With the rapid advancement of 3D sensing technologies such as LiDAR （light detection and ranging） and depth cameras， large-scale 3D point clouds have emerged as a crucial data source for a wide range of applications， including autonomous driving， robotic navigation， augmented reality， and urban scene reconstruction. Compared to 2D images， point clouds offer precise spatial geometry and provide a comprehensive representation of the environment without perspective distortion. Additionally， they are robust to variations in lighting and texture. Point cloud segmentation plays a crucial role in scene analysis and interpretation. The segmentation can be categorized into three types： semantic segmentation， instance segmentation， and joint semantic-instance segmentation. Semantic segmentation partitions a 3D scene into informative regions and assigns each region to a specific class. Instance segmentation identifies and separates individual objects at the point level， including those that belong to the same semantic category. In recent years， researchers have increasingly focused on combining the two tasks to achieve more consistent and informative scene-level interpretations. Joint semantic-instance segmentation leverages the intrinsic correlation between semantic and instance-level segmentation， enabling the two tasks to complement and reinforce each other. In 3D point cloud contexts， this joint approach substantially improves the capability of the system to comprehend complex environments and offers strong technical support for the development of intelligent systems. Consequently， this approach has become an area of growing interest and active research. However， most existing methods for joint semantic-instance segmentation rely on simplistic feature fusion strategies， which limit their effectiveness in fully capturing the potential relationship between semantic and instance features. Aiming to address this limitation， an enhanced attention-based joint semantic-instance segmentation network is proposed. This network is designed to effectively model and utilize the correlation between semantic and instance information.

The enhanced attention-based joint semantic-instance segmentation neural network （EAJS-Net） incorporates a semantic feature extraction module based on an attention mechanism. This module focuses on the local neighborhood of each point and dynamically adjusts attention weights to emphasize key information， thereby enhancing the extraction of semantic features across points. Additionally， an attention-enhanced semantic/instance feature fusion module is introduced， which adaptively learns the similarity between central and adjacent features. This design reinforces key characteristics and effectively captures the correlation between instance and semantic segmentation， ultimately improving overall segmentation accuracy. EAJS-Net integrates PointNet++ and PointConv as its backbone network and comprises three main components： a point feature enhancement module， an encoder-decoder module， and an enhanced attention-based joint segmentation module. The input to EAJS-Net includes N × 9 dimensional point cloud data， where N represents the number of points， and the nine dimensions include coordinate values （XYZ）， color information （RGB）， and normalized coordinates. A semantic feature extraction module based on an attention mechanism is employed to effectively capture local contextual information between points. The enhanced features extracted by this module are then fed into the encoding layer， which includes four encoding modules： one attention pooling-based set abstraction layer adapted from PointNet++ and three feature encoding layers derived from PointConv. The corresponding decoding layer comprises four decoding modules： three deep feature decoding layers derived from PointConv and one feature propagation layer from PointNet++. By utilizing the attention pooling-based set abstraction layer from PointNet++， the network effectively captures spatial geometric relationships among features. Through the combination of the encoding and decoding layers， the initial semantic and instance features of the point cloud are extracted， laying the foundation for accurate joint segmentation. An enhanced attention module is designed to adaptively learn the similarity between central and neighboring features through dual attention mechanisms， which dynamically compute attention weights. These dual attention weights are summed and applied to the initial semantic features， resulting in enhanced semantic representations. This module is embedded within the semantic branch of the joint segmentation module， enabling more effective integration of semantic and instance features to improve joint segmentation accuracy. The encoded features are then upsampled through two parallel decoder branches to generate an instance feature matrix and a semantic feature matrix， which serve as inputs to the joint segmentation module. Within this module， the semantic and instance branches are integrated using the enhanced attention mechanism. The final output comprises instance embeddings and semantic predictions， supporting precise and consistent segmentation results.

The proposed network is evaluated on the Stanford large-scale 3D indoor spaces （S3DIS） dataset and ScanNet V2 to assess its performance on point cloud segmentation tasks. Six fold cross-validation is performed on the S3DIS dataset， and the results of EAJS-Net are compared with those of the state-of-the-art （SOTA） methods. For semantic segmentation on the S3DIS dataset， EAJS-Net achieves a mean intersection over union （mIoU） of 65.9%， overall accuracy （oAcc） of 89.1%， and mean accuracy （mAcc） of 76.0%. Compared to JSNet++， these results represent improvements of 3.5% （mIoU）， 0.4% （oAcc）， and 3.2% （mAcc）. For instance segmentation， EAJS-Net reaches a weighted coverage rate of 61.1%， outperforming JSNet++ by 4.1% （mean weighted coverage， mWCov）， 4.6% （mean coverage， mCov）， and 1.2% （mean recall， mRec）. On the ScanNet dataset， EAJS-Net improves the mIoU for semantic segmentation by 3.2% and increases the weighted coverage rate for instance segmentation by 2.8% compared to JSNet. Visual comparisons between EAJS-Net and other SOTA methods are also presented， demonstrating that EAJS-Net consistently achieves superior segmentation results， even in complex indoor scenes. In addition， ablation experiments are conducted to validate the effectiveness of individual modules within the network. The enhanced attention-based joint segmentation module in EAJS-Net dynamically adjusts attention weights to effectively capture various features， successfully integrating semantic and instance features into the semantic feature space. This integration notably enhances the performance of the semantic segmentation task.

