62.0
62.4
Swin-L Liu et al. (202Ib)
DINO
63.2
63.3
BETT-3 Wang et al. (2022)
VITDet
63.7
Swin V2-G Liu al. 2022
HTC++
62.5
63.1
MANNET-B
YOLOv8
62.6
63.0
LiANET-L
YOLOv8
63.1
64.4
Table 4: Instance Segmentation on COCO dataset with Mask R-CNN (Ix schedule).
Backbone
Apbos
A.
APhox
A.mask
APmask
APmas
ResNet-50 He et al. (0166)
38.0
58.6
41.4
34.4
55.1
36.7
PVT-M Hassani et al. (2023
42.0
64.4
45.6
39.0
61.6
42.1
TRT-ViT-C Wang et al.
2221a
44.7
66.9
48.8
40.8
63.9
44.0
Focal-T
Xia et al.
[202
44.8
67.7
49.2
41.0
64.7
44.2
UniFormer-S/In
Yang et al. 2021a
45.6
68.1
49.7
41.6
64.8
45.0
Swin-T Li al.
(2023)
42.2
64.6
46.2
39.1
61.6
42.0
Dilate-S
Jiao et al.
2023
45.8
68.2
50.1
41.7
65.3
44.7
BoT50
Srinivas et al.
(2021
43.7
37.9
PVT-L
Wang et al.
2021bb
42.9
65.0
46.6
39.5
61.9
42.5
CAE-CA GReaT
Zhang et al.
(0224)
44.0
67.9
47.3
HAANET-
45.3
65.1
49.8
39.5
58.9
58.0
HAANET-L
45.8
68.3
51.7
42.1
65.1
59.5
4.4
Visualizing long-range spatial transport
Joura
Axcet
Convererce Not boited recunple tall
Convergenc scn tard
15-
Figure 6: To evaluate the models' ability to capture long-range dependencies, we transport information across distant spatial regions. We place the source one corner and the target another corner using various shapes. Both ResNet-50 and iiANET are trained under identical settings (AdamW, 1r=0.001, 50 epochs, input 2242, MSF loss). iANET achieves faster, smoother convergence and more accurate reconstruction than ResNet-50, effectively capturing range dependencies and spatial transport patterns.
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Table 5: Semantic segmentation results on the ADE20K dataset.
Backbone
Method
img size
#param
mloU %
ResNet-101
He et al.
2016
LeepLab v3+
512
63M
44.1
MosNet-1011
He et al.
2016
UperNet
5122
86M
44.9
DeiT-S Touvron el. a..
2002
UperNet
5122
43M
44.0
Swin Dai et al.
2021b
UperNet
5122
121M
48.1
ViM-S Zhu al.
2024
UperNet
5122
46M
44.9
Focal-B3 Yang et al.
(021b)
UperNet
512
126
49.0
Mamba Vision-B Hatamizadeh & Kautz (2024)
UperNet.
5122
126
49.1
MANET--
UperNet
5122
66M
48.5
HANET-L
UperNet
512²
92M
49.2
4.5 Ablation Studies
We performed ablation studies to analyse the impact of different components, scaling, and efficiency of HANET..
Settings. All ablation experiments were conducted the AID dataset Xia et al (017 using NVIDIA GeForce RTX 2070 with Max-Q Design (8 GB VRAM), CUDA 12.1 and Py ToTcch 2.0.
4.5.1 iiABlock vs. MHSA Complexity Analysis
Table 6 shows iiA Block and MHSA evaluation across channel-spatial scaling, and attention heads.
Channel Scaling (32-644 182-664-112 @ 112x112): iiABlock shows more efficient scaling with channel width, achieving lower FLOPs (-5,4%), fewer parameters (-25.4%), and se 28% faster inference compared to MHSA. Its memory growth also smaller, confirming better computational efficioncy.
Spatial Resolution (28x28 144x14 @ 512/1024): iiABlock achieves 30% lower FLOPs. 29% fewer parameters, and 20% faster latency than MHSA. It also uses slightly less memory and maintains higher throughput, demonstrating robust efficiency across scales.
Hcads (2-+4 @ 56x56): With 2x heads, iiABlock maintains about 4% lower FLOPs and 21% fewer parameters, while achieving up to +34% higher throughput than MHSA. Although latency increases slightly, BlABlock consistently achieves faster inference and better scaling efficiency.
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Table Complexity comparison between iiABlock and MHSA across scaling dimensions.
Type
Spatial Dim.
