Correspondence pruning aims to identify inliers from an initial set of correspondences with a low inlier ratio. Current Graph Neural Networks (GNNs) based correspondence pruning approaches suffer from feature over-smoothing during information propagation, making it difficult to distinguish inliers from outliers. In addition, Transformer-based methods can model long-range dependencies, but their quadratic complexity limits computational efficiency. To address these issues, we propose MatchMamba, a dual-view correspondence pruning network based on a selective state space model, Mamba. MatchMamba combines the strengths of GNNs and Mamba, enhancing local feature extraction while modeling global context with appropriate complexity. Specifically, to overcome Mamba’s limitations in correspondence pruning, such as the lack of local context and unidirectional modeling, we introduce the Cluster Sampling Spatial Mamba (CSSM) block and Correspondence Flip Bidirectional Mamba (CFBM) block. CSSM captures fine-grained local context through the implicit soft assignment and mitigates GNN’s over-smoothing using Mamba’s selective mechanism. CFBM block leverages Mamba’s efficient long-sequence modeling by constructing a pseudo-sequential structure through clustering. It applies forward and backward scanning to enable each correspondence to fully capture contextual information from others, achieving global context modeling with appropriate computational cost. Extensive experiments demonstrate that MatchMamba outperforms current state-of-the-art methods on several challenging tasks. The code is available at https://github.com/Mrwyb/MatchMamba.

The accuracy and stability of pathology image segmentation have become critical factors in clinical applications such as cancer screening and tumor grading. However, the presence of complex local structures, uncertain regions, and subtle morphological variations in pathological images continues to pose significant challenges. Most existing feature fusion approaches rely on the simplistic aggregation of extracted features, neglecting the unique characteristics and relative importance of distinct feature representations, which ultimately limits their potential to enhance model performance. To address these issues, we propose a Gestalt-Inspired Feature Integration Network (GeNet), a novel architecture inspired by Gestalt theory that mirrors the human visual system's ability to derive holistic understanding from partial information. Embracing the principle that 'the whole is greater than the sum of its parts,' GeNet introduces a mechanism to synergistically leverage multi-scale information, which assesses the similarity between features to achieve a more meaningful fusion of global context and local detail. Given the variability in target appearance within pathological images, we use information entropy to quantify feature uncertainty, allowing the model to prioritize uncertain regions and reduce the occurrence of ambiguous results. To explicitly eliminate multi-feature redundancy and misalignment, the refinement block utilizes parallel convolutional recalibration to fully leverage the advantages of various features. Extensive experiments on multiple pathological image segmentation datasets, including GlaS, GCaSeg, and EBHI-Seg, demonstrate that GeNet achieves high accuracy and strong robustness, offering a new perspective for joint modeling of global and local features in medical image analysis.

Whole slide image (WSI) classification is a critical task in computational pathology and is aimed at providing automated diagnostic support through high-resolution tissue image analysis. In weakly supervised WSI classification scenarios, the main challenge concerns the traditional multiple instance learning (MIL) methods, which rely on instance-level embeddings aggregated by an attention-based pooling mechanism. These methods often depend on data-driven statistical correlations, leading to misalignments between their attention allocation schemes and histopathological diagnostic regions and reducing the resulting prediction reliability. To address this, we propose frequency-aware causal regularized multiple instance learning (FC-MIL), an innovative framework combining that combines frequency-aware attention (FAA) and causal regularization (CR). FAA extracts more granular, fine-grained histological textures by jointly modeling spatial- and frequency- domain features, whereas CR introduces feature-level counterfactual perturbations as an intervention-inspired regularizer in the latent space, encouraging the model to rely less on spurious correlations and more on invariant pathological cues. Experimental results obtained on four WSI datasets show that FC-MIL outperforms the state-of-the-art MIL methods in terms of both accuracy and interpretability. Our source code is available at https://github.com/7FFDW/FCMIL.

Accurate lung inflammation segmentation is essential for clinical decision-making, yet remains challenging due to the large variability in lesion appearance and location across different lung regions. Existing CNN-based models excel at local feature extraction, but they struggle to capture long-range dependencies and complex spatial relationships, such as those between the left and right lung lobes. Transformer-based models, while effective in modeling long-range dependencies, incur high computational costs and often fail to capture irregular anatomical relationships due to their reliance on Euclidean positional encodings. To overcome these challenges, we propose a novel Channel-Region Adaptive Unet (CRA-Unet) for accurate lung inflammation segmentation. Specifically, we design a Channel-Region Adaptive (CRA) layer that expands the recalibration process of the Squeeze-Excitation layer to include not only the channel dimension but also the height and width dimensions, enabling dynamical element-wise feature adjustment within different regions of interest across all three dimensions—channel, height, and width. Additionally, we propose a region-adaptive positional encoding strategy that learns dynamic weights for spatial locations, allowing the model to capture both intra-region and inter-region spatial relationships. Unlike traditional Euclidean positional encodings, which assume regular and grid-like spatial structures, our strategy can adapt to the irregular and asymmetric spatial relationships commonly found in anatomical structures such as the lungs. Experimental results on several datasets demonstrate that our CRA-Unet achieves state-of-the-art segmentation performance while maintaining high computational efficiency.