Aiming to address the limitations of existing feature fusion strategies that fail to fully capture inter-instance semantic correlations， this paper proposes a novel semantic-instance joint segmentation network， EAJS-Net， based on an enhanced attention mechanism. A new semantic feature extraction module is designed to capture contextual relationships among points. Additionally， an enhanced attention module is introduced to effectively aggregate instance features into the semantic feature space. This improved feature fusion strategy boosts the performance of joint semantic-instance segmentation. Experimental results demonstrate that EAJS-Net effectively integrates semantic and instance features， substantially improving the accuracy of both segmentation tasks compared to SOTA methods.

Human pose estimation aims to locate skeletal keypoints of individuals in a given image. As a fundamental task in computer vision， human pose estimation has wide applications in human activity recognition， person re-identification， pose tracking， and related fields. Two main approaches for human pose estimation are available： top-down and bottom-up. Top-down methods first detect human bodies in the image， crop out each person， and then estimate the keypoint coordinates. While effective， these methods perform poorly in cases of occlusion， and their computation cost increases with the number of people in the image. In contrast， bottom-up methods detect all identity-independent keypoints simultaneously and then group them into individual poses. These methods are typically lightweight and fast but must handle varying human scales. Bottom-up human pose estimation methods commonly use 2D Gaussian kernels to generate keypoint heatmaps as regression targets because they provide rich spatial information. However， conventional approaches apply Gaussian kernels with a fixed variance across all keypoints， resulting in uniform heatmap structures. This uniformity is problematic given the existing scale variability in bottom-up methods. On the one hand， different keypoints cover different pixel areas in images， and using large Gaussian kernels may introduce semantic ambiguity， particularly for small joints. On the other hand， differences in keypoint scale imply different levels of annotation uncertainty， which the heatmap variance should ideally reflect. The variance of the Gaussian kernel represents uncertainty； thus， it should be proportional to the scale and ambiguity associated with each keypoint. Aiming to address these issues， an adaptive heatmap generation network （AHGNet） for bottom-up human pose estimation is proposed. AHGNet estimates the appropriate radius of the Gaussian kernel for each keypoint by integrating inherent scale information and geometric relationships. Through formula derivation， the relationship between the radius and the Gaussian kernel variance is established， enabling the creation of customized， scale-adaptive ground-truth heatmaps. This approach improves localization accuracy by effectively aligning the heatmap structure with the spatial characteristics of each keypoint.

First， an adaptive heatmap generation module is introduced. This module combines the inherent scale information from image features and the geometric relationship between adjacent keypoints to constrain the coverage areas of kernels. Keypoint scale is defined by semantic coverage areas in images. However， in the actual scene， accurately allowing pixel areas to occupy keypoints is almost impossible， and determining the potential relationship between Gaussian kernels and coverage areas is difficult. Interestingly， the areas occupied by keypoints are found to be related to geometric distance from adjacent keypoints. Therefore， an adaptive heatmap generation module is introduced to generate kernel scale maps of keypoints. This module combine the geometric relationship between adjacent keypoints and inherent scale information from image features. Second， local probabilistic consistency loss is presented to define the distance between the predicted and ground truth heatmaps globally and locally. Most methods based on heatmap regression use L₂ loss for supervised learning. However， as the loss function for heatmap regression， L₂ loss assumes that each pixel point is independent and overlooks the local structural correlation， making it difficult to describe the probability distribution of heatmaps. A keypoint heatmap is a probability distribution that describes pixels belonging to a certain joint. Thus， KL Divergence must be added to describe local probability consistency. Moreover， samples with large prediction errors are difficult to predict； thus， the weight of difficult samples should be increased. Similarly， the weight of easily detected samples should be reduced. Therefore， the dynamic weight is added to balance the contribution of different samples. Inspired by focal loss， which allows the model to actively focus on hard-to-detect samples， this paper utilizes dynamic weights to reduce the contribution of easily detected samples while enhancing the contribution of hard-to-detect samples.

