Correspondence pruning is a core problem in computer vision, aiming to distinguish correct matches from a large set of initial correspondences. Recent Transformer-based methods, such as VSFormer, have shown remarkable performance by jointly modeling visual appearance and spatial relationships. Nevertheless, these methods often depend on abstract, scene-level visual representations, which are insufficiently precise for validating individual correspondences. In addition, their context aggregation modules often struggle to capture multi-granularity corresponding features, particularly under complex geometric variations. To overcome these limitations, we propose the Calibrated Context-aware Transformer with two key components. The Calibrated Visual Cues Extractor samples features directly at putative match locations to provide well-aligned, correspondence-specific visual cues. The Dual-Perspective Graph Transformer replaces conventional graph attention to better model both node-level and edge-level interactions. Together, these modules enable more robust local-global context fusion. Extensive experiments on YFCC100 M and SUN3D show that CCTformer consistently outperforms the baseline and other state-of-the-art methods.

Modeling and analyzing multiphysics effects has become one of the most challenging issues in integrated circuit design. Equivalent thermal circuit method is one of the most commonly used method in circuit design to simulate the electrothermal coupling effects, since it is fast and compatible with SPICE. However, it is still difficult to realize electro-thermalmechanical coupling simulation based on equivalent circuit method due to a lack of equivalent mechanical circuit method, which brings difficulties to do electro-thermal-mechanical analysis by SPICE. The equivalent mechanical circuit method is proposed based on solid mechanics equilibrium equation by deriving the electro-mechanical equivalent relation, equivalent circuit elements, equivalent circuit structure, equivalent circuit boundary condition, and the solving algorithm. The equivalent multiphysics circuit of TSV and FinFET are then further constructed to simulate the electro-thermal-mechanical coupling effects and verified with simulation results obtained from the finite element method (FEM). The results show that our proposed equivalent multiphysics circuit framework is able to simulate the electro-thermal-mechanical coupling effects by SPICE in integrated circuits.

Occluded person re-identification aims to address the identification challenges posed by pedestrians obscured by other individuals or objects. Existing methods often rely on incorporating pose or semantic information to improve model performance under occlusion. However, such information often depends on external models with inevitably cross-domain gaps, whose stability is limited in complex occlusion environments and prone to false results. In this paper, we propose a Transformer-based uncertainty-driven Gaussian model, termed as UD-Gaussian. Firstly, to enrich the detailed features of pedestrian images, a high-frequency enhancement module is introduced. The high-frequency components of the pedestrian image are extracted by Discrete Haar Wavelet Transform, and Top-K high-frequency patches are extracted to construct a graph Laplacian matrix to achieve high-frequency graph attention, which is fused with features learned from self-attention to enhance the high-frequency feature representation. Given the uncertainty in pedestrian feature learning induced by occlusion makes it challenging to obtain reliable and stable pedestrian features, we propose a probability distribution learning module. This module establishes a memory bank to build Gaussian distributions for each pedestrian identity and the entropy is introduced as a loss function to encourage the model to generate more deterministic and relatively independent probability distributions, thereby enhancing the discriminative ability of the model across different pedestrian identities. The high-frequency enhancement module provides a solid foundation for the probability distribution learning module, alleviating uncertainty caused by pedestrian images themselves. Experimental results on occluded and holistic person re-identification datasets demonstrate the superiority of the proposed method.

