基于动态提示与知识蒸馏的轻量化多模态语义分割方法

沈博; 张志翔; 商正莹; 朱振宇; 杨刚

doi:10.11999/JEIT260729

基于动态提示与知识蒸馏的轻量化多模态语义分割方法

doi: 10.11999/JEIT260729 cstr: 32379.14.JEIT260729

西北工业大学计算机学院西安 710129

详细信息

作者简介:
沈博：男，副教授，研究方向为分布式计算。邮箱：shen@nwpu.edu.cn

张志翔：男，硕士生，研究方向为计算机视觉

商正莹：女，硕士生，研究方向为计算机视觉

朱振宇：男，硕士生，研究方向为嵌入式计算

杨刚：男，教授，研究方向为分布式计算

通讯作者:
沈博　shen@nwpu.edu.cn

中图分类号: TP391.41
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- 文章访问数: 10
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出版历程
- 修回日期: 2026-10-16
- 录用日期: 2026-10-16
- 网络出版日期: 2026-10-25

A Lightweight Multi-Modal Semantic Segmentation Method Based on Dynamic Prompt and Knowledge Distillation

School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China

摘要

摘要: 语义分割是计算机视觉中场景理解的重要组成部分，然而传统方法大多基于可见光图像，在低亮度、强光照与遮挡等场景中性能急剧下降。尽管多模态融合能有效增强模型鲁棒性，现有方法普遍采用双支路网络架构，大幅增加模型参数量，难以部署在算力受限的嵌入式平台。针对上述问题，本文提出基于多模态特征融合提示网络的语义分割模型(Multi-modal Feature Fusion Prompt Network for robust semantic segmentation, MFFPNet)。该模型采用单支路骨干架构，引入轻量级动态视觉提示模块，将热红外或深度信息编码为动态提示向量，通过注意力机制注入预训练骨干网络，实现跨模态信息的高效交互。在此基础上，结合知识蒸馏对模型进行轻量化压缩，在硬件方面分层解耦架构屏蔽异构性，并引入多线程异步流水线，实现模型运行时推理优化，提升嵌入式平台上的语义分割实时性。实验结果表明，MFFPNet仅需更新9.4 M参数，约占模型总参数量的10.4%，在MFNet数据集上平均交并比(mIoU)达到63.3%，较基准方法提高1.9%，在NYUDv2数据集上mIoU达到60.6%，方法在精度与轻量化之间取得了较优平衡；轻量化变体MFFPNet-S在MFNet数据集上，以教师模型约20.0%的总参数量，保留其约88.8%的性能；提出的运行时优化策略在三种异构硬件平台上将吞吐量提升了21.7%～40.9%，满足了主流商用嵌入式平台与国产自主平台的实时部署需求。
- 语义分割 /
- 多模态融合 /
- 视觉提示 /
- 知识蒸馏 /
- 嵌入式平台
Abstract: Objective Semantic segmentation assigns a semantic category to each image pixel and supports scene understanding in autonomous driving and robot navigation. Methods based on visible-light images can lose accuracy under low illumination, strong glare, and occlusion. Thermal infrared and depth images provide complementary information, but extracting features with two full backbone networks increases model size and computational cost. These costs limit deployment on embedded devices with restricted computing resources. Parameter-efficient adaptation reduces the number of weights updated during training, while a large pretrained backbone can still impose substantial inference overhead. Model compression and runtime optimization are therefore also needed. This study develops a multi-modal segmentation framework that combines dynamic visual prompts, structured knowledge distillation, and deployment optimization. The objective is to improve the use of auxiliary modality information with limited trainable parameters, transfer the resulting representations to a smaller student network, and improve inference throughput across heterogeneous embedded platforms. Methods A Multi-modal Feature Fusion Prompt Network (MFFPNet) is constructed using an ImageNet-pretrained Mix Transformer B5 (MiT-B5) backbone and a SegFormer decoder. Most pretrained backbone parameters remain frozen, while the prompt, fusion, normalization, and decoding components are adapted to the segmentation task. The encoder retains four hierarchical stages. A lightweight Feature Fusion Prompt (FFP) module encodes thermal infrared or depth information into dynamic visual prompts, which are added to visible-light features. The prompt generated at one stage is passed to the next stage after spatial and channel adjustment, allowing auxiliary information to contribute at multiple feature scales. Global learnable prompts provide an additional mechanism for adapting the pretrained representations to the task (Fig. 1). Within the FFP module, spatial and channel attention assign different weights to the two modalities. Complementary feature reweighting is followed by cross-attention to capture cross-modal relationships and generate the visual prompt. The decoder aggregates outputs from all four stages to produce pixelwise predictions. Training combines label-smoothed cross-entropy and Dice losses to account for pixel classification and regional overlap. The backbone is initialized from pretrained weights, and training uses stochastic gradient descent with momentum and polynomial learning-rate decay. Data augmentation includes multi-scale resizing, random cropping, horizontal flipping, and photometric perturbations. For model compression, the trained MFFPNet serves as a frozen teacher in an offline distillation framework. MFFPNet-S uses MiT-B1 as its main student backbone, with MiT-B2 also evaluated on NYUDv2 (Fig. 4). A feature adapter applies convolution, batch normalization, and a rectified linear unit to reduce differences between student and teacher features (Fig. 5). Distillation combines feature content, channel relations, spatial structure, and logit supervision. A confidence mask emphasizes teacher