HrHRNet is used as the baseline to establish AHGNet for bottom-up human pose estimation. The model is tested on two public datasets： MS COCO and CrowdPose. Experimental results reveal that AHGNet surpasses HrHRNet in terms of average precision （AP）， achieving 72.1% AP and 74.1% AP on COCO test-dev and CrowdPose dataset， providing improvements of +1.6% AP and +6.5% AP， respectively. In addition， the substantial improvement on the CrowdPose dataset with crowded scenes indicates that AHGNet helps alleviate the problem of human scale changes in complex crowded scenes. Simultaneously， the ablation experiments verified the effectiveness of the proposed method.

AHGNet leverages geometric features between adjacent keypoints and inherent scale information within the image to generate adaptive heatmaps as groundtruth. This network further employs a local probability consistency loss function to address the challenges posed by various human scales， effectively improving the accuracy of bottom-up human pose estimation. AHGNet provides a new paradigm for optimizing supervision signals in bottom-up pose estimation. By dynamically adjusting the Gaussian kernel scale and enforcing local probability constraints， it effectively reduces multiscale ambiguity in complex scenarios.

3D model classification is a fundamental problem in the fields of computer graphics and computer vision， with wide-ranging applications in areas such as computer-aided design， mixed reality， autonomous driving， and robotic navigation. The challenges associated with 3D model classification primarily arise from three key aspects： the difficulty in representing 3D surface geometric features， the diversity of 3D transformations and deformations， and the incompleteness of geometric and topological structures. Existing multi-view-based 3D model classification methods typically render 3D models from multiple preset viewpoints and input all rendered views into a neural network for classification. However， due to the presence of redundant and ineffective views， not all views contribute equally to the classification task. Selecting views that substantially enhance classification performance can not only improve the overall accuracy of multi-view 3D model classification but also help identify representative views that effectively capture the essential characteristics of the 3D model.

This paper proposes a Transformer attention-guided approach for optimal view selection and classification of 3D models. The 3D model is first rendered from 20 viewpoints arranged on a regular icosahedron. A convolutional neural network is then employed to extract feature information from these multiple views， producing a sequence of local multi-view feature tokens. Aiming to retain spatial location information， position encoding is applied to the token sequence. Next， a learnable global classification token is introduced and concatenated with the multi-view feature tokens， forming the input to a Transformer encoder that performs global view feature fusion and generates an initial global classification feature. Subsequently， the optimal view selection module calculates the contribution of each view to the initial global classification token using the attention score matrix from the feature fusion process. The highest-scoring views are selected as the optimal views. These optimal view feature tokens are then concatenated with the initial global classification token and input into the Transformer encoder for a second round of feature fusion， producing the final global classification token. This final token is passed through a classifier to generate the classification probabilities and simultaneously output the selected optimal views. Aiming to enhance generalization during training， the model incorporates random view dropping and contrastive learning strategies.

This study experiments on the ModelNet40 dataset， which comprises 40 object categories. The dataset is suitable for research in 3D object recognition and is widely used for benchmarking algorithm performance. Evaluation metrics include overall accuracy （OA）， average accuracy （AA）， and speed. OA measures classification accuracy across the entire dataset， while AA calculates the mean accuracy across all categories， addressing issues related to class imbalance. The dataset， created by Stanford University， is widely used for performance evaluation of algorithms. First， the Transformer-based multi-view selection and 3D model classification method proposed in this paper are compared with other state-of-the-art deep learning-based 3D model classification methods to validate its effectiveness. Subsequently， ablation experiments are conducted to analyze the impact of different parameter settings on the performance of the proposed method， including multi-view representation， feature extraction backbone， Transformer hidden layer dimension， number of attention heads， contrastive learning strategy， and random view dropout module. On the ModelNet40 benchmark dataset， the proposed method achieves an overall recognition accuracy of 97.61% and an average recognition accuracy of 96.36%. In addition to reaching state-of-the-art classification performance， the optimal views selected based on the Transformer attention score matrix are shown to be highly representative.

The proposed method leverages the Transformer architecture to perform feature fusion across different views. By employing mechanisms such as self-attention， residual connections， and multi-layer stacking， the Transformer effectively learns complex features and captures global contextual relationships among different views. Furthermore， the attention score matrix generated by the Transformer serves as a basis for optimal view selection， enabling efficient classification while identifying the most representative views.

