Semantic Aggregation Research Articles

PDeT: A Progressive Deformable Transformer for Photovoltaic Panel Defect Segmentation

Defects in photovoltaic (PV) panels can significantly reduce the power generation efficiency of the system and may cause localized overheating due to uneven current distribution. Therefore, adopting precise pixel-level defect detection, i.e., defect segmentation, technology is essential to ensuring stable operation. However, for effective defect segmentation, the feature extractor must adaptively determine the appropriate scale or receptive field for accurate defect localization, while the decoder must seamlessly fuse coarse-level semantics with fine-grained features to enhance high-level representations. In this paper, we propose a Progressive Deformable Transformer (PDeT) for defect segmentation in PV cells. This approach effectively learns spatial sampling offsets and refines features progressively through coarse-level semantic attention. Specifically, the network adaptively captures spatial offset positions and computes self-attention, expanding the model’s receptive field and enabling feature extraction across objects of various shapes. Furthermore, we introduce a semantic aggregation module to refine semantic information, converting the fused feature map into a scale space and balancing contextual information. Extensive experiments demonstrate the effectiveness of our method, achieving an mIoU of 88.41% on our solar cell dataset, outperforming other methods. Additionally, to validate the PDeT’s applicability across different domains, we trained and tested it on the MVTec-AD dataset. The experimental results demonstrate that the PDeT exhibits excellent recognition performance in various other scenarios as well.

FMD-UNet: fine-grained feature squeeze and multiscale cascade dilated semantic aggregation dual-decoder UNet for COVID-19 lung infection segmentation from CT images

With the advancement of computer-aided diagnosis, the automatic segmentation of COVID-19 infection areas holds great promise for assisting in the timely diagnosis and recovery of patients in clinical practice. Currently, methods relying on U-Net face challenges in effectively utilizing fine-grained semantic information from input images and bridging the semantic gap between the encoder and decoder. To address these issues, we propose an FMD-UNet dual-decoder U-Net network for COVID-19 infection segmentation, which integrates a Fine-grained Feature Squeezing (FGFS) decoder and a Multi-scale Dilated Semantic Aggregation (MDSA) decoder. The FGFS decoder produces fine feature maps through the compression of fine-grained features and a weighted attention mechanism, guiding the model to capture detailed semantic information. The MDSA decoder consists of three hierarchical MDSA modules designed for different stages of input information. These modules progressively fuse different scales of dilated convolutions to process the shallow and deep semantic information from the encoder, and use the extracted feature information to bridge the semantic gaps at various stages, this design captures extensive contextual information while decoding and predicting segmentation, thereby suppressing the increase in model parameters. To better validate the robustness and generalizability of the FMD-UNet, we conducted comprehensive performance evaluations and ablation experiments on three public datasets, and achieved leading Dice Similarity Coefficient (DSC) scores of 84.76, 78.56 and 61.99% in COVID-19 infection segmentation, respectively. Compared to previous methods, the FMD-UNet has fewer parameters and shorter inference time, which also demonstrates its competitiveness.

SWFormer: A scale-wise hybrid CNN-Transformer network for multi-classes weed segmentation

Weeds in rapeseed field are an important factor in crop yield reduction and economic loss. Thus, Precision Agriculture is an important task for sustainable agriculture and weed management. At present, deep learning techniques have shown great potential for image-based detection and classification in various crops and weeds. However, the inherent limitations of traditional convolutional neural networks pose significant challenges due to the locally similarity of weeds and crops in color, shape and texture. To address this issue, we introduce SWFormer, a scale-wise hybrid CNN-Transformer network. SWFormer leverages the distinct strengths of both convolutional and transformer architectures. Convolutional structures excel at extracting short-range dependency information among pixels, whereas transformer structures are adept at capturing global dependency relationships. Additionally, we propose two innovative modules. Firstly, the Scale-wise Cascade Convolution (SWCC) module is designed to capture multiscale features and expand the receptive field. Secondly, the Adaptive Semantic Aggregation (ASA) module facilitates adaptive and effective information fusion across two distinct feature maps. Our experiments were conducted on the publicly available cropandweed dataset and SB20 dataset. it yields improved performance over other mainstream segmentation models. Specifically, SWFormer with 52.33M/527.51GFLOPs achieves an mAP of 76.54% and an accuracy of 83.95% on the cropandweed dataset. For the SB20 dataset, it attains an mAP of 61.24% and an accuracy of 79.47%. Overall, the evaluation clearly demonstrates our proposed SWFormer is conducive to promoting further research in the area of Precision Agriculture.