predictions in regions with higher confidence, while a global supervision term and ground-truth labels retain training information outside the selected regions. Local and global supervision together preserve structural information while reducing the influence of low-confidence local predictions (Fig. 6). For deployment, Open Neural Network Exchange (ONNX) provides an intermediate model representation for platform-specific compilation. TensorRT uses half precision on NVIDIA Jetson Orin NX, the Compute Architecture for Neural Networks (CANN) software stack supports Huawei Atlas 200I DK A2, and the Rockchip Neural Network (RKNN) toolchain supports RK3588. Mixed precision on RK3588 retains higher precision for sensitive attention and output layers. Containerized environments and a common inference interface support deployment, while a three-stage asynchronous pipeline overlaps data loading, inference, and network communication. Results and Discussions Evaluation covers the MFNet RGB-Thermal and NYUDv2 RGB-Depth datasets. On MFNet, MFFPNet achieves 63.3% mean Intersection over Union (mIoU) with 9.4 million trainable parameters, approximately 10.4% of its 90.4 million total parameters. Its mIoU exceeds the listed CRM-B, DPLNet, and MixPrompt results by 1.9, 5.0, and 3.2 percentage points, respectively. MixPrompt uses fewer trainable parameters, indicating a tradeoff between segmentation accuracy and adaptation size (Table 2). Visual comparisons illustrate improved pedestrian contours under challenging nighttime illumination (Fig. 2). On NYUDv2, MFFPNet reaches 60.6% mIoU with single-scale testing, compared with 58.3% for DPLNet under the same testing mode. The multi-scale DPLNet and MixPrompt results use a different evaluation mode and do not establish a comparison under identical testing conditions (Table 3). Indoor examples show finer object boundaries and better separation of visually similar regions (Fig. 3). Ablation results associate the global prompt and full fusion module with improved segmentation performance (Table 4). Removing cross-attention reduces MFNet mIoU from 63.3% to 62.1% (Table 5). Fusion across all four stages also exceeds insertion at only the first or last stage, supporting the use of information at multiple resolutions (Table 6). On MFNet, the MiT-B1 student achieves 56.2% mIoU with approximately 18.1 million parameters and 19.6 GFLOPs, compared with 63.3% mIoU, 90.4 million parameters, and 122.8 GFLOPs for the teacher. The student therefore retains about 88.8% of the teacher's mIoU at 20.0% of its parameter count and reduces computation by approximately 84%. Its measured inference speed is 23.9 frames per second (FPS), compared with 10.0 FPS for the teacher (Table 8). On NYUDv2, distillation raises the MiT-B1 student's mIoU from 38.6% to 42.9%, a gain of 4.3 percentage points. The MiT-B2 student reaches 48.5%, providing higher accuracy with a larger model (Table 9). The separate MFNet distillation ablation reports successive improvements as the supervision components are added (Table 10). In synchronous deployment tests, the student achieves 55.7% mIoU at 48.6 FPS on Jetson Orin NX, 51.5% at 31.5 FPS on Atlas 200I DK A2, and 49.4% at 28.6 FPS with mixed precision on RK3588. The RK3588 INT8 configuration reaches 31.2 FPS but lowers mIoU to 41.1%, illustrating the accuracy cost of more aggressive quantization (Table 12). Over a continuous 1000-frame input, asynchronous execution raises throughput to 68.5, 41.2, and 34.8 FPS on the three platforms, respectively, corresponding to gains of 40.9%, 30.8%, and 21.7% over their serial configurations (Table 13). Conclusions The results support dynamic visual prompting as a parameter-efficient approach to RGB-Thermal and RGB-Depth semantic segmentation. Structured distillation reduces the inference cost of the large teacher while retaining 88.8% of its mIoU on MFNet. The student results on NYUDv2 also indicate a tradeoff between model capacity and segmentation accuracy. Platform-specific compilation and asynchronous scheduling provide further throughput improvements, while the deployment results show that quantization can materially reduce segmentation accuracy. The framework thus addresses training adaptation, model compression, and runtime execution as complementary aspects of embedded deployment. Future work will investigate modality completion using generative models or cross-modal distillation to improve segmentation when auxiliary sensor information is unavailable.
- semantic segmentation /
- multi-modal fusion /
- visual prompt /
- knowledge distillation /
- embedded platform