Weakly supervised semantic segmentation （WSSS） aims to reduce the cost associated with annotating “strong” pixel-level labels by using “weak” labels， such as points， bounding boxes， image-level class labels， and scribbles. Among these， image-level class labels are the most cost-effective and readily available； however， leveraging them for precise segmentation remains a considerable challenge. A widely used WSSS approach based on image-level class labels generally comprises the following steps： 1） training a neural network for image classification using the class labels； 2） using the trained network to generate class activation maps （CAMs）， which serve as seed regions for the segmentation task； and 3） refining these CAMs into pseudo-labels， which are then used as the ground truth to supervise a segmentation network. These steps can be integrated into a single collaborative stage； typically， single-stage frameworks are highly efficient due to their simplified training pipeline. However， the quality of pseudo-labels is crucial to the overall performance of semantic segmentation. High-quality pseudo-labels result in superior segmentation outcomes， whereas noisy or inaccurate pseudo-labels hinder the capability of the model to learn meaningful features. WSSS based on image-level labels faces considerable challenges due to the absence of precise positional and shape-related information， making it difficult to generate accurate segmentation maps. These challenges have led to the development of various approaches， which can be broadly categorized into two types： single-stage methods and multistage methods. Although single-stage methods offer greater efficiency and simplify the overall training process， they often produce less accurate pseudo-labels. This condition is due to the limited refinement of CAMs， resulting in imprecise supervision signals that ultimately degrade segmentation performance. Aiming to alleviate these limitations， a simple yet novel single-stage WSSS framework that incorporates knowledge distillation is introduced to enhance pseudo-label quality without relying on any additional external supervision. The framework enhances the feature learning process within the teacher-student network using a dual-stage knowledge distillation module. This module allows the student network to acquire more dynamic and informative knowledge from the teacher network while preserving key features， thereby enhancing the overall robustness of the student model. Moreover， to further improve segmentation accuracy， a pseudo-label correction module based on a Gaussian mixture model （GMM） is introduced. This module refines the pseudo-labels by modeling the distribution of the CAMs， resulting in highly accurate and reliable supervision signals. The combination of dual-stage knowledge distillation and the Gaussian correction module ensures accurate learning and improved segmentation results， even under weak supervision signals such as image-level labels. Ultimately， the proposed method effectively mitigates the impact of noise during training and enhances the accuracy of the generated pseudo-labels， resulting in superior semantic segmentation outcomes in WSSS tasks.

A novel weakly-supervised semantic segmentation method， aimed at addressing the challenges posed by noisy data points and weak supervision， is proposed. First， a dual-stage knowledge interaction module is introduced to enhance the feature learning process of the teacher and student networks. By enabling highly effective knowledge exchange between the two networks， the proposed approach notably reduces the impact of noise during training， leading to robust feature extraction. Additionally， a Gaussian correction module is proposed to enhance the quality of pseudo-labels. This module refines the pseudo-labels by modeling the distribution of class activation maps. By fitting the distribution more accurately， the module corrects potential errors in the pseudo-labels， ensuring that the model learns from high-quality， refined labels. Therefore， the method boosts the overall performance of weakly-supervised semantic segmentation， making it more robust to noise and improving segmentation accuracy. This method provides a promising solution for weakly-supervised segmentation tasks.

The mIoU values of this method on the PASCAL VOC 2012 and MS COCO 2014 datasets were 74.8% and 42.3%， respectively， surpassing other comparative methods. Specifically， on the PASCAL VOC 2012 dataset， the proposed method achieved a 3.7% improvement over ToCo， an 8.8% enhancement compared to AFA， a 7.5% increase relative to TSCD， and 1.1% compared to BECO. On the MS COCO 2014 dataset， the method improved performance by 2.2% compared to TSCD， 3.4% compared to AFA， and 5.3% compared to AuxSegNet+. Additionally， the mIoU values of different categories are compared on the PASCAL VOC 2012 validation set. The experimental results showed that the method outperformed the competing methods in 16 categories. Notably， for the background class， the method achieved an mIoU of 92.4%， the highest among all methods evaluated. This result indicates that the method effectively leverages the Gaussian correction module to reduce misclassification of background regions， thereby improving segmentation performance. Furthermore， the method achieved notable improvements in categories such as bird， bottle， car， chair， and cow， further demonstrating its effectiveness.

The proposed method effectively mitigates the impact of noise during training and address the issue of incomplete pseudo-label generation through the integration of a dual-stage knowledge distillation module and a Gaussian correction module. This approach achieves remarkable performance improvements compared to existing methods. Overall， the results demonstrate notable advantages in end-to-end weakly supervised semantic segmentation and holds considerable research value.

Pedestrian trajectory prediction constitutes a critical research challenge in autonomous driving systems， intelligent security surveillance， and human-robot interaction frameworks. The capability to accurately anticipate pedestrian movement patterns directly influences the operational safety of autonomous vehicles， the responsiveness of surveillance systems， and the adaptability of social robots in dynamic environments. While existing approaches predominantly focus on leveraging sequential data patterns and optimizing model architectures through recurrent neural networks， they often overlook the intrinsic social-semantic characteristics embedded in real-world pedestrian interactions. Current methodologies tend to treat trajectory prediction as a purely sequential modeling task， overlooking three fundamental aspects： 1） the social constraints governing crowd movement patterns， 2） the intentional， destination-oriented nature of human locomotion， and 3） the dynamic adaptation mechanisms pedestrians employ during path navigation. This oversight leads to suboptimal performance， particularly in dense pedestrian scenarios where social interactions and environmental adaptability notably influence movement decisions. Aiming to address these limitations， this paper proposes path stepwise estimation network （PSEN）， a novel framework that systematically integrates social relationship modeling， endpoint-aware trajectory planning， and environment-adaptive path refinement. The proposed model bridges the gap between conventional sequence prediction paradigms and the complex socio-spatial dynamics inherent in real-world pedestrian navigation scenarios.

