Local Modification Research Articles

Local modification of the crystalline structure due to co-doping with RE3+ in cadmium metasilicate

Persistent emission is a well-known property of cadmium metasilicate (CdSiO3) doped and co-doped with rare earth ions due to the ease of choosing the desired luminescence colour, as well as the possibility of adjusting the emission colour and the luminescence duration. Experimental work has demonstrated that combinations of different rare earth ions can produce different emission spectra colours as well as modify the lifetime of emissions. Theoretical work demonstrated a preference of single dopants to substitute cadmium sites with a Cd-vacancy as a compensation mechanism. To help elucidate the luminescent mechanisms through structural modification and consequently modification of the crystal field generated in the material by the defective region, in this work, several configurations for combined defects were tested considering the distance and preferential configuration of all the defects that comprise the system, as well as the possibility of a high concentration of these defects. Results showed a preferential arrangement for the incorporation of a Cd-vacancy as a form of charge compensation. The arrangement forms an angle closer to 180° considering the Cd-vacancy as vertex and presents a distance of around 7 Å between rare earth ions. It was also identified that the formation of a single pair with two different RE3+ ions close to a cadmium vacancy are minimally energetically more favourable to be formed in relation to clusters of dopants and co-dopants. This indicates that at low doping concentrations, RE3+ dopant pairs will form separately, but by increasing the concentration it is possible to form a cluster of defects involving RE3+. This may explain the low concentrations of dopants in this matrix. The distortion caused in the local lattice by the presence of a RE3+ pair of different dopants is bigger than the distortion caused by a RE3+ pair of dopants of the same chemical species. Therefore, CdSiO3: RE13+, RE23+ has a crystal field that is different from the crystal field experienced by RE3+ as a single dopant and the presence of the co-dopant ion will modify the luminescence of the system even though the generating defect of the luminescent centre remains the same.

Dual-former: Hybrid self-attention transformer for efficient image restoration

Recently, image restoration transformers have achieved comparable performance with previous state-of-the-art CNNs. In this work, we present Dual-former whose critical insight is to combine the powerful global modeling ability of self-attention modules and the local modeling ability of convolutions in an overall architecture. With convolution-based Local Feature Extraction modules equipped in the encoder and the decoder, we only adopt a novel Hybrid Transformer Block in the latent layer to model the long-distance dependence in spatial dimensions and handle the uneven distribution between channels. Such a design eliminates the substantial computational complexity in previous image restoration transformers and achieves superior performance on multiple image restoration tasks. Experiments demonstrate that Dual-former achieves a 1.91 dB gain over the state-of-the-art MAXIM method on the Indoor dataset for single image dehazing while consuming only 4.2% GFLOPs as MAXIM. For single image deraining, it exceeds the SOTA method by 0.1 dB PSNR on the average results of five datasets with only 21.5% GFLOPs. Dual-former also substantially surpasses the latest methods on various tasks, with fewer parameters.

LIFNS: Design of a novel Lidar-IMU fusion navigation system for AGVs in smart factories

Traditional Automated Guided Vehicle (AGV) robots commonly employ 2D plane navigation systems. However, with the expansion of AGV application scenarios and the increasing complexity of structures, the limitations of existing 2D navigation systems have become more pronounced, rendering them inadequate for addressing these challenges. To tackle this issue, this paper proposes a novel navigation system suitable for AGV robots composed of fused Lidar and Inertial Measurement Unit (IMU), named as Lidar-IMU Fusion Navigation System (LIFNS). LIFNS primarily comprises mapping, localization, and fused path planning modules. In the mapping module, a 3D point cloud map for precise 3D localization and a 2D grid map for path planning are constructed using multiple-line Lidar and IMU. For the localization module, a fusion localization approach is introduced, combining IMU data with Normal Distributions Transform (NDT) point cloud registration through Unscented Kalman Filtering (UKF). Finally, global path planning is executed using the A* algorithm on the grid map, while local path planning utilizes the Timed-Elastic Band (TEB) algorithm. The effectiveness and universality of LIFNS are validated through simulation experiments and real-world deployment tests. The experimental results demonstrate that LIFNS achieves centimeter-level accuracy in both mapping and localization, effectively alleviating the issues of low precision and significant limitations present in traditional AGV robots. This positions LIFNS with promising applications in enclosed settings such as smart factories, industrial parks, and healthcare facilities.