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图 1 多模态特征融合的语义分割模型MFFPNet结构

图中左侧蓝色虚线框表示PMiT模块，内部依次包含4个PETB模块，右侧为FFP内部结构；红色模块代表需更新的可训练参数，蓝色模块代表冻结的预训练参数。

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图 2 MFNet数据集上的可视化分割结果

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图 3 NYUDv2数据集上的可视化分割结果

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图 4 基于自适应结构化蒸馏的整体框架

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图 5 特征分布适配器结构与特征对齐示意图

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图 6 基于置信度的自适应掩码生成策略

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图 7 异构嵌入式计算验证平台

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表 1 实验参数配置

属性名称	数据信息
运行环境	Ubuntu 22.04
CUDA版本	12.4
显存容量	48 GB GDDR6
输入分辨率	480×640
Batch size	6
优化器	SGD
epoch	1000
初始学习率	0.005(SGD)

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表 2 在MFNet数据集上的精度对比

Method	Backbone	mIoU(%)	总参数量(M)	训练参数量(M)
MFNet^[16]	Custom CNN	39.7	-	-
RTFNet^[7]	ResNet	53.2	-	-
CMX-B2^[6]	MiT-B2	58.2	66.6	66.6
CMX-B4^[6]	MiT-B4	59.7	139.9	139.9
DPLNet^[9]	MiT-B5	58.3	88.6	7.15
CRM-B^[31]	Swin-B	61.4	-	-
MixPrompt^[10]	MiT-B5	60.1	87.2	5.7
Ours	MiT-B5	63.3	90.4	9.4

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表 3 在NYUDv2数据集上的指标对比

Method	Backbone	测试方式	mIoU (%)	总参数量(M)	训练参数量(M)
TokenFusion-B3[12]	MiT-B3	SS	54.2	45.9	45.9
CMX-B5[6]	MiT-B5	MS	56.9	181.1	181.1
CMNeXt[20]	MiT-B4	MS	56.9	119.6	119.6
DFormer-L[32]	DFormer-L	MS	57.2	39.0	39.0
DPLNet[9]	MiT-B5	SS	58.3	88.6	7.2
DPLNet[9]	MiT-B5	MS	59.3	88.6	7.2
MixPrompt[10]	MiT-B5	MS	61.2	87.2	5.7
Ours	MiT-B5	SS	60.6	90.4	9.4

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表 4 核心模块有效性消融实验

模型配置	$ {P}_{\text{global}} $	$ {F}_{\text{FFP}} $	MFNet mIoU(%)	NYUDv2 mIoU(%)	训练参数量(M)
Baseline (Frozen MiT-B5 Backbone)^[26]	-	-	40.5	48.4	-
Baseline + Global Prompt	√	-	45.1	55.0	～0.1
Baseline + Global Prompt + Simple Add	√	-	58.6	57.4	2.6
MFFPNet (Ours)	√	√	63.3	60.6	9.4