This paper incorporates the characteristics of path planning observed in daily human walking， which can be broadly divided into three key aspects. First， social restrictions are considered. The crowd is categorized based on movement direction， speed， and distance to demonstrate these reflections. Intra-class feature learning is then performed on the classified groups. The social relationships between predicted pedestrians and other pedestrians are calculated using social weights to obtain social attention， which affects the subsequent path estimation network. Second， an endpoint estimation network is introduced by stimulating the feature that pedestrians typically identify a destination and then purposefully plan their walking path. This network leverages the strengths of serialized prediction tasks by using spatiotemporal sequences to predict an endpoint. The estimated endpoint serves as a reference condition within the overall network model， guiding the complete path planning process. Third， this paper address the fact that pedestrians constantly fine-tune their local paths and adjust their focus based on environmental context and destination. Aiming to model this behavior， an endpoint and path fine-tuning network is constructed using conditional variational autoencoder （CVAE） and multilayer perceptron （MLP）. This module takes the output of the endpoint estimation network as a condition and uses the output from the social restriction module， along with the historical trajectory， as inputs for feature learning. After every three frames of prediction， the social restriction and endpoint module outputs are updated according to the current environment of the pedestrians. This update allows the model to automatically fine-tune the planned path in response to dynamic surroundings.

The experiments are conducted by comparing the proposed method with six baseline methods on the ETH/UCY dataset， five baseline methods on the SDD dataset， and four baseline methods on the NBA SportVU dataset. The evaluation metrics used are average displacement error （ADE） and final displacement error （FDE）. On the entire ETH/UCY dataset， ADE and FDE are reduced by an average of 5.1% and 7.5%， respectively. On the SDD dataset， reductions of 1% in ADE and 2% in FDE are observed on average. When analyzing individual datasets， the performance improvements are highly pronounced in scenarios with denser pedestrian traffic. Notably， in the ZARA1， ZARA2， and UNIV datasets， the proposed method achieves improvements of over 10% in prediction accuracy. Ablation experiments are also conducted on the ETH/UCY dataset to evaluate the contributions of individual components of the PSEN framework. The experimental results demonstrate that each module of PSEN notably improves the effectiveness of pedestrian trajectory prediction， achieving average reductions of 19% and 31% in ADE and final displacement error FDE， respectively. Ablation experiments are performed in parameters such as social distance， social attention weights， and the number of frames used in stepwise trajectory generation. These experiments confirm that all aspects of the network design positively impact pedestrian trajectory prediction. However， the model does not perform as well on the NBAsportVU dataset. This dataset is characterized by 10 players moving at high speeds， with trajectory endpoints changing dynamically based on in-game situations and players’ intentions. Different from ETH/UCY and SDD datasets， where movement is predictable and socially constrained， the varying roles and tactical decisions of agent in NBA dataset play a crucial role in path planning， making prediction highly challenging. Therefore， achieving accurate predictions by relying solely on time-position information is difficult because the characteristics of pedestrians in this setting notably differ from those in typical pedestrian scenes. In sports scenes， athletes actively seek collisions and obstructions as part of their strategic movement. PSEN does not consider the role-specific behaviors of agents， limiting its effectiveness in such environments.

The PSEN model proposed in this paper integrates the serialization task with three key features of real-world pedestrian scenes. By combining recurrent neural networks with a CVAE， PSEN effectively reflects the complex features of pedestrian trajectory prediction in realistic scenarios. The model achieves superior performance on the ETH/UCY and SDD datasets， providing a new direction for subsequent tasks in pedestrian trajectory prediction. However， this study focuses only on interactions among pedestrians and does not consider the relationship between pedestrians and other objects， such as vehicles and obstacles. In novel environments， or in scenes where pedestrians are sparse but other dynamic or static objects are abundant， the performance of the model may degrade. Further research is needed in terms of the relationships between pedestrians and objects， along with their associated feature information.

