Co-salient object detection （Co-SOD） aims to identify target regions from a set of related images that are not only visually salient but also semantically consistent across the group. The core challenge of Co-SOD lies in detecting visually prominent objects within individual images and determining which objects exhibit cross-image consistency among multiple images. Traditional Co-SOD methods mainly rely on low-level visual features to construct inter-image relationships. These approaches extract discriminative representations on the basis of handcrafted features，such as color，shape，and texture，to mine common salient objects. Although such methods perform reasonably well under simple backgrounds，they often fail in complex real-world scenarios with strong background clutter，severe occlusions，or multiple coexisting objects. In these cases，non-co-salient but visually salient objects can easily interfere with detection，leading to high false detection rates，poor robustness，and limited generalization. In recent years，with the rapid progress of deep neural networks，Co-SOD methods that are based on convolutional neural networks or vision Transformer have been proposed. By learning high-level semantic features across images，these methods substantially improve detection performance. However，they typically still rely on modeling image groups as the basis of detection without sufficiently incorporating explicit semantic priors. As a result，they lack the capability for proactive understanding of image categories or user-specific targets and therefore struggle in open-domain，multiobject scenarios or when fine-grained semantic distinctions are required. Moreover，when an image contains multiple visually salient but semantically irrelevant objects，these models often fail to distinguish them correctly，mistakenly including non-co-salient objects in the detection results，thus reducing the overall performance. For instance，in multiclass image datasets，such as CoCA，a single image often contains multiple visually prominent objects，but only a subset of them is semantically related and constitute co-salient targets. Other salient regions，although visually striking，act as semantic distractors. Without explicit semantic filtering mechanisms，traditional methods are often unable to distinguish which objects are the true co-salient targets，thus decreasing the accuracy of Co-SOD. To address these limitations，this study proposes a semantic-guided open-vocabulary Co-SOD framework. The proposed method leverages language prompts to guide the model’s attention toward target regions that are semantically aligned with image categories，thereby enabling semantic filtering of co-salient objects in complex multiobject scenarios and improving detection accuracy and semantic generalization ability.

The proposed semantic-guided open-vocabulary Co-SOD framework integrates three key components，namely，a CLIP-based group-consistent category generation method，the open-vocabulary object detection model OWLv2，and the universal segmentation model SAM，forming a complete pipeline that begins from semantic prompt generation to object detection and pixel-level segmentation. First，the system introduces a CLIP-based group-consistent category generation method to automatically derive the most representative group-consistent category for a given image set. This procedure eliminates the need for manually specifying class names，reduces human effort，and provides accurate and stable semantic prompts for subsequent detection. Specifically，the method computes cross-modal similarities between the visual features of each image in the group and candidate category text features and automatically generates the most representative category name that is consistent across the group to serve as a reliable input for the detection model. Second，the generated group-consistent category name is used as a natural language prompt and fed into the open-vocabulary object detection model OWLv2. OWLv2，which is pretrained on large-scale image-text pairs，possesses strong cross-modal semantic alignment and matching capabilities. By mapping image region and text prompt features into a unified semantic space，OWLv2 can identify target regions that are highly relevant to the natural language prompts. To further improve the robustness of semantic understanding，this work constructs multiple semantically related natural language phrases for each category name and adopts a multiprompt parallel input mechanism to feed them into OWLv2 simultaneously. This process enhances the model’s capacity to capture diverse semantic descriptions，improves stability and robustness in semantic matching，and effectively addresses uncertainties caused by language ambiguity. During the candidate region generation stage，similarities between region feature and text semantic vectors are calculated to identify candidate regions that are highly related to the input prompts，with the model outputting corresponding bounding boxes and confidence scores. A dynamic thresholding strategy is introduced to further improve detection flexibility and adaptability. This mechanism automatically adjusts detection thresholds on the basis of the confidence levels of semantic matches，thereby increasing the recall of low-confidence targets while maintaining precision. As a result，the framework can effectively detect occluded or weakly salient targets and avoid missed detections caused by overly strict thresholds. At the saliency segmentation stage，the bounding box regions filtered during the semantic detection stage are passed to segment anything model （SAM）. SAM then performs pixel-level mask generation for each region of interest. Leveraging its powerful structural perception and boundary modeling capabilities，SAM produces high-quality segmentation results. The final system outputs salient masks that are structurally complete and have clear boundaries. This design effectively decouples the two subtasks of semantic perception and visual detail modeling，allowing each module to fully leverage its strengths，thereby enhancing the overall semantic guidance ability and object delineation quality of the system.