Development of an in situ alloying method for high-performance welding processes to achieve an LTT effect by local modification of the alloy content

One possible option for increasing the fatigue strength of welded joints is the use of so-called low transformation temperature (LTT) alloys. The aim is to introduce residual compressive stresses into the weld to counteract crack initiation and propagation. Until now, there has been no application of an LTT effect to high-performance welding processes such as the laser beam submerged arc hybrid welding process (LUPuS hybrid). First, the LUPuS hybrid single-wire process was further developed into the LUPuS tandem hybrid process. This makes it possible to equip the two submerged arc welding torches with different commercially available filler wires. The aim of the work is to further develop the LUPuS tandem hybrid welding process to enable the use of the LTT effect. The in situ alloying process for obtaining the LTT effect from commercially available material combinations was extended to the two-wire process. The alloy obtained was investigated by means of energy dispersive x-ray spectroscopy and hardness measurements and the influence on residual stresses was determined by the borehole method supported by electronic speckle pattern interferometry.

From Lewis Acid to Lewis Base by La3+-to-Y3+ Substitution in α-YB5O9: Local Structure Modification Induced Lewis Basicity.

Different from the common perspective of average structure, we propose that the locally elongated metal-oxygen bonds induced by La3+-to-Y3+ substitution to a Lewis acid α-YB5O9 generate medium-strength basic sites. Experimentally, NH3- and CO2-TPD experiments prove that the La3+ doping of α-Y1-xLaxB5O9 (0 ≤ x ≤ 0.24) results in the emergence of new medium-strength basic sites and the increasing La3+ concentration modifies the number, not the strength, of the acidic and basic sites. The catalytic IPA conversion exhibits a reversal of the product selectivity, i.e., from 93% of propylene for α-YB5O9 to ∼90% of acetone for α-Y0.76La0.24B5O9, which means the La3+ doping gradually turns the solid from a Lewis acid to a Lewis base. Besides, α-Y0.76RE0.24B5O9 (RE = Ce, Eu, Gd, Tm) compounds were prepared to consolidate the above conjecture, where the acetone selectivity exhibits a linear dependence on the ionic radius (or electronegativity). This work suggests that the substitution-induced local structure change deserves more attention.

Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum

Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely applied to GFD, characterizing the anomalous possibility of a node by aggregating neighbor information. However, fraud graphs are inherently heterophilic, thus most of GNNs perform poorly due to their assumption of homophily. In addition, due to the existence of heterophily and class imbalance problem, the existing models do not fully utilize the precious node label information. To address the above issues, this paper proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively. The first module starts from the perspective of the spectral domain, and solves the heterophily problem to a certain extent. Specifically, it divides the spectrum into various mixed-frequency bands based on the correlation between spectrum energy distribution and heterophily. Then in order to make full use of the node label information, a local environmental constraint module is adaptively designed. The comprehensive experimental results on four real-world fraud detection datasets denote that SEC-GFD outperforms other competitive graph-based fraud detectors. We release our code at https://github.com/Sunxkissed/SEC-GFD.

Recasting Regional Lighting for Shadow Removal

Removing shadows requires an understanding of both lighting conditions and object textures in a scene. Existing methods typically learn pixel-level color mappings between shadow and non-shadow images, in which the joint modeling of lighting and object textures is implicit and inadequate. We observe that in a shadow region, the degradation degree of object textures depends on the local illumination, while simply enhancing the local illumination cannot fully recover the attenuated textures. Based on this observation, we propose to condition the restoration of attenuated textures on the corrected local lighting in the shadow region. Specifically, We first design a shadow-aware decomposition network to estimate the illumination and reflectance layers of shadow regions explicitly. We then propose a novel bilateral correction network to recast the lighting of shadow regions in the illumination layer via a novel local lighting correction module, and to restore the textures conditioned on the corrected illumination layer via a novel illumination-guided texture restoration module. We further annotate pixel-wise shadow masks for the public SRD dataset, which originally contains only image pairs. Experiments on three benchmarks show that our method outperforms existing state-of-the-art shadow removal methods. Project page in: yuhaoliu7456.github.io/RRL-Net.