With the advancement of image processing and artificial intelligence， deep learning-based algorithms have become increasingly important in the tasks of image target detection and recognition. In the aerospace domain， satellite remote sensing object detection consistently confronts challenges， including cluttered imaging backgrounds， numerous minuscule targets， and wide dynamic imaging ranges. In recent years， convolutional neural network-based approaches have witnessed significant progress in satellite remote sensing object detection， particularly in fine-grained target recognition. These advancements play crucial roles across domains such as military reconnaissance， postdisaster reconstruction， and resource exploration. Given the challenges of large coverage， small and dense targets， and complex imaging backgrounds in satellite-based remote sensing images， large and complex neural networks have been utilized to represent image features for further target detection. Although large neural networks exhibit certain detection capabilities， they are difficult to deploy in space-based remote sensing tasks because of the high real-time requirements and limited computing resources. To address these issues， this study proposes a lightweight space-based remote sensing image target detection algorithm that integrates multiattention mechanisms in the spatial domain and channels. It deploys remote sensing image data processing and target detection algorithms to a remote sensing edge intelligent computing platform， achieving efficient and accurate target recognition and analysis for remote sensing images. This approach provides a solution for future in-orbit fast target detection algorithm processing and real-time tracking of detection targets.

Based on a You Only Look Once version 11 model （i.e.， YOLOv11n）， the proposed algorithm integrates the channel prior convolutional attention （CPCA） mechanism， which combines channel and spatial attention mechanisms. It utilizes the channel attention mechanism to generate a channel attention map. Subsequently， this map is multiplied element-wise with the model’s input feature map to produce a channel-weighted feature map. This channel-weighted feature map is then fed into a depthwise convolution module to generate a spatial attention feature map. The CPCA mechanism can dynamically allocate attention weights across channel and spatial dimensions， enriching the network’s target features by extracting channel-wise and spatial attention features， thereby enhancing the network’s feature extraction capability. By employing a 2D convolutional layer based on partial convolution （Pconv）， which convolves only a subset of input channels， it leverages redundant compression in interchannel feature maps. This approach avoids the issue of excessive parameters typically introduced by adding attention modules. Consequently， the improved model reduces the parameter count by 0.48 M （approximately 18.53%） compared with the original YOLOv11n. This approach partially addresses the challenge of deploying network models on embedded devices. For ensuring consistent dimensions between the two branches of Pconv， a max-pooling operation is applied to the nonconvolved channels， downsizing the feature maps to half their original dimensions. Through leveraging pointwise convolution to fully utilize the representational capacity of channel-wise features， this design reduces the computational load while preventing significant degradation in the model’s feature extraction capability.

During validation on the DIOR dataset， the proposed algorithm was compared with various YOLO algorithms for object detection. Experimental results demonstrate that real-time detection transformer（RTDETR） has the largest parameter count at 9.42 M， YOLOv11n has 2.59 M parameters， and YOLOv11n_CBAM has 2.74 M. By contrast， the proposed model contains only 2.11 M parameters， accounting for 81.47% of those of the original YOLOv11n. Meanwhile， compared with the original YOLOv11n algorithm， the proposed method achieves a mean improvement of 1.9% in accuracy and 1.2% in recall. The neural network processing unit （NPU） inference latency of YOLOv11n is 19.6 ms， whereas the proposed algorithm achieves only 14.8 ms. This result indicates a reduction of 4.8 ms in comparison with the original model， representing a 24.49% speed improvement. Additionally， the NPU-deployed YOLOv11n model attains an accuracy of 0.799 and a recall of 0.642， whereas the proposed algorithm achieves 0.819 accuracy and 0.652 recall. Accordingly， no potential accuracy degradation occurs during model migration and deployment. Compared with merely adding the CPCA module， the proposed algorithm exhibits a slight accuracy decrease of 0.10% but reduces the parameter count by 0.66 M. When contrasted with solely incorporating the Pconv module， it shows a marginal parameter increase of 0.08 M， yet it improves the accuracy by 1.7%.

Targeting space-based remote sensing minute object detection tasks， this study draws inspiration from the YOLOv11n model to propose a lightweight object detection algorithm that integrates multiattention mechanisms in the spatial domain and channels and contextual information. This approach significantly enhances detection accuracy while effectively reducing model parameters. By refining the attention mechanism in YOLOv11n， we introduce an improved architecture incorporating the CPCA module. This architecture enables comprehensive feature extraction for minute objects across spatial and channel dimensions， effectively mitigating missed detections and false alarms in spaceborne imagery. The conventional 2D convolutional layers in YOLO are replaced with Pconv-based designs， circumventing parameter inflation typically caused by attention modules. This replacement achieves an 18.53% parameter reduction and model lightweighting. Finally， through NPU-optimized deployment， the model’s hardware compatibility is enhanced. Compared with the original YOLOv11n， the proposed algorithm reduces inference time by 4.8 ms while maintaining detection accuracy， meeting real-time monitoring requirements. The solution proves exceptionally resource efficient for space-based engineering deployment with constrained computational resources and memory， providing crucial technical support for onboard implementation in spaceborne remote sensing systems.