Experiments are conducted on three publicly available datasets： CoCA，CoSal2015，and CoSOD3k. The proposed method is compared with 15 state-of-the-art Co-SOD methods. On the CoCA dataset，our method achieves a 25.7% improvement in F measure and a 5.2% reduction in the mean absolute error （MAE） compared with the second-best model. On the CoSOD3k dataset，our method improves the F measure by 2.5%. On the CoSal2015 dataset，it improves the F measure by 2.2% and reduces MAE by 1.2%. These results comprehensively validate the strong ability of the proposed framework to recognize semantically relevant co-salient objects in multiobject open scenarios. Ablation experiments conducted on the CoSOD3k dataset further confirm the effectiveness of the proposed approach in enhancing Co-SOD performance.

This study proposes a semantic-guided open-vocabulary Co-SOD framework. The framework leverages a CLIP-based group-consistent category generation method to automatically generate semantic prompts for image groups and integrates OWLv2 for open-vocabulary object detection with SAM for universal segmentation. This process enables explicit semantic filtering of co-salient targets and fine-grained pixel-level segmentation. The proposed framework substantially enhances semantic generalization and target recognition robustness，enabling the effective localization of semantically consistent salient objects in multiobject and complex background scenarios. By unifying three critical components，namely，semantic prompt generation，open-vocabulary detection，and saliency segmentation，this framework provides an innovative and practical solution for advancing Co-SOD in open environments.

Specular highlight detection and removal，with the primary objective of accurately identifying highlight regions exhibiting mirror-like reflection characteristics and precisely recovering the underlying pixel information，constitute a fundamental research topic in computer vision and image processing. This technology demonstrates considerable practical value across various visual applications，including object detection，medical image analysis，industrial defect identification，and 3D imaging. From an optical perspective，the specular highlight phenomenon originates from the directional reflection behavior of incident light on smooth non-Lambertian surfaces，and its physical mechanism is theoretically describable through Fresnel equations. Traditional highlight removal methods typically rely on strict physical assumptions，which primarily encompass three classical approaches： 1） chromaticity space analysis-based techniques，2） illumination estimation-based algorithms，and 3） polarization information-utilizing solutions. However，these methods exhibit limited adaptability to complex and variable highlight removal requirements in real-world scenarios because of their constraints regarding specific scenes or object types. By contrast，deep learning-based highlight detection and removal techniques adopt a data-driven approach to automatically learn the complex nonlinear mapping relationships between highlight reflections and scene content，thereby effectively circumventing the dependency on prior assumptions inherent in traditional methods. The shifted window attention mechanism demonstrates unique advantages in highlight removal tasks and effectively addresses issues，such as texture information loss and color distortion，caused by specular highlights. This mechanism integrates local window partitioning with cross-window interaction，thus reducing computational complexity while maintaining global modeling capability. Building upon the exceptional modeling capability demonstrated by the shifted window attention mechanism during the feature extraction stage，this study proposes a two-stage mask-guided highlight removal network architecture. The proposed framework employs the shifted window attention mechanism to extract multiscale cross-channel features and utilizes highlight masks as prior knowledge to guide subsequent pixel recovery in highlight regions，thereby achieving highlight localization and removal. Notably，existing deep learning methods face challenges regarding insufficient training data. Current publicly available highlight datasets predominantly employ synthetic generation or laboratory-controlled lighting acquisition methods，failing to meet the requirements of real-world applications. To address this limitation，this study additionally constructs an in-situ automotive highlight dataset captured under multiple illumination conditions. This dataset has the following characteristics： 1） coverage of diverse natural illumination conditions，2） inclusion of complex background interference，and 3） precise pixel-level annotations，thereby providing a data foundation for enhancing model generalization performance in real-world scenarios.