Accurately identifying correct correspondences in two images is a crucial task in computer vision. Current methods predominantly use PointCN blocks as feature extraction backbones and learn local-global consensus through a progressive learning strategy. However, such methods have two main drawbacks: First, PointCN blocks, composed of multilayer perceptrons and normalization layers, process spatial positions independently, leading to limited interaction between channel-wise and spatial-wise dimensions. Second, the progressive learning strategy primarily focuses on unidirectional transfer from local to global consensus, yet neglects the bidirectional interaction between local and global consensus. To address these issues, we propose the Channel-Spatial interaction and Bidirectional Consensus interaction-Based Network (CSBCNet), which contains three innovative blocks: Channel-Spatial Interaction (CSI), Local Consensus Mining (LCM), and Global Consensus-Aware Attention (GCAA). Specifically, CSI enhances interaction between channel-wise and spatial-wise dimensions through a dual-path attention mechanism, addressing the limited interaction caused by the independent processing of spatial positions in PointCN blocks. LCM extracts reliable local consensus by modeling geometric structures and spatial continuity within correspondences. GCAA captures global consensus by aggregating correspondences that are highly likely to be correct ones, and achieves bidirectional interaction between local and global consensus through cross attention. Experiments demonstrate our CSBCNet's superior performance in camera pose estimation and correspondence pruning. Notably, when the CSI block is applied to the existing OANet and MS2DGNet networks, it achieves significant performance improvements of 10.27% and 7.5%, respectively, on the mAP5° metric on the camera pose estimation task.

This paper presents a low-dropout regulator (LDO) with a 5-A current capability, supporting a wide range of output capacitors. An output impedance shaping technique is proposed, introducing two half-zeros into the loop to shape the LDO’s output impedance between resistive and inductive characteristics. The proposed technique ensures a decent frequency and transient response across an output capacitance range from 0 to 150 μF. To achieve a widely adjustable output voltage, a two-stage buffer with fast transient response and active feedback is employed, which further improves the overall stability and transient performance of the LDO. Under light-load conditions, the current sensing module provides an auxiliary feedback path for the error amplifier (EA), thereby securing loop stability by pushing the dominant pole to a higher frequency and lowering the DC loop gain. Implemented with a 0.18 μm CMOS process, the chip occupies an area of 1.9 × 1.9 mm2. The LDO regulates an output voltage from 0.8 to 3.5 V within an input voltage range of 2.8 to 5.5 V. With a minimum dropout voltage of 150 mV, the open-loop gain is 80 dB and remains comparatively unaffected by load alterations, ensuring a robust load regulation of 1.3 mV/A. Measured results demonstrate a 170-mV undershoot during a 5-A/1-μs load step without external output capacitors.

Correspondence pruning has recently drawn considerable attention as a crucial step in image matching. Existing methods typically achieve this by constructing neighborhoods for each feature point and imposing neighborhood consistency. However, the nearest-neighbor matching strategy often results in numerous many-to-one correspondences, thereby reducing the reliability of neighborhood information. Furthermore, the smoothness constraint fails in cases of large-scale rotations, leading to misjudgments. To address the above issues, this paper proposes a novel robust correspondence pruning method termed RoSe, which is based on rotation-invariant sequence-aware consensus. We formulate the correspondence pruning problem as a mathematical optimization problem and derive a closed-form solution. Specifically, we devise a rectified local neighborhood construction strategy that effectively enlarges the distribution between inliers and outliers. Meanwhile, to accommodate large-scale rotation, we propose a relative sequence-aware consistency as an alternative to existing smoothness constraints, which can better characterize the topological structure of inliers. Experimental results on image matching and registration tasks demonstrate the effectiveness of our method. Robustness analysis involving diverse feature descriptors and varying rotation degrees further showcases the efficacy of our method.

Synthetic aperture radar (SAR) is a vital tool for ship detection, as it acquires high-resolution remote sensing images when optical images cannot penetrate. However, two primary challenges confronting SAR ship detection are complex backgrounds with islands, clutter, and land, as well as diverse scales of ship targets, particularly small ones, leading to numerous missed detections and false alarms. To overcome these challenges, we propose a multi-granularity-aware network (MGA-Net). Specifically, we design a multi-granularity hybrid feature fusion module (MGHF2M) to extract more representative local detail and global semantic information, enhancing the model’s capability to represent ship features to adapt to complex backgrounds. In addition, we design a multi-granularity feature synergy enhancement module (MGFSEM), which uses depthwise separable convolutions with different kernel sizes to extract features at different granularities and retain the original features, significantly improving the model’s representation of ship features at different scales. Experimental results show that our MGA-Net achieves the highest mAP and F1-score, surpassing eight advanced methods on three public datasets.