Establishing reliable correspondences is a fundamental task in computer vision. Constructing neighbor graphs in feature space with position information to mine correspondence consistency has become a common strategy for recognizing correct correspondences (inliers). However, these neighbors may include a high ratio of incorrect correspondences (outliers), only using the correspondence consistency from feature space will probably be difficult to guarantee the matching accuracy. To address this issue, we propose a novel motion consistent space to find consistent neighbors that are independent of the correspondence's position and have a larger search range. On top of that, we build two neighbor graphs according to the feature space and motion consistent space separately, and expand a shift annular convolution to retain rich neighbor graph structure information and fully exploit the neighborhood context. Then, we design a dynamic feature fusion block to dynamically fuse these dual-neighbor graphs to flexibly cope with various complex scenarios. Finally, we develop a Dual-Neighborhoods Dynamic Fusion Network (DDFNet) for accurately identifying inliers and retrieving camera poses. Experimental results demonstrate that our proposed DDFNet outperforms the state-of-the-art methods. Source code:https://github.com/1211193023/DDFNet.

The accurate identification of high-quality correspondences is a prerequisite task in feature-based point cloud registration. However, it is extremely challenging to handle the fusion of local and global features due to feature redundancy and complex spatial relationships. Given that Gestalt principles provide key advantages in analyzing local and global relationships, we propose a novel Gestalt-guided Parallel Interaction Network via orthogonal geometric consistency (GPI-Net) in this paper. It utilizes Gestalt principles to facilitate complementary communication between local and global information. Specifically, we introduce an orthogonal integration strategy to optimally reduce redundant information and generate a more compact global structure for high-quality correspondences. To capture geometric features in correspondences, we leverage a Gestalt Feature Attention (GFA) block through a hybrid utilization of self-attention and cross-attention mechanisms. Furthermore, to facilitate the integration of local detail information into the global structure, we design an innovative Dual-path Multi-Granularity parallel interaction aggregation (DMG) block to promote information exchange across different granularities. Extensive experiments on various challenging tasks demonstrate the superior performance of our proposed GPI-Net in comparison to existing methods. The code will be released at https://github.com/XXX/GPI-Net.

Object detection in scenes captured by unmanned aerial vehicles (UAV) is an active research area. However, the performance and efficiency of current small object detection models for UAV images are far from reaching the desired level. The inherent limitations of the features of the small objects themselves and the inconsistency of the contextual information in the feature maps lead to a degradation of the final detection performance. In this letter, to improve the performance of UAV image small object detection, we propose a multi-scale feature optimization network, named MFO-Net. We have designed three crucial modules: feature optimization fusion (FOF) module, multi-scale localized feature aggregation (MLFA) module, and feature enhancement (FE) module. FOF module enhances the fusion of features with inconsistent contexts at different levels by learning pixel-wise displacement, facilitating more effective feature fusion, which further helps focus on and capture critical information about small objects. MLFA module aggregates richer contextual information through multi-branch stripe convolution blocks, while the FE module extracts richer gradient flow information, suppresses incompatible information, and enhances feature representation capability. We conduct extensive experiments on the challenging VisDrone2019 dataset and compare the results against those obtained from the state-of-the-art methods. The experimental results show that MFO-Net performs better than other detectors. Specifically, MFO-Net achieves the best performance with 22.3% AP, 38.9% AP50, and 22.5% AP75on VisDrone2019. Code: https://github.com/Lanziyang121/MFO-Net.

Frame deletion detection is a challenging task in the field of digital video forensics. This paper proposes a deep-learning-based frame deletion detection method for single-shot videos. We capture traces of frame deletion forgery from both adjacent and long-range continuous frames. Specifically, we propose a novel multi-scale difference convolutional neural network (MSDCNN) structure, which models different levels of inter-frame variations. Then, we use the long-short-term memory network (LSTM) to capture the long-term variation pattern of multi-scale differential features. The proposed method is a simple and principled frame deletion detection framework with a small computational cost. According to the experiments, the proposed framework can achieve a more advanced performance of frame deletion detection than traditional methods and methods based on 3D convolutions.