In this study，we construct a real highlight dataset with cars as the main object，which is based on common cars in daily life with different highlight regions and intensities. Each pair of highlight images has its corresponding real highlight mask image. For highlight detection and removal，we propose a mask-guided two-stage highlight removal network （MG-TransUNet）. This network explicitly divides the highlight detection and removal task into two phases. In the first phase，we train with a lightweight U-Net architecture to extract the highlight regions in the highlight images and generate the highlight mask map. In the second phase，a shifted window attention Transformer network that is based on the U-Net architecture is designed to recover the diffuse images of highlight regions by using the predicted highlight mask map as an a priori guide. Our experimental dataset is selected from three major public datasets （i.e.，paired specular-diffuse image dataset（PSD），specular highlight image quadruples（SHIQ） and synthetic specular highlight removal dataset（SSHR）） with our proposed automotive dataset. Among the four datasets，only SHIQ and automobile datasets provide highlight masks. To make our method trainable on the PSD and SSHR datasets，we subtract and threshold the highlight images and the corresponding specular no-highlight images from the two datasets to obtain the highlight mask for each pair of images. The highlight detection network is trained with the highlight images and masks at the first stage，and the obtained highlight masks are used as a priori information to guide the highlight removal task at the second stage. Our mask-guided highlight removal network is implemented by the Pytorch toolbox. The input images and ground truth maps are resized to 256 × 256 for training，the momentum parameter is set to 0.99，and the initial learning rate is set to 2 ×

Experiments are conducted on the proposed automotive dataset and three publicly available datasets to compare eight state-of-the-art methods. The quantitative evaluation metrics include the structural similarity index measure and peak signal-to-noise ratio （PSNR）. Experimental results show that the PSNR value ranks second on the PSD dataset. It is improved by 1.11 compared with that in the model with second-best performance on the SHIQ dataset and by 0.38 compared with that in the model with the second-best performance on the SSHR dataset. Comparative experiments are also performed on automotive datasets to validate the effectiveness of the proposed dataset.

We propose a car highlight dataset for real scenarios and a mask-guided network incorporating a window-shift attention mechanism to perform the highlight removal task. The experimental results show that our highlight removal network can accurately recognize highlight regions and remove highlight images. The code link： https://github.com/chenWULUQI/MG-Trans_Unet.

The continuous iteration of virtual reality （VR） technology has driven the digital transformation of education，and its application in physics experiment teaching has gradually become a research hotspot. However，existing consumer-grade VR devices are limited by their tracking nodes； they fail to realize natural and smooth full-body motion reconstruction，leading to insufficient user immersion，discontinuous interaction experiences，and even visual discomfort （e.g.，motion sickness caused by conflicts between visual and vestibular perceptions）. These issues severely restrict the application of VR systems in high-demand immersive physics experiment scenarios that require precise interactions and strong sense of presence. This study aims to 1） address the technical bottlenecks of poor full-body motion reconstruction and low interaction fidelity in current VR-based physics experiments； 2） optimize the biomechanical rationality，motion naturalness，and interaction accuracy of virtual embodiments while achieving dynamic proportion calibration for users with different body types and consistency adjustment for cross-platform models； 3） explore the mechanism by which virtual embodied interaction influences user experience，realizing the synergistic optimization of immersive experience enhancement and physiological comfort （i.e.，reducing motion sickness）； and 4） provide a theoretical basis and practical guidance for the future implementation of multiuser collaborative virtual embodied interaction in the field of immersive physics education.