In/Out
FLOPs(G)
Params
Latency (ms)
TP
Head
Scaling Channels
iiA Block
(112,112)
32/61
1.024
5.932K
38.87
25.73
iiABlock
(112,112)
64/128
1.230
21.944K
41.32
24.20
MHSA
(112,112)
32/64
1.040
7.520K
53.93
18.54
MHSA
(112,112)
64/128
1.317
29.376K
57.90
17.27
Scaling Attention Heads
iiABlock
(56,56)
32/64
108.471M
5.932K
9.44
105.94
fiABlock
(56,56)
32/64
167.472M
5.932K
12.00
83.34
4
MHSA
(56,56)
32/64
112.586M
7.520K
12.64
79.11
2
MHSA
(56,56)
32/64
171.586M
7.520K
12.81
78.05
4
Scaling Spatial Dimension
BiABlock
(28,28)
512/1024
1.037
1.305M
5.59
178.74
4
iiA Block
(14,14)
512/1024
257.627M
1.305M
4.14
241.35
MHSA
(28,28)
512/1024
1.454
1.841M
6.61
151.29
MHSA
(14,14)
512/1024
361.842M
1.841M
4.32
231.71
4.5.2 Effect of iiABlock Components
Table highlights the effectiveness of the proposed combination of modules within the iiABlock. Variant (c), which integrates MBConv, Dilated Convolution, and MHSA, achieves the best performance with top-1 accuracy of 80.57%. This demonstrates that the synergy between local depth-wise convolutions, dilated receptive fields, and global attention significantly improves feature representation. Variants (a), (b), and (e) show progressive improvements, while (d), which lacks MBConv, suffers a performance drop, underscoring the importance of lightweight local feature extraction.
Table 7: Ablation studies on various variants of iiABlock using the AID datasct.
Settings
Model (Components)
Size
Top-1
MBConv2
299
69. 69.23%
MBConv + Dilated Conv
299
74.85%
(c)
MBCoπv + Dilated Conv + MHSA
299
80.57%
(d)
Dilated MHSA
299
67.01%
MBCoπv + MHSA
299
78.72%
4.5.3 Ablation on MHSA Heads and Block Ratio
Table presents the results of ablation experiments in which the channel ratio was modified from 1.6.1 to 2.4.2, resulting in reduction of 9.6M parameters and 2.97G FLOPs, with a corresponding 6.36% decrease in top-1 accuracy. Increasing the number MHSA heads in the last iiANET block from 8 to 16 caused minimal changes in computational cost and -0.34% change accuracy. These results indicate that reducing the block width improves efficiency but decreases accuracy, while increasing attention heads has minimal effect, suggesting the original configuration is optimal.
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Table 8: Ablation study on the effect of changing the MHSA head size and iiABlock ratio on AlD dataset.
Settings
Model (Components)
Size
#params
FLOPs
Top-1
HANET (all layers)
Ratio: 1.6.1 + 2.4.2
299
15.72M
5.64G
74.21%
iANET (layer4)
Head Size: 8 16
299
25.32
8.604G
80.23%
4.5.4 Effects of Different Fusion Strategies
Table compares cross attention, gated fusion, and additive fusion in йAANET-B. Additive fusion provides a better trade-off, with lower computational cost (8.60 GFLOPs), fewer parameters 25.32M, and faster inference (27.70 mss 36.10 img/s), with a slight decrease in top-1 accuracy 80.57% compared to gated fusion, Cross attention achieves higher accuracy 81.32% but the cost of increased complexity and latency.
Table Ablation studies on fusion
Fusion Type
#params
FLOPs
latency(m/s) TP(in//sec)
Top-1 (%)
Cross Attention Chen al (021cc)
27.18M
9.39G
42.95
23.29
81.32%
Gated fusion Jiang & Ji 2022
28.422M
9.56G
36.66
27.28
80.75 %
Additive Fusion
25.32M
8.60G
27.70
36.10
80.57%
4.5.5 Effect of register tokens
Table 10 demonstrates that introducing register tokens into the 2D-MHSA improves performance on the AID dataset. With four registers, the model achieves the highost top-1 accuracy of 80.57%, compared to 80.36% without any. The consistent improvement suggests that registers help compensate for MHSA's 's order-agnostic nature by enhancing the contextual richness of learned representations.
Table 10: Ablation study on the effect of adding register tokens to 2D-MHSA mechanism
Registers
Top-1
0
80.36%
1
80.37%
2
80.45%
4
80.57%
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Figure Grad-CAM Selvaraju et al. (2017) heatmaps illustrating the effect of registers on iANET for the AID dataset Xia al. (2017). Adding registers to the r-MHSA module enables iANNET capture long-range dependencies more effectively, improving interpretability of the learned representations.