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表 5 FFP内部融合策略消融实验

融合策略	描述	mIoU(%)
Element-wise Sum	简单的逐元素相加融合	56.5
Concatenation	通道拼接后卷积降维融合	59.8
Channel + Spatial Attn Only	仅使用双路注意力加权	60.5
w/o Cross-Attention	保留互补加权，不使用交叉注意力	62.1
c/s Attn + Cross-Attn (Ours)	完整的$ {F}_{\text{FFP}} $模块	63.3

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表 6 FFP插入位置策略消融实验

实验设置	策略描述	mIoU(%)
$ {F}_{\text{FFP}} $插入位置	Stage 1 only (仅浅层)	57.6
	Stage 4 only (仅深层)	59.2
	Stage 1 → 4 (All Stages)	63.3
训练范式对比	Frozen Decoder (SegFormer-B5[26]，冻结解码器)	58.1
训练范式对比	Full Fine-tuning (SegFormer-B5[26]，全量微调，不含提示机制)	60.8

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表 7 MFFPNet关键超参数敏感性分析(MFNet数据集)

实验编号	参数设置	mIoU(%)
E0-B5	λ₁=0.5，λ₂=0.5(默认)	63.3
E1	λ₁=0.3，λ₂=0.7	62.8
E2	λ₁=0.7，λ₂=0.3	62.8
E0-B5	α=0.1(默认)	63.3
E3	α=0	63.2
E4	α=0.2	63.1

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表 8 在MFNet数据集上的性能对比

方法	总参数量(M)	训练参数量 (M)	FLOPs (G)	帧率/ (帧·s^–1)	mIoU (%)
Teacher	90.4	9.4	122.8	10.0	63.3
Student	18.1	18.1	19.6	23.9	56.2

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表 9 NYUDv2数据集上的模型性能对比与增益分析

模型	总参数量 (M)	FLOPs (G)	帧率/ (帧·s^–1)	mIoU (%)
Teacher(MFFPNet)	90.4	122.8	9.83	60.6
Student-B1(w/o KD)	18.05	19.6	25.06	38.6
MFFPNet-S (MiT-B1)	18.05	19.6	25.59	42.9
MFFPNet-S (MiT-B2)	29.33	29.1	20.09	48.5

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表 10 不同蒸馏组件对模型性能的影响(MFNet数据集)

模型配置	$ {\mathcal{L}}_{\text{logit}} $	$ {\mathcal{L}}_{\text{feat}} $	$ {\mathcal{L}}_{\text{rel}} $	$ {\mathcal{L}}_{\text{pair}} $	mIoU(%)
Student Baseline(MiT-B1)	-	-	-	-	38.6
+ Adaptive Logit Loss	√	-	-	-	45.2
+ Feature Content Loss	√	√	-	-	51.6
+ Channel Relation Loss	√	√	√	-	53.4
MFFPNet-S(Ours)	√	√	√	√	56.2
MFFPNet(Teacher)	-	-	-	-	63.3

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表 11 MFFPNet-S蒸馏权重敏感性分析(MFNet数据集)

实验编号	参数设置	mIoU(%)
E0-B1	ρ=0.7(默认)	56.21
E5	ρ=0.5	55.60
E6	ρ=0.9	56.18

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表 12 异构平台模型推理性能对比

模型配置	部署平台	量化精度	mIoU (%)	帧率/ (帧·s^–1)
MFFPNet(Teacher)	Server(L40S)	FP32	63.3	10.0
MFFPNet-S (PyTorch)	Server(L40S)	FP32	56.2	23.9
MFFPNet-S(TensorRT)	Jetson Orin	FP16	55.7	48.6
MFFPNet-S (CANN)	Atlas 200I	FP16	51.5	31.5
MFFPNet-S (RKNN)	RK3588	Hybrid	49.4	28.6
MFFPNet-S (RKNN)	RK3588	INT8	41.1	31.2

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表 13 多线程流水线优化效果对比

硬件平台	运行模式	CPU利用率 (%)	GPU/NPU 利用率(%)	帧率/ (帧·s^–1)
Jetson Orin NX	串行	35	42	48.6
Jetson Orin NX	并行	68	95	68.5
Atlas 200I DK A2	串行	55	38	31.5
Atlas 200I DK A2	并行	92	65	41.2
RK3588	串行	20	85	28.6
RK3588	并行	35	96	34.8

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