Precise facial landmark detection by Dynamic Semantic Aggregation Transformer

At present, deep neural network methods have played a dominant role in face alignment field. However, they generally use predefined network structures to predict landmarks, which tends to learn general features and leads to mediocre performance, e.g., they perform well on neutral samples but struggle with faces exhibiting large poses or occlusions. Moreover, they cannot effectively deal with semantic gaps and ambiguities among features at different scales, which may hinder them from learning efficient features. To address the above issues, in this paper, we propose a Dynamic Semantic-Aggregation Transformer (DSAT) for more discriminative and representative feature (i.e., specialized feature) learning. Specifically, a Dynamic Semantic-Aware (DSA) model is first proposed to partition samples into subsets and activate the specific pathways for them by estimating the semantic correlations of feature channels, making it possible to learn specialized features from each subset. Then, a novel Dynamic Semantic Specialization (DSS) model is designed to mine the homogeneous information from features at different scales for eliminating the semantic gap and ambiguities and enhancing the representation ability. Finally, by integrating the DSA model and DSS model into our proposed DSAT in both dynamic architecture and dynamic parameter manners, more specialized features can be learned for achieving more precise face alignment. It is interesting to show that harder samples can be handled by activating more feature channels. Extensive experiments on popular face alignment datasets demonstrate that our proposed DSAT outperforms state-of-the-art models in the literature. Our code is available at https://github.com/GERMINO-LiuHe/DSAT.

Automatic segmentation of spine x-ray images based on multiscale feature enhancement network.

Automatic segmentation of vertebrae in spinal x-ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions. However, due to the inherent characteristics of x-ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer-aided spine image analysis and disease diagnosis applications. In this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x-ray images, to aid doctors in diagnosing spinal-related diseases. To enhance feature extraction, the network incorporates a Dual-branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual-branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task-irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high-level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network's feature extraction capability. In this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People's Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x-ray images compared to other advanced methods, achieving 92.61±0.431 for MIoU, 92.42±0.329 for DSC, and 99.51±0.037 for Global_accuracy. Our model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions.

SaraNet: Semantic aggregation reverse attention network for pulmonary nodule segmentation

Accurate segmentation of pulmonary nodule is essential for subsequent pathological analysis and diagnosis. However, current U-Net architectures often rely on a simple skip connection scheme, leading to the fusion of feature maps with different semantic information, which can have a negative impact on the segmentation model. In response to this challenge, this study introduces a novel U-shaped model specifically designed for pulmonary nodule segmentation. The proposed model incorporates features such as the U-Net backbone, semantic aggregation feature pyramid module, and reverse attention module. The semantic aggregation module combines semantic information with multi-scale features, addressing the semantic gap between the encoder and decoder. The reverse attention module explores missing object parts and captures intricate details by erasing the currently predicted salient regions from side-output features. The proposed model is evaluated using the LIDC-IDRI dataset. Experimental results reveal that the proposed method achieves a dice similarity coefficient of 89.11%and a sensitivity of 90.73 %, outperforming state-of-the-art approaches comprehensively.