To achieve these objectives，this study proposes a virtual embodied motion-solving framework that integrates three core technologies： bone retargeting，inverse kinematics （IK），and data-driven motion matching. First，a hierarchical 3D human skeletal structure model is constructed. This model divides the human body into two biomechanically distinct subsystems，namely，spinal composite chains （pelvis-vertebrae-neck-head） and limb chains （upper limbs： shoulder-upper arm-lower arm-hand； lower limbs： pelvis-thigh-shank-foot），and adopts vertex skinning technology with blend shapes to avoid unnatural joint deformation caused by traditional linear blend skinning. For bone retargeting，a dual-calibration mechanism is designed. Global proportion calibration uses position data from head-mounted displays and hand controllers to calculate vertical and horizontal scaling factors； it adjusts the root joint position to match the user’s overall proportion while maintaining foot-ground contact. Local skeletal chain calibration adjusts the length of individual segments in five key chains （spinal chain，two arm chains，and two leg chains） while preserving internal length ratios to adapt to local body differences. For IK solving，a segmented strategy is adopted. An improved Gauss-Seidel algorithm （with joint angle constraints and multithreaded acceleration） is used for the head-spine composite chain to ensure stable convergence. A geometric analytical method with physiological limits （e.g.，elbow flexion 0°-150°） is applied to limb chains to avoid nonphysical phenomena，such as limb penetration. Meanwhile，a hybrid hand control scheme （integrating repositioning and motion correction） on the basis of the OpenXR skeleton system is implemented to ensure fine interaction accuracy. For data-driven motion matching，an animation database is built using an Xsens motion capture system； this database covers diverse gaits，speeds，and turning motions with key features，such as joint position and foot contact state. Real-time frame matching （via hierarchical indexing） and motion smoothing （via inertial interpolation） are realized to compensate for insufficient lower-limb tracking data. For evaluation，64 healthy college students from Beijing Normal University （31 males and 33 females aged 18-40，with no prior virtual embodiment experience） are stratified into an experimental group （using the improved virtual embodied interaction model） and a control group （using meta movement-based virtual embodiment） on the basis of scores from the visually induced motion sickness susceptibility questionnaire （VIMSSQ）. The experiment is conducted on a hardware platform that has an Intel i7-12700F processor，NVIDIA RTX 3070 graphics card，and Meta Quest 3 HMD，and scenes are developed in Unity 2021.3.32f1c1 （frame rate > 90 frame/s to meet real-time requirements）. Participants are asked to complete three basic tasks （i.e.，sitting on a chair，picking up a cup，button interaction with left/right hands） and a momentum-conservation physics experiment. Objective data （task completion time and completion rate） and subjective data （obtained via the embodiment questionnaire （EQ），igroup presence questionnaire （IPQ），and simulator sickness questionnaire （SSQ）） are collected and analyzed using MATLAB R2022b，Origin 2024b，and SPSS 27 （including independent samples t-test，Mann-Whitney U test，Pearson correlation，and multiple linear regression）.

Comprehensive experimental results verify the effectiveness of the proposed framework. In terms of quantitative task performance，Task 1 （sitting） has both groups achieve 100% completion rate，with similar average times （experimental group： 32.25 s； control group： 32.85 s）. In Task 2 （cup picking），the experimental group has a shorter average time compared with the control group （30.78 s vs. 32.00 s） and a stable completion rate （90% vs. 95%）. Task 3 （button interaction） reveals the experimental group’s advantage，especially in nondominant left-hand operations （average time： 36.45 s vs. 38.36 s； completion rate： 70% vs. 55%）. In terms of subjective experience，the EQ results show that the experimental group has significantly high scores in agency （p = 0.003，Cohen’s d = 1.028） and self-location （p = 0.037，Cohen’s d = 1.272） but no significant difference in ownership. The IPQ results indicate that the experimental group scores significantly high in general presence （p = 0.032），involvement （p = 0.012），and overall immersion （p = 0.045）. Multiple linear regression （R² = 0.543，p < 0.001） shows that the experimental group has low SSQ scores （β = -0.495，p < 0.001）； the IPQ scores negatively predict motion sickness （β = -0.493，p = 0.017）. The interaction terms （VIMSSQ × Group，β = -0.365，p = 0.024； IPQ × Group，β = -0.326，p = 0.026） confirm that the framework weakens the effect of motion sickness susceptibility and strengthens immersion’s mitigating effect on discomfort.