Privileged Prior Information Distillation for Image Matting

Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we propose a novel framework named Privileged Prior Information Distillation for Image Matting (PPID-IM) that can effectively transfer privileged prior environment-aware information to improve the performance of trimap-free students in solving hard foregrounds. The prior information of trimap regulates only the teacher model during the training stage, while not being fed into the student network during actual inference. To achieve effective privileged cross-modality (i.e. trimap and RGB) information distillation, we introduce a Cross-Level Semantic Distillation (CLSD) module that reinforces the students with more knowledgeable semantic representations and environment-aware information. We also propose an Attention-Guided Local Distillation module that efficiently transfers privileged local attributes from the trimap-based teacher to trimap-free students for the guidance of local-region optimization. Extensive experiments demonstrate the effectiveness and superiority of our PPID on image matting. The code will be released soon.

BCLNet: Bilateral Consensus Learning for Two-View Correspondence Pruning

Correspondence pruning aims to establish reliable correspondences between two related images and recover relative camera motion. Existing approaches often employ a progressive strategy to handle the local and global contexts, with a prominent emphasis on transitioning from local to global, resulting in the neglect of interactions between different contexts. To tackle this issue, we propose a parallel context learning strategy that involves acquiring bilateral consensus for the two-view correspondence pruning task. In our approach, we design a distinctive self-attention block to capture global context and parallel process it with the established local context learning module, which enables us to simultaneously capture both local and global consensuses. By combining these local and global consensuses, we derive the required bilateral consensus. We also design a recalibration block, reducing the influence of erroneous consensus information and enhancing the robustness of the model. The culmination of our efforts is the Bilateral Consensus Learning Network (BCLNet), which efficiently estimates camera pose and identifies inliers (true correspondences). Extensive experiments results demonstrate that our network not only surpasses state-of-the-art methods on benchmark datasets but also showcases robust generalization abilities across various feature extraction techniques. Noteworthily, BCLNet obtains significant improvement gains over the second best method on unknown outdoor dataset, and obviously accelerates model training speed.

Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

Learning 3D representation plays a critical role in masked autoencoder (MAE) based pre-training methods for point cloud, including single-modal and cross-modal based MAE. Specifically, although cross-modal MAE methods learn strong 3D representations via the auxiliary of other modal knowledge, they often suffer from heavy computational burdens and heavily rely on massive cross-modal data pairs that are often unavailable, which hinders their applications in practice. Instead, single-modal methods with solely point clouds as input are preferred in real applications due to their simplicity and efficiency. However, such methods easily suffer from limited 3D representations with global random mask input. To learn compact 3D representations, we propose a simple yet effective Point Feature Enhancement Masked Autoencoders (Point-FEMAE), which mainly consists of a global branch and a local branch to capture latent semantic features. Specifically, to learn more compact features, a share-parameter Transformer encoder is introduced to extract point features from the global and local unmasked patches obtained by global random and local block mask strategies, followed by a specific decoder to reconstruct. Meanwhile, to further enhance features in the local branch, we propose a Local Enhancement Module with local patch convolution to perceive fine-grained local context at larger scales. Our method significantly improves the pre-training efficiency compared to cross-modal alternatives, and extensive downstream experiments underscore the state-of-the-art effectiveness, particularly outperforming our baseline (Point-MAE) by 5.16%, 5.00%, and 5.04% in three variants of ScanObjectNN, respectively. Code is available at https://github.com/zyh16143998882/AAAI24-PointFEMAE.

Multi-Modal Disordered Representation Learning Network for Description-Based Person Search

Description-based person search aims to retrieve images of the target identity via textual descriptions. One of the challenges for this task is to extract discriminative representation from images and descriptions. Most existing methods apply the part-based split method or external models to explore the fine-grained details of local features, which ignore the global relationship between partial information and cause network instability. To overcome these issues, we propose a Multi-modal Disordered Representation Learning Network (MDRL) for description-based person search to fully extract the visual and textual representations. Specifically, we design a Cross-modality Global Feature Learning Architecture to learn the global features from the two modalities and meet the demand of the task. Based on our global network, we introduce a Disorder Local Learning Module to explore local features by a disordered reorganization strategy from both visual and textual aspects and enhance the robustness of the whole network. Besides, we introduce a Cross-modality Interaction Module to guide the two streams to extract visual or textual representations considering the correlation between modalities. Extensive experiments are conducted on two public datasets, and the results show that our method outperforms the state-of-the-art methods on CUHK-PEDES and ICFG-PEDES datasets and achieves superior performance.