Viaduct
Mountains
Storagetanks
River
Input
iiANET
HANETT HANET-Registen Ree--teesstss
5
Conclusion
This work proposed iAANET, novel hybrid model designed efficiently improve long-range dependencies in complex images by integrating CNN layers and the MHSA mechanism with registers in parallel. Com- prehensive qualitative and quantitative results show improvements in capturing long-range dependencies compared to some previous SOTA models. Additionally, we validate the performance of our model across diverse datasets and highlight its potential as an efficient backbone for visual downstream tasks.
5.1 Limitations
iANETT is effective images with prevalent long-range dependencies but may be less effective datasets with mostly localized objects. Its multi-branch and ratio design can also introduce scaling challenges, making it difficult to determine optimal configurations in some downstream visual recognition tasks.
This work was supported by NewraLab, Suzhou, China, an research and develop- ment startup founded by Yunusa Haruna. The authors gratefully acknowledge the support of the NewraLab team throughout this research.
Acknowledgment
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Published in Transactions on Machine Learning Research (12/2025)
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Reviews:
[{"review_id": "W4lTzuMnSY", "reviewer": "Reviewer_9scR", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper introduces iiANET, a hybrid visual backbone for modeling long-range dependencies. Its core unit, iiABlock, runs three paths in parallel: a global r-MHSA branch with register tokens, an inverted-bottleneck MBConv branch, and a 3×3 atrous convolution branch, followed by fusion with channel shuffling and an ECA step. The backbone stacks iiABlocks across four stages with downsampling.\n\n\nExperiments cover image classification and transfer to detection and instance segmentation. Datasets include ImageNet-1K for classification, AID and Oxford-IIIT Pets for transfer, and COCO for detection and instance segmentation. The paper shows qualitative Grad-CAM visualizations and reports ablations on the contribution of each branch and on design hyperparameters.\n\n\n\n# Strengths:\n\n1. Clear hybrid design that unifies local detail and global context inside a single reusable block.\n\n2.  Qualitative heatmaps indicate attention to extended structures consistent with the long-range goal.\n\n3. Ablations on the branch composition and on register tokens provide initial evidence that the three-path combination is beneficial.\n\n# Weaknesses:\n\n1.  Several key comparisons are not performed under strictly matched training protocols. For detection and instance segmentation, iiANET is paired with a specific detector while baselines often use other frameworks, schedules, or image sizes, which makes backbone-only conclusions hard to draw.\n\n2.  Efficiency reporting is incomplete. FLOPs and parameters appear, but wall-clock latency, memory usage, and throughput under fixed hardware and batch are not consistently provided.\n\n3. Parts of the technical exposition are hard to follow, especially the register token formulation and tensor shapes in the attention branch. The notation could be clarified, and a compact complexity derivation for the 2D r-MHSA with registers would help.", "claims_and_evidence_yn": "No", "claims_explanation": "The architectural idea is sound and the qualitative visualizations are encouraging, but the empirical support does not yet fully substantiate the strongest claims. The main issues are comparability and completeness. For classification and dense tasks, some baselines  seem to be trained under different recipes or detectors than iiANET, so the superiority of the backbone itself is not isolated. Ablations are helpful but limited in scope and dataset coverage. The description of r-MHSA with registers lacks sufficient formal detail to independently verify complexity and stability claims. With standardized protocols, expanded ablations, and clearer equations, the evidence could be made convincing.", "audience_yn": "Yes", "audience_explanation": "Hybrid CNN-attention designs for long-range dependency are highly relevant. A reusable block that blends global attention, atrous convolution, and MBConv is of practical interest, especially if it can be dropped into common pipelines. The topic aligns well with the audience of  TMLR.", "requested_changes": "1. Re-run core comparisons under strictly matched settings. For ImageNet-1K, either adopt a single training recipe across all baselines or rely on widely reproduced numbers with matched input sizes and regularization. For detection and instance segmentation, compare backbones within the same detector, same schedule, and same resolution so the detector is not a confounder.\n\n2. Provide comprehensive efficiency reporting. Include latency on a specified GPU, throughput at fixed batch and resolution, and peak memory during training and inference. Add a per-stage FLOP and parameter breakdown and an accuracy-vs-complexity plot.\n\n3.  Extend ablations beyond a single transfer dataset. Quantify the marginal contribution of registers and relative positional handling on a large-scale setting. Test alternative fusion strategies at the block output, such as summation and gated fusion, and include sensitivity to the channel split among branches.\n\n4. Add a concise complexity derivation for the 2D r-MHSA with registers. Specify tensor shapes for Q, K, V, registers, attention map dimensions, and any sparsity or approximation used. This will also help readers assess memory scaling at higher resolutions.