Semantic segmentation demands a careful balance between accuracy, efficiency, and scalability, which remains difficult to achieve for high-resolution imagery. Convolutional networks effectively model local patterns but struggle with long-range dependencies, whereas Vision Transformers capture global context at a high computational cost. While recent work largely focuses on encoder design, the bottleneck stage, central to contextual aggregation and information flow, has been relatively overlooked. We propose SiConMo, a lightweight yet effective framework, implemented in two variants: an RGB-only model (SiConMo) and a GME-enhanced variant (SiConMo†). We show that simplicity arises from a key design principle: at very low computational budgets, the bottleneck is the most efficient stage to integrate local and global context. SiConMo integrates three complementary components: a Token Pyramid Extraction Module for hierarchical multi-scale representation, a Transformer-Branched Depthwise Convolution block for bottleneck-aware context modeling, and a Feature Merging Module that preserves spatial structure while enhancing semantic consistency. Extensive experiments on ADE20K, PASCAL Context, Cityscapes, and COCO-Stuff demonstrate that SiConMo achieves a state-of-the-art accuracy-efficiency trade-off among lightweight semantic segmentation models, highlighting simplicity as a powerful design principle.
Figures & tables
Figure 1: (Top) Efficiency–accuracy comparison of lightweight semantic segmentation models on ADE20K validation set. Circle sizes denote GFLOPs. SiConMo achieves a superior trade-off, illustrating the effectiveness of a simple bottleneck-aware design principle. (Bottom) Variants of the proposed SiConMo architecture.
Figure 2: Overview of the proposed SiConMo framework. The architecture integrates the Token Pyramid Extraction Module (TPEM) for multi-scale representation, the Trans-BDC block for bottleneck-aware context modeling, and the Feature Merging Module (FMM) for adaptive spatial–semantic integration.
Models
Input
mIoU
GFLOPs
Params
Latency(ms)
SiConMo*
448×448
34.4
0.5
1.7M
14
SiConMo † *
448×448
34.7
0.5
1.7M
14
Table 1: Quantitative evaluation on ADE20K [ 32 ] and complementary analyses. (a) Comparison with state-of-the-art representative semantic segmentation models on ADE20K dataset. (b) Input-resolution variants trained at 448×448 . (c) Ablation study on ADE20K. (d) GME variants on ADE20K. GFLOPs are reported at 512×512 input resolution with single-scale inference; ‘-’ denotes unreported values. (e) object detection results on COCO dataset using RetinaNet (Sec. 4.3 ). SiConMo † : GME variant.
Figure 3: Visual results on the ADE20K validation set. The proposed SiConMo model produces high-quality segmentation maps with improved spatial consistency and fine-detail preservation, compared to TopFormer [ 30 ] .
Methods
Backbone
mIoU 59
mIoU 60
GFLOPs
DeepLabV3+ [ 3 ]
ENet-s16 [ 22 ]
43.07
39.19
23.00
DeepLabV3+ [ 3 ]
MobileNetV2-s16 [ 23 ]
42.34
38.59
22.24
LR-ASPP [ 11 ]
MobileNetV3-s16 [ 11 ]
38.02
35.05
2.04
TopFormer [ 30 ]
TopFormer-T [ 30 ]
40.39
36.41
0.53
SeaFormer [ 26 ]
SeaFormer-T [ 26 ]
41.49
37.27
0.51
SiConMo (Ours)
Ours
41.84
37.49
0.47
Table 2: Results on PASCAL Context test set [ 19 ] .
Methods
Encoder
mIoU
GFLOPs
PSPNet [ 31 ]
MobileNetV2 [ 23 ]
70.2
423.4
FCN [ 16 ]
MobileNetV2 [ 23 ]
61.5
317.1
SegFormer [ 27 ]
MiT-B0 [ 27 ]
71.9
17.7
L-ASPP [ 3 ]
MobileNetV2 [ 23 ]
72.7
12.6
LR-ASPP [ 11 ]
MobileNetV3-Large [ 11 ]
72.4
9.7
LR-ASPP [ 11 ]
MobileNetV3-Small [ 11 ]
68.4
2.9
Table 3: Results on Cityscapes validation set [ 4 ] .
Methods
Encoder
mIoU
GFLOPs
PSPNet [ 31 ]
MobileNetV2-s8 [ 23 ]
30.14
52.94
DeepLabV3+ [ 3 ]
EfficientNet-s16 [ 25 ]
31.45
27.10
DeepLabV3+ [ 3 ]
MobileNetV2-s16 [ 23 ]
29.88
25.90
LR-ASPP [ 11 ]
MobileNetV3-s16 [ 11 ]
25.16
2.37
TopFormer [ 30 ]
TopFormer-T [ 30 ]
28.34
0.64
SeaFormer [ 26 ]
SeaFormer-T [ 26 ]
29.24
0.62
Table 4: Results on COCO-Stuff test set [ 1 ] .
Figure 4: The architecture of the proposed SiConMo model for the task of image classification.
Method
Input Size
Top-1 Accuracy(%)
GFLOPs
Parameters
SiConMo (Ours)
224×224
66.2
0.13
1.79M
SiConMo † (Ours)
224×224
66.4
0.13
1.79M
Table 5: SiConMo results for ImageNet classification.
Stage
Details
Output Resolution
layer
kernel size
expand ratio
output channels
stride
Stem
Conv
3
-
16
2
MobileNetV2
3
1
16
1
256×256
MobileNetV2
3
4
16
2
MobileNetV2
3
3
16
1
128×128
MobileNetV2
5
3
32
2
Table 6: Architectural details of SiConMo model. For input resolution of 512×512 .
Figure 5: Design of Feed-Forward Network.
Figure 6: Visual results on the ADE20K validation set. Our proposed model illustrates superior consistency with the ground truth segmentation results, highlighting its robustness.
Nanjing University of Science and Technology · State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery · Department of Electrical and Computer Engineering, Sungkyunkwan University +2