Person re-identification (ReID) typically encounters varying degrees of occlusion in real-world scenarios. While previous methods have addressed this using handcrafted partitions or external cues, they often compromise semantic information or increase network complexity. In this paper, we propose a new method from a novel perspective, termed as OAT. Specifically, we first use a Transformer backbone with multiple class tokens for diverse pedestrian feature learning. Given that the self-attention mechanism in the Transformer solely focuses on low-level feature correlations, neglecting higher-order relations among different body parts or regions. Thus, we propose the Second-Order Attention (SOA) module to capture more comprehensive features. To address computational efficiency, we further derive approximation formulations for implementing second-order attention. Observing that the importance of semantics associated with different class tokens varies due to the uncertainty of the location and size of occlusion, we propose the Entropy Guided Fusion (EGF) module for multiple class tokens. By conducting uncertainty analysis on each class token, higher weights are assigned to those with lower information entropy, while lower weights are assigned to class tokens with higher entropy. The dynamic weight adjustment can mitigate the impact of occlusion-induced uncertainty on feature learning, thereby facilitating the acquisition of discriminative class token representations. Extensive experiments have been conducted on occluded and holistic person re-identification datasets, which demonstrate the effectiveness of our proposed method.

The success of most robust model estimation methods heavily relies on their used data sampling algorithms. This paper proposes a novel sampling algorithm, called Guided Sampling by Neighborhood Information and Matching Scores (NIMS), to efficiently sample promising hypotheses for fitting multi-structure data. Specifically, NIMS follows a specific sampling process. First, the proposed NIMS randomly selects a data point. Then, NIMS selects the neighbors of the selected data by using the neighborhood information to remove most of the outlier neighbors. Finally, NIMS samples a data subset using matching scores from the selected neighbors, which encourages NIMS to sample inliers from the selected neighbors. Experimental results on the publicly availableAdelaideRMFdataset demonstrate that the proposed NIMS outperforms several state-of-the-artsampling algorithms.

Robust model fitting is a critical technique for artificial intelligence. The performance of most robust model fitting techniques heavily depends on the use of sampling algorithms. In this paper, we propose an efficient guided sampling algorithm for multi-structure data by using the neighborhood consensus and the residual sorting. Specifically, a Neighborhood Consensus based Strategy (NCS) is first proposed to select the first datum (i.e., seed datum) of a minimal subset, and then a Residual Sorting based Strategy (RSS) samples the rest data of the minimal subset based on the seed datum. This strategy effectively combines the benefits of neighborhood consensus and residual sorting, where neighborhood consensus can judge whether a selected data point is an inlier, and residual sorting encourages this strategy to select data points from the same structure of the first selected data point. Moreover, to achieve better fitting performance, the Markov Chain Monte Carlo process is used to combine NCS with the random selection strategy to select the seed datum, and an appropriate size is set to the initial block of randomly sampled hypotheses for RSS. Experimental results on three vision tasks (e.g., two-view motion segmentation and 3D motion segmentation) demonstrate that the proposed algorithm achieves superior performance to several state-of-the-art sampling algorithms.