The proposed virtual embodied motion solving framework effectively addresses the technical limitations of current VR-based immersive physics experiments. By integrating bone retargeting，IK，and data-driven methods，it substantially enhances the biomechanical rationality （avoiding nonphysical phenomena，such as limb penetration），motion naturalness （via hierarchical skeletal modeling and inertial interpolation），and interaction accuracy （through hand optimization and local chain calibration） of virtual embodiments. The framework improves users’ embodiment sense （especially agency and self-location），immersion，and spatial motion perception and reduces visual discomfort via a dual-path mechanism （direct experience enhancement through improved presence； compensatory mitigation of motion sickness via strengthened immersion）. Future research may focus on three directions： exploring high-precision and personalized virtual embodiment generation （e.g.，physics experiment-specific motion libraries driven by large models），integrating physical laws and environmental factors to enhance interaction authenticity，and optimizing real-time performance for multiuser collaborative scenarios. This technology has broad application prospects in advancing immersive physics education and promoting the popularization of high-quality VR-based experimental teaching.

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.

Most existing vision-based rail defect detection methods face challenges such as high parameter counts， computational complexity， slow detection speeds， and limited accuracy. Aiming to overcome these limitations， this paper introduces a lightweight pyramid cross-attention network （LPCANet） for orbital image defect detection using RGB images and depth images.

LPCANet adopts MobileNetv2 as its backbone network to extract multiscale feature maps from RGB images. Simultaneously， a lightweight pyramid module （LPM） is employed to extract similarly-sized feature maps from depth images. Each stage of the LPM comprises a sequence of operations including max pooling， a 3 × 3 convolutional layer， batch normalization， and ReLU activation， enabling efficient extraction of features from depth images. By leveraging deep learning， RGB-D technology， and salient object detection， LPCANet efficiently extracts multiscale feature representations from RGB and depth data. The LPM handles depth image features， while the backbone captures detailed pyramid features from RGB images. Subsequently， a cross-attention mechanism （CAM） is applied to integrate the feature maps from both modalities， enhancing the network’s focus on relevant defect regions. Additionally， a spatial feature extractor （SFE） is introduced to further boost defect detection performance. Finally， a “pixel shuffle” operation is used to restore the output to the original image resolution.

The proposed scheme was computationally evaluated using the PyTorch library in an environment equipped with an NVIDIA 3090 GPU， alongside several benchmark models for comparison. For the evaluation of LPCANet， three publicly available unsupervised RGB-D rail datasets were used： NEU-RSDDS-AUG， RSDD-TYPE1， and RSDD-TYPE2. Experimental results on the NEU-RSDDS-AUG dataset indicate that LPCANet achieves excellent efficiency， with 9.90 million parameters， a computational complexity of 2.50 G， a model size of 37.95 MB， and a running speed of 162.60 frames per second. Compared to 18 existing rail defect detection schemes， LPCANet exhibits superior lightness in performance. In particular， when compared against CSEPNet， the current best-performing model， LPCANet achieves improvements across several evaluation metrics： +1.48% in S_{\alpha }Sα， +0.86% in intersection over union （IOU）， +0.14% in F_{\beta }^{\mathrm{m}\mathrm{a}\mathrm{x}}Fβmax， +0.03% in mean average precision （mAP）， and +1.77% in mean absolute error （MAE）. An ablation study was conducted on four upsampling methods （interpolation， transposed convolution， patch merging， and “pixel shuffle”） to evaluate their effectiveness within the LPCANet framework. Among these， the “pixel shuffle” method demonstrated clear advantages and was found to be the most suitable for the LPCANet model. Further ablation studies were conducted on four different components （backbone network， LPM， SFE， and CAM）. The results indicate that CAM and SFE notably enhance the detection performance of LPCANet. An in-depth analysis of various backbone networks confirmed that LPCANet model is not only compatible with existing backbone networks but also consistently achieves superior detection results. Aiming to evaluate the model’s generalization capability beyond rail datasets， experiments were also conducted on three non-rail defect datasets： DAGM2007， MT， and Kolektor-SDD2. The results show that LPCANet delivers improved performance across three key metrics： mAP， MAE， and IOU， demonstrating its potential for general-purpose defect detection tasks.

The LPCANet model proposed in this study effectively combines the advantages of traditional and deep learning approaches， demonstrating strong practical value in the field of rail defect image processing. In the future， this scheme will focus on further reducing the model size to achieve rapid detection speeds while ensuring further improvements in performance quality.

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.