LGMRec: Local and Global Graph Learning for Multimodal Recommendation

The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority of existing studies typically focus on utilizing modal features or modal-related graph structure to learn user local interests. Nevertheless, these approaches encounter two limitations: (1) Shared updates of user ID embeddings result in the consequential coupling between collaboration and multimodal signals; (2) Lack of exploration into robust global user interests to alleviate the sparse interaction problems faced by local interest modeling. To address these issues, we propose a novel Local and Global Graph Learning-guided Multimodal Recommender (LGMRec), which jointly models local and global user interests. Specifically, we present a local graph embedding module to independently learn collaborative-related and modality-related embeddings of users and items with local topological relations. Moreover, a global hypergraph embedding module is designed to capture global user and item embeddings by modeling insightful global dependency relations. The global embeddings acquired within the hypergraph embedding space can then be combined with two decoupled local embeddings to improve the accuracy and robustness of recommendations. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our LGMRec over various state-of-the-art recommendation baselines, showcasing its effectiveness in modeling both local and global user interests.

Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal

Visible watermarks, while instrumental in protecting image copyrights, frequently distort the underlying content, complicating tasks like scene interpretation and image editing. Visible watermark removal aims to eliminate the interference of watermarks and restore the background content. However, existing methods often implement watermark component removal and background restoration tasks within a singular branch, leading to residual watermarks in the predictions and ignoring cases where watermarks heavily obscure the background. To address these limitations, this study introduces the Removing Interference and Recovering Content Imaginatively (RIRCI) framework. RIRCI embodies a two-stage approach: the initial phase centers on discerning and segregating the watermark component, while the subsequent phase focuses on background content restoration. To achieve meticulous background restoration, our proposed model employs a dual-path network capable of fully exploring the intrinsic background information beneath semi-transparent watermarks and peripheral contextual information from unaffected regions. Moreover, a Global and Local Context Interaction module is built upon multi-layer perceptrons and bidirectional feature transformation for comprehensive representation modeling in the background restoration phase. The efficacy of our approach is empirically validated across two large-scale datasets, and our findings reveal a marked enhancement over existing watermark removal techniques.

RetouchFormer: Semi-supervised High-Quality Face Retouching Transformer with Prior-Based Selective Self-Attention

Face retouching is to beautify a face image, while preserving the image content as much as possible. It is a promising yet challenging task to remove face imperfections and fill with normal skin. Generic image enhancement methods are hampered by the lack of imperfection localization, which often results in incomplete removal of blemishes at large scales. To address this issue, we propose a transformer-based approach, RetouchFormer, which simultaneously identify imperfections and synthesize realistic content in the corresponding regions. Specifically, we learn a latent dictionary to capture the clean face priors, and predict the imperfection regions via a reconstruction-oriented localization module. Also based on this, we can realize face retouching by explicitly suppressing imperfections in our selective self-attention computation, such that local content will be synthesized from normal skin. On the other hand, multi-scale feature tokens lead to increased flexibility in dealing with the imperfections at various scales. The design elements bring greater effectiveness and efficiency. RetouchFormer outperforms the advanced face retouching methods and synthesizes clean face images with high fidelity in our list of extensive experiments performed.

Modulation of AQP2 localization and water reabsorption.

Modulation of AQP2 localization and water reabsorption.

Abstract 3996: Secreted cytokine-αPDL1 fusion proteins improve solid tumor chimeric antigen receptor (CAR) T-cell therapy