\n\n5. Broaden related baselines to include recent long-range modules and hybrid backbones that explicitly target efficiency and global context. Ensure that training setups are matched when drawing comparative conclusions.\n\n6. Tighten notation in the attention branch section and align symbols with the figure. Add a small schematic that tracks spatial sizes and channels through the three branches and the fusion step.", "broader_impact_concerns": "I am not aware of impact concerns.", "additional_comments": "1. **Positioning relative to recent PDE-inspired and non-local operators:** It is highly important that the authors compare and discuss Advection Augmented Convolutional Neural Networks (NeurIPS 2024). That work introduces a semi-Lagrangian push operator that transports features, enabling non-local movement of information within a CNN stack. Since your goal is to capture long-range dependencies efficiently, an explicit comparison along three axes would help readers position iiANET: Mechanism for non-locality. Please discuss how iiABlock’s global branch differs from advection in terms of receptive field growth, information transport, and stability.\nEfficiency and memory. If possible, include a small controlled benchmark where objects must be related over long distances or undergo spatial displacement, and compare iiANET with an advection-augmented CNN under matched training.\n\n\n2. **Data and setup clarifications:** Please clarify which baselines in the classification table were retrained in your pipeline and which were taken from prior work. If retrained, include exact hyperparameters and confirm that augmentation strength, optimizer, and schedule were aligned. For Oxford-IIIT Pets, per-class results or confusion matrices would reveal whether errors cluster by visually similar breeds.\n\n3. **Limitations:** Consider explicitly stating where iiANET is less effective. For example, settings dominated by very local texture cues may not benefit from the global branch. A short diagnostic on when the three-path design helps most would be useful.", "extra_scores": {}, "extra_text": {}}, {"review_id": "m0Ui30o9r7", "reviewer": "Reviewer_XPD1", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents a hybrid network for capturing long-range feature dependencies in visual recognition tasks, namely image classification, object detection, and instance segmentation.", "claims_and_evidence_yn": "No", "claims_explanation": "The authors claim that the proposed approach is computationally efficient and effective compared to existing methods. While the idea of combining convolutional and self-attention mechanisms is promising, the manuscript has several shortcomings that need to be addressed before publication.", "audience_yn": "Yes", "audience_explanation": "See above.", "requested_changes": "The authors claim that their iiABlock achieves lower computational complexity and fewer parameters than Multi-Head Self-Attention (MHSA). However, Figure 2 does not provide a clear quantitative comparison (e.g., FLOPs, parameter counts) between iiABlock and MHSA, making the efficiency claim ambiguous.\n\nThe paper lacks comparisons with state-of-the-art hybrid vision architectures, such as CAE-GReaT, NextViT, CPVT, CvT, and Convnets + Transformers (e.g., CoAtNet, PVT). Without these comparisons, the claimed improvements are not fully substantiated.\n\nWhile Grad-CAM is useful for classification, the manuscript overlooks qualitative analysis in detection and segmentation.", "broader_impact_concerns": "No.", "additional_comments": "This work presents interesting ideas but needs stronger empirical validation (particularly efficiency proofs and hybrid model comparisons) and better explanations for design choices.", "extra_scores": {}, "extra_text": {}}, {"review_id": "Tr0iKYx8QG", "reviewer": "Reviewer_YRJK", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents iiANET (Inception Inspired Attention Network), an efficient hybrid visual backbone designed to improve the modeling of long-range dependencies in complex visual recognition tasks. The core innovation of iiANET is the iiABlock, a unified building block that integrates a modified global r-MHSA (Multi-Head Self-Attention) and convolutional layers in parallel. This design enables iiABlock to simultaneously capture global context and local details, making it effective for extracting rich and diverse features. By efficiently fusing these complementary representations, iiABlock allows iiANET to achieve strong feature interaction while maintaining computational efficiency. Extensive qualitative and quantitative evaluations on some SOTA benchmarks demonstrate improved performance. However, the method proposed in this paper has not achieved sufficient progress in performance, nor has it optimized the number of parameters; thus, its innovation has not been well demonstrated.", "claims_and_evidence_yn": "Yes", "claims_explanation": "This paper demonstrates the performance and innovation of the proposed method through comparative experiments, visualization experiments, and ablation experiments.", "audience_yn": "Yes", "audience_explanation": "Efficient hybrid visual backbones have always received widespread attention, influencing the development and innovation of deep learning, and driving the advancement of various types of models.", "requested_changes": "1. Please incorporate literature from the past three years for comparative experiments.\n\n2. Please expand more visual tasks for comparison.\n\n3. Please further discuss the computational efficiency to demonstrate the superiority of the model.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)