Accurately identifying correct correspondences (inliers) in two-view images is a fundamental task in computer vision. Recent studies usually adopt Graph Neural Networks or stack local graphs into global ones to establish neighborhood relations. However, the smoothing properties of Graph Convolutional Neural network (GCN) cause the model to fall into local extreme, which leads to the issue of indistinguishability between inliers and outliers. Especially when the initial correspondences contain a large number of incorrect correspondences (outliers), these studies suffer from severe performance degradation. To address the above issues and refocus perspective information on distinct features, we design a Consistency Guided ResFormer Network (CGR-Net) that uses consistent correspondences to guide model perspective focusing, thereby avoiding the negative impact of outliers. Specifically, we design an efficient Graph Score Calculation module, which aims to compute global graph scores by enhancing the representation of important features and comprehensively capturing the contextual relationships between correspondences. Then, we propose a Consistency Guided Correspondences Selection module to dynamically fuse global graph scores and consistency graphs and construct a novel consistency matrix to accurately recognize inliers. Extensive experiments on various challenging tasks demonstrate that our CGR-Net outperforms state-of-the-art methods. Our code is released athttps://github.com/XiaojieLi11/CGR-Net.

Multi-objective segmentation (MOS) in medical images is to simultaneously extract multiple regions of interest in the medical images. Due to the unbalanced distribution of samples and the similarity and significant differences between features in medical images, current methods still struggle to achieve satisfactory results. In this context, we propose a novel Morphological Guided Causal Constrain segmentation network (MCCSeg) for medical image multi-object segmentation. We introduced a Causal Constrain Module (CCM) for feature decorrelation by sample reweighting. The morphological guidance module (MG) is designed to extract the boundary features as the prior shape information for enhancing feature representation. Our experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, obtaining up 3.76% and 5.41% improvements in DICE and HD95 scores on Synapse dataset, respectively.

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.

Building high-quality correspondences is critical in the feature-based point cloud registration pipelines. However, existing single-sequence learning frameworks are difficult to accurately and adequately capture contextual information, leaving a large proportion of outliers between two low-overlap scenes. In this paper, we present a progressive guidance network (PG-Net) to gather rich contextual information and exclude outliers. Specifically, we design a novel iterative structure that exploits the inlier probabilities of correspondences to guide the classification of initial correspondences progressively. This structure can mitigate outlier effects with robust contextual information to obtain more accurate model estimation. In addition, to sufficiently capture contextual information, we propose a grouped dense fusion attention feature embedding module to enhance the representation of inliers and significant channel-spatial. Meanwhile, we propose a two-stage neural spectral matching module to compute the inlier probability of each correspondence and estimate a 3D transformation model in a coarse-to-fine manner. Experiments results on indoor and outdoor datasets using distinct 3D local descriptors demonstrate that our PG-Net surpasses state-of-the-art outlier removal methods. Especially compared to the recent outlier removal network PointDSC, our PG-Net improves the registration recall by 4.06% on the indoor dataset with the FPFH descriptor. Source code: https://github.com/changcaiyang/PG-Net.

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.

Segmenting organs at risk from head and neck CT images is a prerequisite for the treatment of head and neck cancer using intensity modulated radiotherapy. However, accurate and automatic segmentation of organs at risk is a challenging task due to the low contrast of soft tissue and image artifact in CT images. Shape priors have been proved effective in addressing this challenging task. However, conventional methods incorporating shape priors often suffer from sensitivity to shape initialization and also shape variations across individuals. In this paper, we propose a novel approach to incorporate shape priors into a hierarchical learning-based model. The contributions of our proposed approach are as follows: 1) a novel mechanism for critical vertices identification is proposed to identify vertices with distinctive appearances and strong consistency across different subjects; 2) a new strategy of hierarchical vertex regression is also used to gradually locate more vertices with the guidance of previously located vertices; and 3) an innovative framework of joint shape and appearance learning is further developed to capture salient shape and appearance features simultaneously. Using these innovative strategies, our proposed approach can essentially overcome drawbacks of the conventional shape-based segmentation methods. Experimental results show that our approach can achieve much better results than state-of-the-art methods.

Non-rigid point set registration is a fundamental problem for many computer vision technologies. In this paper, we proposed a new non-rigid point set registration method based on coherent spatial mapping (CSM) and local geometrical constraint. Our central idea is to express each point as a weighted sum of several nearest neighbors and the same relation holds after the transformation. The registration problem is solved by minimizing an error function, which combines the the global model and local geometrical constraint. The registration experiments are undertaken on various synthetic and real data. The results demonstrate that the proposed approach is robust and is superior to the state-of-the-art methods.