Abstract Background: Success of Chimeric Antigen Receptor (CAR) T cells therapy for solid tumors are limited by the suppressive solid tumor microenvironment (TME), T cell exhaustion, lack of persistence, and poor trafficking to tumors. Strategies to improve this include therapeutic combinations, such as checkpoint inhibition (CPI) or cytokine support. However, CPI therapy has largely failed because of refractory and resistant tumors and cytokine administration can lead to lethal toxicities. To this end, we propose that enabling CAR T cells to secrete bi-functional fusion proteins consisting of cytokine modifiers (ie: IL12, IL15, or TGFβ-trap) combined with checkpoint inhibition (αPDL1 scFv) can provide tumor PDL1 sequestered cytokine activity and local tumor-immune modulation to boost solid tumor CAR T cell efficacy, enhance CPI impacts, and safely improve durable outcomes. Methods: Mouse T cells dual transduced to express surface CAR targeting prostate or ovarian solid tumors in addition to secretable cytokine fused to an αPDL1 scFv were generated. In vitro, the PDL1 blockade capacity and tumor-surface cytokine presentation of supernatants from dual transduced CAR T cells was assessed on PDL1 induced tumor cells. Further, secreted fusion protein transduced CAR T cells were assessed for function, phenotype, and cytokine release in a repetitive tumor rechallenge assay. In vivo, mouse CAR T cells dual transduced with fusion proteins and appropriate controls were assessed for anti-tumor efficacy, survival kinetics, and toxicity in immunocompetent solid tumor mouse models of prostate cancer and intraperitoneal disseminated ovarian cancer. Results: CAR T cells were shown to be successfully dual transduced, and secreted αPDL1-cytokine fusion proteins exhibiting functional PDL1 binding characteristics in vitro. CAR T cells engineered with αPDL1-IL12 fusion protein had greater anti-tumor activity and CAR specific expansion compared to other tested fusion proteins in in vitro co-culture assays. In a syngeneic prostate tumor model, mice receiving CAR T cells with αPDL1-IL12 fusion T cells out competed other cytokine fusion combinations. In a syngeneic ovarian tumor model we safely achieved 100% curative response rate with CAR secreting αPDL1-IL12 fusion in contrast to CAR with αPDL1mutIL12 control along with tumor regional PDL1 blockade on myeloid subsets within the TME. Conclusions: Our findings suggest that CAR T cells engineered to secrete αPD-L1-IL12 fusion protein show improved tumor control, less systemic toxicity, and enhanced expansion which promotes eradication of disease in two independent in vivo tumor models with two unique solid tumor CAR targets. We believe this strategy has the potential to improve solid tumor CAR T-cell efficacy and enhance durable innate immune responses. Citation Format: Lea Christian, John P. Murad, Lupita Lopez, Anthony Park, Jason Yang, Eric Lee, Candi Trac, Stephen Forman, Saul J. Priceman. Secreted cytokine-αPDL1 fusion proteins improve solid tumor chimeric antigen receptor (CAR) T-cell therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3996.

ALNet: An adaptive channel attention network with local discrepancy perception for accurate indoor visual localization

Visual localization, a fundamental component of several computer vision tasks, has been predominantly realized by scene coordinate regression (SCoRe) techniques. These methods leverage neural networks for scene coordinates prediction, followed by a PnP algorithm to recover the 6-DOF camera pose. However, similar image patches are prevalent in indoor scenes, which results in the extraction of comparable features for the regression of different scene coordinates. As a result, the localization accuracy is severely declined. In this work, we develop ALNet, a novel SCoRe method that incorporates a local discrepancy perception module (LDPM) and an adaptive channel attention module (ACAM) to address this challenge. For LDPM, our key insight lies in that scene attributes around different similar image patches are inconsistent. Technically, for each image patch, LDPM identifies a certain number of the most dissimilar patches around it and computes difference vectors to enrich its own features, thereby enabling the differentiation of similar image patches. Considering geometric attributes are beneficial for distinguishing similar patches while semantic context is conducive to encoding regression issues, integrating multi-level features is an effective approach to elevate the localization accuracy. Therefore, ACAM concatenates multi-level features together and leverages both average pooling and max pooling to generate reliable channel-wise weighting coefficient, thereby modeling the correlation among channels to integrate multi-level features effectively. Comprehensive experiments are conducted on mainstream indoor localization benchmarks and an actual environment, showing that ALNet achieves impressive performance. Source code and the experimental results video are available at https://github.com/DAMMONGAO/alnet.