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

Cultivated land plays a fundamental role in the sustainable development of the world. Monitoring the non-agricultural changes is important for the development of land-use policies. A bitemporal image transformer (BIT) can achieve high accuracy for change detection (CD) tasks and also become a key scientific tool to support decision-making. Because of the diversity of high-resolution RSIs in series, the complexity of agricultural types, and the irregularity of hierarchical semantics in different types of changes, the accuracy of non-agricultural CD is far below the need for the management of the land and for resource planning. In this paper, we proposed a novel non-agricultural CD method to improve the accuracy of machine processing. First, multi-resource surveying data are collected to produce a well-tagged dataset with cultivated land and non-agricultural changes. Secondly, a hierarchical semantic aggregation mechanism and attention module (HSAA) bitemporal image transformer method named HSAA-CD is performed for non-agricultural CD in cultivated land. The proposed HSAA-CD added a hierarchical semantic aggregation mechanism for clustering the input data for U-Net as the backbone network and an attention module to improve the feature edge. Experiments were performed on the open-source LEVIR-CD and WHU Building-CD datasets as well as on the self-built RSI dataset. The F1-score, intersection over union (IoU), and overall accuracy (OA) of these three datasets were 88.56%, 84.29%, and 68.50%; 79.84%, 73.41%, and 59.29%; and 98.83%, 98.39%, and 93.56%, respectively. The results indicated that the proposed HSAA-CD method outperformed the BIT and some other state-of-the-art methods and proved to be suitable accuracy for non-agricultural CD in cultivated land.

Trash to Treasure: Low-Light Object Detection via Decomposition-and-Aggregation

Object detection in low-light scenarios has attracted much attention in the past few years. A mainstream and representative scheme introduces enhancers as the pre-processing for regular detectors. However, because of the disparity in task objectives between the enhancer and detector, this paradigm cannot shine at its best ability. In this work, we try to arouse the potential of enhancer + detector. Different from existing works, we extend the illumination-based enhancers (our newly designed or existing) as a scene decomposition module, whose removed illumination is exploited as the auxiliary in the detector for extracting detection-friendly features. A semantic aggregation module is further established for integrating multi-scale scene-related semantic information in the context space. Actually, our built scheme successfully transforms the "trash" (i.e., the ignored illumination in the detector) into the "treasure" for the detector. Plenty of experiments are conducted to reveal our superiority against other state-of-the-art methods. The code will be public if it is accepted.

Robust depth completion based on Semantic Aggregation

Robust depth completion based on Semantic Aggregation

Multi-axis interactive multidimensional attention network for vehicle re-identification

Learning fine-grained discriminative information is essential to address the challenges of small inter-class differences and large intra-class differences in vehicle re-identification (Re-ID). Attentional mechanism is often used to capture important global information in images rather than fine-grained discriminative information. Studies have shown that the multi-axis interaction of information can enhance the feature representation ability of networks. This paper explores how to use the multi-axis interaction of information to facilitate more effective learning of attention and how to capture important detailed information in local regions. We propose a multi-axis interactive multidimensional attention network (MIMA-Net) for vehicle Re-ID. The network allows information to interact on multiple axes and calibrates the weight distribution of features from multiple dimensions to learn subtle discriminative information in vehicle parts/regions. The window-channel attention module (W-CAM) in MIMA-Net facilitates the learning of channel attention by interacting first across locations and then across channels, while the channel group-spatial attention module (CG-SAM) facilitates the learning of spatial attention by interacting first across channels and then across locations. These two modules perform window partitioning in a priori manner and channel semantic aggregation in an adaptive manner to learn discriminative semantic features in parts, respectively. These two approaches complement each other to strengthen the feature representation ability of MIMA-Net. Extensive experiments on three large public datasets, VeRi-776, VehicleID, and VERI-Wild, verify the effectiveness of our MIMA-Net and show that our method achieves state-of-the-art performance.

Improvement of MQTT semantic to minimize data flow in IoT platforms based on distributed brokers

AbstractMessage queuing telemetry transport (MQTT) is an application layer protocol that enables effective device communication in the Internet of Things (IoT). MQTT operates according to a publish‐subscribe model, where a broker receives messages from the publishers and then forwards them to the subscribers. However, existing cloud‐based MQTT brokers lead to network bottlenecks due to the large number of devices that interact with them. Therefore, mist computing is involved, where the MQTT brokers are deployed closer to the IoT devices and the workload is distributed across multiple brokers. Nevertheless, the distributed mist‐based MQTT architecture introduces serious issues caused by the massive volume of flow exchanged by IoT devices. In this article, we introduce MQTT‐SD to address these issues. MQTT‐SD enhances MQTT protocol syntax with fusion semantic aggregation. It accomplishes MQTT topic fusion on the MQTT broker side with consideration of topics' distribution. We carried out extensive simulation and emulation to demonstrate MQTT‐SD efficacy. The results show that MQTT‐SD reduces MQTT flow and has the lowest traffic load compared to MQTT+ and MQTT‐MFA.