Facial age estimation from images constitutes a prominent area of research within the field of computer vision， offering extensive potential applications in fields such as biometrics， digital marketing， healthcare， and human-computer interaction. Despite substantial efforts by numerous researchers in this field， achieving accurate facial age estimation remains a formidable challenge， primarily due to the lack of high-quality， large-scale labeled datasets for facial age estimation. The manual annotation of facial datasets necessitates considerable time and financial costs. Semi-supervised learning has emerged as a promising strategy for solving this problem because it enables the simultaneous utilization of labeled and unlabeled data. However， achieving satisfactory results in the domain of facial age estimation using semi-supervised learning methods is difficult. This difficulty arises from the limited accuracy of the pseudo-labels produced by these methods， as well as their susceptibility to the influence of outlier data. These factors hinder the effective utilization of unlabeled data， consequently limiting overall performance. Aiming to address these challenges， optimizing the capability of the model to extract features is essential. Such improvements will facilitate the effective acquisition of valuable representations from unlabeled data， thereby yielding highly precise pseudo-labels. Additionally， establishing a semi-supervised learning framework that can adeptly manage the challenges associated with outlier data while optimizing the utilization of the unlabeled dataset is crucial. Consequently， this study presents an open-set semi-supervised multi-task approach for facial age estimation.

This research presents the SwinLEDF model to optimize the capability of the model to extract local and global features from facial images. This model is based on the Swin Transformer architecture and integrates local enhanced feedforward （LEFF） modules along with dynamic filter networks （DFNs）. The Swin Transformer demonstrates proficient capabilities in capturing long-range dependencies and global characteristics， particularly in the analysis of age-related trends and the overall morphology of facial structures. The LEFF module incorporates non-linear transformations at the feature level， facilitating the identification of local patterns within images or feature representations. This capability is essential for differentiating age-related attributes， including intricate details such as wrinkles and skin texture. The DFN module implements a dynamic filtering operation within the spatial dimension of the model’s output， thereby enhancing model flexibility and adaptability. Furthermore， this research presents an open-set semi-supervised multitask learning algorithm to optimize the use of labeled and unlabeled data. In this algorithm， the model assesses the probability of unlabeled data being classified as outliers by integrating the outcomes of a closed-set classifier and a multi-class binary classifier. Subsequently， the model generates pseudo-labels for non-outlier data that meet a specified confidence threshold. Additionally， the model simultaneously learns to estimate sex， race， and age using labeled and unlabeled data. Through this process， the model learns not only the unique characteristics associated with each specific task but also the interrelationships among gender， race， and age， thereby enhancing the capability of the model to process diverse data and increases its expressive power and robustness. Furthermore， the process enables the effective utilization of unlabeled datasets， addressing the challenge of limited labeled data in the field of age estimation. This study employs an adaptive threshold mechanism and a negative learning strategy to optimize the use of unlabeled data. The adaptive threshold mechanism dynamically adjusts the confidence threshold for pseudo-labels based on the model’s training performance across different categories， effectively addressing category imbalance and improving the precision of pseudo-label production. The negative learning strategy enhances the handling of unlabeled data by identifying categories to which the input data does not belong， thereby mitigating the adverse effects of false pseudo-labels on model performance.

This study assesses the proposed methodology using the MORPH and UTKface datasets. On the MORPH dataset， the model exhibits a mean absolute error （MAE） of 1.908 when trained solely on labeled data. This error is further reduced to 1.885 with the inclusion of labeled and unlabeled datasets. Similarly， for the UTKface dataset， the initial MAE is recorded at 4.343 using only labeled datasets， which subsequently reduces to 4.246 following the integration of labeled and unlabeled datasets. Compared to current facial age estimation methods， the proposed approach exhibits superior performance and further optimizes its accuracy by leveraging unlabeled facial datasets.

This study introduces an open-set semi-supervised multi-task learning method for facial age estimation. The proposed method effectively extracts gender， race， and age attributes from facial images while leveraging unlabeled data and appropriately handling potential outliers. This approach addresses the challenges associated with limited labeled data， thereby enhancing the accuracy of facial age estimation. Furthermore， the methodology presents innovative strategies for achieving precise results and holds strong potential for practical applications.

RGB-D salient object detection （SOD） combines complementary information from RGB and depth images， offering substantially enhanced performance in complex and challenging scenes compared to RGB-only models. This technique has gained considerable attention in the academic community due to its capability to effectively capture salient objects by leveraging visual and spatial information. However， existing RGB-D detection models face several key challenges. First， efficiently utilizing and fusing multi-modal information from RGB and depth inputs remains a difficult task due to the inherent differences between the two modalities. RGB images provide rich color and texture details but lack depth information， whereas depth maps offer spatial cues but are often noisy or of low quality. Second， achieving accurate boundary detection is particularly challenging in cluttered or noisy environments. Noisy depth maps and cluttered backgrounds can obscure object contours， making it difficult to predict sharp and precise boundaries. These challenges highlight the urgent need for a robust model that can effectively integrate RGB and depth information while simultaneously addressing noise and enhancing boundary precision.