Stem–Leaf segmentation and phenotypic trait extraction of individual plant using a precise and efficient point cloud segmentation network

Rapid and precise 3D organ segmentation is crucial for the automatic extraction of phenotypic traits, forming a fundamental prerequisite for intelligent plant breeding. The advancement of deep learning technology has replaced labor-intensive manual measurements and traditional computer vision methods, which are sensitive to parameters, in phenotypic trait extraction. However, current larger network structures not only require extensive point cloud data but also consume substantial computational resources, rendering them unsuitable for agricultural tasks with limited plant samples. Therefore, this study developed a lightweight 3D deep learning network (PEPNet) that achieves precise plant organ segmentation and stem-leaf phenotypic trait extraction. The adopted simple-but-effective network architecture and innovative modern operations, including a high-dimensional feature mapping strategy for preprocessing input points, a local feature extraction module based on inverted residual bottleneck block, and a cost-free attention block for spatial feature fusion, effectively implement multi-scale hierarchies and adaptively reduce computational overheads. Experimental results from cotton stem-leaf segmentation demonstrated that PEPNet not only presented approximately 2 × faster inference speed (9.59 ms) and throughput (146.32 plants per second) but also achieved competitive segmentation performance compared to other six state-of-the-art deep learning networks, namely PointNet++, DGCNN, CurveNet, Point Cloud Transformer, PointMLP, and SPoTr, achieving 95.99 %, 94.66 %, 95.32 %, and 91.31 % in Precision, Recall, F1-score, and mIoU, respectively. In transferability experiments with tomato and soybean plants, PEPNet achieved almost all the best metrics and significantly outperformed the second-best model (CurveNet). Furthermore, ablation study verified the optimal trade-off between efficiency and accuracy in this network. Any modifications to the modules could potentially disrupt the optimal trade-off. This work could contribute to reducing computational resources and annotation costs for applying segmentation methods in high-throughput phenotyping tasks.

Multi-view orientational attention network combining point-based affinity for polyp segmentation

Most existing deep learning-based polyp segmentation methods neglect two important aspects of polyps: the geometric orientation information of polyps and the point information of the entire colonoscopy area. In this paper, we introduce a multi-view orientational attention network (MVOA-Net), which incorporates orientation and point awareness to effectively address the issue of intra-class inconsistency resulting from variations in polyp shape, size, and position, as well as the inter-class indistinction caused by the high similarity between polyp lesions and surrounding tissues. To achieve robust orientation awareness, we propose a novel geometric orientation transformer encoder (GOTE) based on horizontal and vertical views. Moreover, To simultaneously capture the global context information of GOTE and emphasize the important local information of the convolution-based attention encoder (CBAE), a global and local cross attention fusion module (CAFM) is also proposed to simultaneously model the long-range dependencies of polyps and pay sufficient attention to the local boundaries of polyps. Additionally, a efficient atrous spatial pyramid pooling (E-ASPP) module is proposed to enhance the semantic representation of high-level features. Finally, a point-based affinity module (PBAM) and a multi-scale fusion module (MSFM) are proposed to distinguish the disguise of polyps, further alleviating inter-class indistinction. The ablation study results demonstrate the effectiveness of each component. Quantitative and qualitative experimental results show that MVOA-Net achieves the best segmentation accuracy across domain polyp datasets and has obvious advantages in segmenting multiple polyp objects.

Coupled study on in-situ synchrotron high-energy X-ray diffraction and in-situ EBSD on the interfacial stress gradient in layered metals

As one of the heterostructures, the layered structure has attracted extensive research interest as it achieves superior properties to individual components. The layer interface is considered a critical factor in determining the mechanical properties of layered metals, where heterogeneity across the interface results in the strengthening of the soft layer and forming an interfacial stress gradient in the hard layer. However, there is still limited research associated with the formation of interfacial stress gradients in the hard layer, as stress measurement at high spatial resolution remains technically challenging. In the present study, we experimentally quantified the formation of interfacial stress gradients in the Ti layer of Ti/Al layered metal upon tension using in-situ high-energy X-ray diffraction (XRD). The analysis coupling in-situ high-energy XRD and in-situ electron back-scattered diffraction (EBSD) suggested that the interfacial stress gradient in the Ti layer rapidly rose as the Al layer was insufficient to accommodate the deformation of Ti. During the later deformation stage, collective effects of dislocation motion and geometrically necessary dislocation (GND) accumulation in the Al layer determined the evolution of interfacial stress gradients. The maximum interfacial stress gradient is below 0.4 MPa/μm in Ti layers, with a constant range width of 35 μm independent of the macroscopic strain. The present study therefore opens a new window to local stress modification using incompatible component deformation, which is instructive for the design and fabrication of high-performance layered metals.