A novel transformer-based aggregation model for predicting gene mutations in lung adenocarcinoma.

In recent years, predicting gene mutations on whole slide imaging (WSI) has gained prominence. The primary challenge is extracting global information and achieving unbiased semantic aggregation. To address this challenge, we propose a novel Transformer-based aggregation model, employing a self-learning weight aggregation mechanism to mitigate semantic bias caused by the abundance of features in WSI. Additionally, we adopt a random patch training method, which enhances model learning richness by randomly extracting feature vectors from WSI, thus addressing the issue of limited data. To demonstrate the model's effectiveness in predicting gene mutations, we leverage the lung adenocarcinoma dataset from Shandong Provincial Hospital for prior knowledge learning. Subsequently, we assess TP53, CSMD3, LRP1B, and TTN gene mutations using lung adenocarcinoma tissue pathology images and clinical data from The Cancer Genome Atlas (TCGA). The results indicate a notable increase in the AUC (Area Under the ROC Curve) value, averaging 4%, attesting to the model's performance improvement. Our research offers an efficient model to explore the correlation between pathological image features and molecular characteristics in lung adenocarcinoma patients. This model introduces a novel approach to clinical genetic testing, expected to enhance the efficiency of identifying molecular features and genetic testing in lung adenocarcinoma patients, ultimately providing more accurate and reliable results for related studies.

Vision transformer: To discover the “four secrets” of image patches

Vision Transformer (ViT) is widely used in the field of computer vision, in ViT, there are four main steps, which are “four secrets”, such as patch division, token selection, position encoding addition, attention calculation, the existing research on transformer in computer vision mainly focuses on the above four steps. Therefore, “how to divide patch?”, “how to select token?”, “how to add position encoding?”, and “how to calculate attention?” are crucial to improve ViT performance. But so far, most of the review literatures are summarized from the perspective of application, and there is no corresponding literature to comprehensively summarize these four steps from the technology perspective, which restricts the further development of ViT in some degree. To address the above questions, the 4 major mechanisms and 5 applications of ViT are summarized in this paper, the main innovative works are as follows: Firstly, the basic principle and model structure of ViT are elaborated; Secondly, aiming to “how to divide patch?”, the 5 key techniques of patch division mechanism are summarized: from single-size division to multi-size division, from fixed number division to adaptive number division, from non-overlapping division to overlapping division, from semantic segmentation division to semantic aggregation division, and from original image division to feature map division; Thirdly, aiming to “how to select token?”, the 3 key techniques of token selection mechanism are summarized: token selection based on score, token selection based on merge, token selection based on convolution and pooling; Fourthly, aiming to “how to add position encoding?”, the 5 key techniques of position encoding mechanism are summarized: absolute position encoding, relative position encoding, conditional position encoding, locally-enhanced position encoding, and zero-padding position encoding; Fifthly, aiming to “how to calculate attention?”, 18 attention mechanisms are summarized based on the timeline; Sixthly, these models that Transformer is combined with U-Net, GAN, YOLO, ResNet, and DenseNet are discussed in the medical image processing field; Finally, around these four questions proposed in this paper, we look forward to the future development direction of frontier technologies such as patch division mechanism, token selection mechanism, position encoding mechanism, and attention mechanism et al, which play an important role in the further development of ViT.

Deep Matrix Factorization With Complementary Semantic Aggregation for Micro-Video Multi-Label Classification

Deep Matrix Factorization With Complementary Semantic Aggregation for Micro-Video Multi-Label Classification

Smoke-Aware Global-Interactive Non-local Network for Smoke Semantic Segmentation.