Establishing superior-quality correspondences in an image pair is pivotal to many subsequent computer vision tasks. Using Euclidean distance between correspondences to find neighbors and extract local information is a common strategy in previous works. However, most such works ignore similar sparse semantics information between two given images and cannot capture local topology among correspondences well. Therefore, to deal with the above problems, Multiple Sparse Semantics Dynamic Graph Network (MS2DG-Net) is proposed, in this paper, to predict probabilities of correspondences as inliers and recover camera poses. MS2 DG-Net dynamically builds sparse semantics graphs based on sparse semantics similarity between two given images, to capture local topology among correspondences, while maintaining permutation-equivariant. Extensive experiments prove that MS2 DG-Net outperforms state-of-the-art methods in outlier removal and camera pose estimation tasks on the public datasets with heavy outliers. Source code:https://github.com/changcaiyang/MS2DG-Net

In this letter, we propose a remote sensing image matching method that is simple yet efficient to deal with different deformations. Inspired by the region growing strategy used in image segmentation, we integrate the motion consistency into the general region growing pipeline from a novel perspective. Specifically, we first obtain a subset with a high ratio inlier as the seed correspondence set. Then, to find more reliable correspondences, we formulate the motion consistency into the correspondence growing criterion, which is general to be suitable to many remote sensing applications. Extensive experimental results on the public available remote sensing data set show that our method achieves the best performance compared with state-of-the-art methods.

This letter is concerned with constructing reliable neighborhoods for the local consistency-based feature matching methods. To alleviate the impact of outliers on neighborhood construction, we propose a rectified neighborhood construction strategy (RNC), which can effectively enlarge the distribution between inliers and outliers. Besides, we also integrate an adaptive parameter estimation into the aforementioned rectified strategy, and it can contribute to determining a reasonable parameter for the rectified strategy. Finally, the experimental results on two representative remote sensing image data sets show that the proposed method can achieve satisfactory feature matching results compared with some state-of-the-arts.

In this article, we present a feature-based remote sensing (RS) image matching method termed progressive motion coherence (PMC). We formulate the matching problem into a mathematical model and derive a closed-form solution. The objective function is only based on two novel coherence constraints, namely, efficient neighborhood element coherence and relative order-aware motion coherence, and hence, it is general enough and can be applied to RS image matching with different image types and degradations. The efficient neighborhood element coherence uses the Jaccard distance to measure the dissimilarity of two neighborhoods, which are lists composed of$k$nearest neighbors of feature points. To prevent overpenalization on the outliers, we combine it with an exponential function, which is simple yet efficient. The relative order-aware motion coherence is an alternative to motion smoothness, which is based on the observation that the relative order of neighboring matches for inliers in a small region can be well preserved, while for outliers, the relative order changes greatly. The above two coherences are robust to large rotation changes and low ratio inliers. Extensive experiments on five RS image datasets compared with seven state of the arts demonstrate that our PMC is more efficient and robust than the competitors.

This paper studies push recovery for humanoid robots based on a variable-height inverted pendulum (VHIP) model. We first develop an approach for treating zero-step capturability of the VHIP with a novel methodology based on Hamilton-Jacobi (HJ) reachability analysis. Such an approach uses the sub-zero level set of a value function to encode capturability of the VHIP, where the value function is obtained by numerically solving a HJ variational inequality offline. Based on this analysis, a simple and effective method for adjusting foothold locations is then devised for cases where the VHIP state is not zero-step capturable. In addition, the HJ reachability analysis naturally induces an optimal control law that allows for rapid planning with the VHIP during push recovery online. To enable use of the strategy with a position-controlled humanoid robot, an associated differential inverse kinematics based tracking controller is employed. The effectiveness of the overall framework is demonstrated with the UBTECH Walker robot in the MuJoCo simulator. Simulation validations show a significant improvement in push robustness as compared to the methods based on the classical linear inverted pendulum model.

Color and depth information provided simultaneously in RGB-D images can be used to segment scenes into disjoint regions. In this paper, a graph-based segmentation method for RGB-D image is proposed, in which an adaptive data-driven combination of color- and normal-variation is presented to construct dissimilarity between two adjacent pixels and a novel region merging threshold exploiting normal information in adjacent regions is proposed to control the proceeding of the region merging. We evaluate our method on the NYU-v2 depth database and compare it with several published RGB-D partition methods. The experimental results show that our method is comparable with the state-of-the-art methods and provides more details of structures in the scene.