Aiming to address these challenges， a novel method， the cross-modal feature fusion and detail-enhanced RGB-D salient object detection network （CFADNet）， is introduced. The proposed network incorporates two innovative modules： the cross-modal attention fusion enhancement module （CAFEM） and the boundary feature extraction module （BFEM）. The CAFEM is designed to enhance the integration of RGB and depth features by leveraging attention mechanisms that emphasize the most informative aspects of each modality. Specifically， channel attention is applied to the RGB features to suppress noise and enhance critical color and texture details. Similarly， spatial attention is applied to the depth features to emphasize spatial regions that are relevant for salient object detection. This attention-based fusion mechanism ensures that the model effectively retains global semantic information from the depth map while preserving fine-grained details from the RGB image. The fusion process is structured in multiple layers， progressively integrating features at different scales to fully utilize the complementary strengths of RGB and depth modalities. In contrast， the BFEM is specifically designed to improve the accuracy of salient object boundaries. Accurate contour detection is crucial for generating high-quality saliency maps； thus， BFEM leverages low-level CNN features， which are rich in edge and texture information. These features are refined through channel attention， which filters out noise and irrelevant details， enhancing the clarity of boundary-related cues. The refined features are then used to guide cross-modal feature decoding， ensuring that the final saliency maps exhibit sharp and accurate boundaries. By combining the edge-extraction capabilities of low-level CNN features with the semantic richness of cross-modal features， BFEM notably improves boundary precision in RGB-D salient object detection.

Aiming to evaluate the performance of CFADNet， extensive experiments are conducted on four widely used RGB-D salient object detection datasets： NJU2K， NLPR， STERE， and SIP. These datasets encompass a wide range of diverse and challenging scenes， making them ideal for evaluating the generalization capability of the proposed model. CFADNet is compared against 16 state-of-the-art RGB-D salient object detection methods， including DCF， CIRNet， and CAVER， using standard quantitative metrics such as mean absolute error （MAE）， F-measure（F_β）， and structural similarity （S_α）. CFADNet demonstrated superior performance across all datasets， particularly excelling in the MAE metric. Specifically， this network outperformed the second-best method by 6.9%， 10.5%， 9.7%， and 2.4% on the NJU2K， NLPR， STERE， and SIP datasets， respectively. These substantial improvements highlight the effectiveness of the attention-based fusion strategy and edge refinement mechanisms. Furthermore， CFADNet consistently achieved higher F-measure and Sα scores， indicating that the model not only reduces pixel-level errors but also more accurately preserves the overall structure and shape of salient objects compared to competing methods. In addition to quantitative evaluations， qualitative comparisons are conducted to visually assess the performance of CFADNet in various challenging scenarios. Results show that the proposed method generates saliency maps with sharp and accurate boundaries， even in cases where salient objects exhibit complex edges or are embedded in cluttered and noisy backgrounds. This finding demonstrates the robustness of CFADNet in handling difficult scenes by effectively separating salient objects from their background while preserving fine boundary details. The visual results further confirm that CFADNet successfully captures global semantic information and local detail， ensuring accurate identification and clear isolation of salient objects from the background.

This paper presents CFADNet， a cross-modal feature fusion and detail-enhancement network for RGB-D SOD， designed to address the two major challenges： effective multimodal feature fusion and accurate boundary detection. CFADNet introduces two novel modules， the CAFEM and the BFEM. CFADNet effectively integrates RGB and depth information while notably enhancing the precision of salient object boundaries. The attention mechanisms used in the CAF0EM enable the network to fully leverage the complementary information from RGB and depth modalities. Simultaneously， the BFEM module focuses on refining edge details， resulting in sharper and more accurate saliency predictions. Extensive experiments conducted on four benchmark datasets demonstrate that CFADNet consistently outperforms existing state-of-the-art methods， achieving superior performance across key evaluation metric， including MAE， F-measure， and structural similarity index. These findings highlight the robustness and strong generalization capability of CFADNet in diverse and challenging environments. By combining attention-based feature fusion with effective edge refinement， CFADNet emerges as a powerful and reliable solution for RGB-D salient object detection into complex scenarios. Future research could explore extending this approach to other multi-modal tasks， such as RGB-Thermal or multi-spectral image processing， where challenges related to multi-modal fusion and boundary detection are also prevalent. Additionally， optimizing the computational efficiency of CFADNet for real-time deployment represents a potential research direction， enabling its application in time-sensitive applications such as autonomous driving and robotics.