Compared with other objects, smoke semantic segmentation (SSS) is more difficult and challenging due to some special characteristics of smoke, such as non-rigid, translucency, variable mode and so on. To achieve accurate positioning of smoke in real complex scenes and promote the development of intelligent fire detection, we propose a Smoke-Aware Global-Interactive Non-local Network (SAGINN) for SSS, which harness the power of both convolution and transformer to capture local and global information simultaneously. Non-local is a powerful means for modeling long-range context dependencies, however, friendliness to single-scale low-resolution features limits its potential to produce high-quality representations. Therefore, we propose a Global-Interactive Non-local (GINL) module, leveraging global interaction between multi-scale key information to improve the robustness of feature representations. To solve the interference of smoke-like objects, a Pyramid High-level Semantic Aggregation (PHSA) module is designed, where the learned high-level category semantics from classification aids model by providing additional guidance to correct the wrong information in segmentation representations at the image level and alleviate the inter-class similarity problem. Besides, we further propose a novel loss function, termed Smoke-aware loss (SAL), by assigning different weights to different objects contingent on their importance. We evaluate our SAGINN on extensive synthetic and real data to verify its generalization ability. Experimental results show that SAGINN achieves 83% average mIoU on the three testing datasets (83.33%, 82.72% and 82.94%) of SYN70K with an accuracy improvement of about 0.5%, 0.002 mMse and 0.805 Fβ on SMOKE5K, which can obtain more accurate location and finer boundaries of smoke, achieving satisfactory results on smoke-like objects.

Drug repositioning via Multi-view Representation Learning with Heterogeneous Graph Neural Network.

Exploring simple and efficient computational methods for drug repositioning has emerged as a popular and compelling topic in the realm of comprehensive drug development. The crux of this technology lies in identifying potential drug-disease associations, which can effectively mitigate the burdens caused by the exorbitant costs and lengthy periods of conventional drugs development. However, current computational drug repositioning methods face challenges in accurately predicting drug-disease associations. These challenges include only considering drugs and diseases to construct a heterogeneous graph without including other biological nodes associated with the disease or drug for a more comprehensive heterogeneous graph, as well as not fully utilizing the local structure of heterogeneous graphs and rich semantic features. To address these problems, we propose a Multi-view Representation Learning method (MRLHGNN) with Heterogeneous Graph Neural Network for drug repositioning. This method is based on a collection of data from multiple biological entities associated with drugs or diseases. It consists of a view-specific feature aggregation module with meta-paths and auto multi-view fusion encoder. To better utilize local structural and semantic information from specific views in heterogeneous graph, MRLHGNN employs a feature aggregation model with variable-length meta-paths to expand the local receptive field. Additionally, it utilizes a transformerbased semantic aggregation module to aggregate semantic features across different view-specific graphs. Finally, potential drug-disease associations are obtained through a multi-view fusion decoder with an attention mechanism. Cross-validation experiments demonstrate the effectiveness and interpretability of the MRLHGNN in comparison to nine state-of-the-art approaches. Case studies further reveal that MRLHGNN can serve as a powerful tool for drug repositioning.

D-SAT: dual semantic aggregation transformer with dual attention for medical image segmentation

Objective. Medical image segmentation is significantly essential to assist clinicians in facilitating a quick and accurate diagnoses. However, most of the existing methods are still challenged by the loss of semantic information, blurred boundaries and the huge semantic gap between the encoder and decoder. Approach. To tackle these issues, a dual semantic aggregation transformer with dual attention is proposed for medical image segmentation. Firstly, the dual-semantic feature aggregation module is designed to build a bridge between convolutional neural network (CNN) and Transformer, effectively aggregating CNN’s local feature detail ability and Transformer’s long-range modeling ability to mitigate semantic information loss. Thereafter, the strip spatial attention mechanism is put forward to alleviate the blurred boundaries during encoding by constructing pixel-level feature relations across CSWin Transformer blocks from different spatial dimensions. Finally, a feature distribution gated attention module is constructed in the skip connection between the encoder and decoder to decrease the large semantic gap by filtering out the noise in low-level semantic information when fusing low-level and high-level semantic features during decoding. Main results. Comprehensive experiments conducted on abdominal multi-organ segmentation, cardiac diagnosis, polyp segmentation and skin lesion segmentation serve to validate the generalization and effectiveness of the proposed dual semantic aggregation transformer with dual attention (D-SAT). The superiority of D-SAT over current state-of-the-art methods is substantiated by both subjective and objective evaluations, revealing its remarkable performance in terms of segmentation accuracy and quality. Significance. The proposed method subtly preserves the local feature details and global context information in medical image segmentation, providing valuable support to improve diagnostic efficiency for clinicians and early disease control for patients. Code is available at https://github.com/Dxkm/D-SAT.

Frequency-aware robust multidimensional information fusion framework for remote sensing image segmentation

Urban scene image segmentation is an important research area in high-resolution remote sensing image processing. However, due to its complex three-dimensional structure, interference factors such as occlusion, shadow, intra-class inconsistency, and inter-class indistinction affect segmentation performance. Many methods have combined local and global information using CNNs and Transformers to achieve high performance in remote sensing image segmentation tasks. However, these methods are not stable when dealing with these interference factors. Recent studies have found that semantic segmentation is highly sensitive to frequency information, so we introduced frequency information to make the model learn more comprehensively about different categories of targets from multiple dimensions. By modeling the target with local features, global information, and frequency information, the target features can be learned in multiple dimensions to reduce the impact of interference factors on the model and improve its robustness. In this paper, we consider frequency information in addition to combining CNNs and Transformers for modeling and propose a Multidimensional Information Fusion Network (MIFNet) for high-resolution remote sensing image segmentation of urban scenes. Specifically, we design an information fusion Transformer module that can adaptively associate local features, global semantic information, and frequency information and a relevant semantic aggregation module for aggregating features at different scales to construct the decoder. By aggregating image features at different depths, the specific representation of the target and the correlation between targets can be modeled in multiple dimensions, allowing the network to better recognize and understand the features of each class of targets to resist various interference factors that affect segmentation performance. We conducted extensive ablation experiments and comparative experiments on the ISPRS Vaihingen and ISPRS Potsdam benchmarks to verify our proposed method. In a large number of experiments, our method achieved the best results, with 84.53% and 87.3% mIoU scores on the Vaihingen and Potsdam datasets, respectively, proving the superiority of our method. The source code will be available at https://github.com/JunyuFan/MIFNet.

Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization

Domain generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. The existing DG methods usually exploit the fusion of shared multi-source data to train a generalizable model. However, tremendous data is distributed across lots of places nowadays that can not be shared due to privacy policies. In this paper, we tackle the problem of federated domain generalization where the source datasets can only be accessed and learned locally for privacy protection. We propose a novel framework called Collaborative Semantic Aggregation and Calibration (CSAC) to enable this challenging problem. To fully absorb multi-source semantic information while avoiding unsafe data fusion, we conduct data-free semantic aggregation by fusing the models trained on the separated domains layer-by-layer. To address the semantic dislocation problem caused by domain shift, we further design cross-layer semantic calibration with an attention mechanism to align each semantic level and enhance domain invariance. We unify multi-source semantic learning and alignment in a collaborative way by repeating the semantic aggregation and calibration alternately, keeping each dataset localized, and the data privacy is carefully protected. Extensive experiments show the significant performance of our method in addressing this challenging problem.

AMCFNet: Asymmetric multiscale and crossmodal fusion network for RGB-D semantic segmentation in indoor service robots

Red-green-blue and depth (RGB-D) semantic segmentation is essential for indoor service robots to achieve accurate artificial intelligence. Various RGB-D indoor semantic segmentation methods have been proposed since the widespread adoption of depth maps. These methods have focused mainly on integrating the multiscale and crossmodal features extracted from RGB images and depth maps in the encoder and used unified strategies to recover the local details at the decoder progressively. However, these methods emphasized crossmodal fusion at the encoder, neglecting the distinguishability between RGB and depth features during decoding, thereby undermining the segmentation performance. To adequately exploit the features, we propose an efficient encoder-decoder architecture called asymmetric multiscale and crossmodal fusion network (AMCFNet) endowed with a differential feature integration strategy. Unlike existing methods, we use simple crossmodal fusion at the encoder and design an elaborate decoder to improve the semantic segmentation performance. Specifically, considering high- and low-level features, we propose a semantic aggregation module (SAM) to process the multiscale and crossmodal features in the last three network layers to aggregate high-level semantic information through a cascaded pyramid structure. Moreover, we design a spatial detail supplement module using low-level spatial details from depth maps to adaptively fuse these details and the information obtained from the SAM. Extensive experiments are conducted to demonstrate that the proposed AMCFNet outperforms state-of-the-art approaches.