A great deal of operational information exists in the form of text. Therefore, extracting operational information from unstructured military text is of great significance for assisting command decision making and operations. Military relation extraction is one of the main tasks of military information extraction, which aims at identifying the relation between two named entities from unstructured military texts. However, the traditional methods of extracting military relations cannot easily resolve problems such as inadequate manual features and inaccurate Chinese word segmentation in military fields, failing to make full use of symmetrical entity relations in military texts. With our approach, based on the pre-trained language model, we present a Chinese military relation extraction method, which combines the bi-directional gate recurrent unit (BiGRU) and multi-head attention mechanism (MHATT). More specifically, the conceptual foundation of our method lies in constructing an embedding layer and combining word embedding with position embedding, based on the pre-trained language model; the output vectors of BiGRU neural networks are symmetrically spliced to learn the semantic features of context, and they fuse the multi-head attention mechanism to improve the ability of expressing semantic information. On the military text corpus that we have built, we conduct extensive experiments. We demonstrate the superiority of our method over the traditional non-attention model, attention model, and improved attention model, and the comprehensive evaluation value F1-score of the model is improved by about 4%.

Rejecting outlier correspondences is one of the critical steps for successful feature-based two-view geometry estimation, and contingent heavily upon local context exploration. Recent advances focus on devising elaborate local context extractors whereas typically adopting explicit neighborhood relationship modeling at a specific scale, which is intrinsically flawed and inflexible, because 1) severe outliers often populated in putative correspondences and 2) the uncertainty in the distribution of inliers and outliers make the network incapable of capturing adequate and reliable local context from such neighborhoods, therefore resulting in the failure of pose estimation. This prospective study proposes a novel network called U-Match that has the flexibility to enable implicit local context awareness at multiple levels, naturally circumventing the aforementioned issues that plague most existing studies. Specifically, to aggregate multi-level local context implicitly, a hierarchy-aware graph representation module is designed to flexibly encode and decode hierarchical features. Moreover, considering that global context always works collaboratively with local context, an orthogonal local-and-global information fusion module is presented to integrate complementary local and global context in a redundancy-free manner, thus yielding compact feature representations to facilitate correspondence learning. Thorough experimentation across relative pose estimation, homography estimation, visual localization, and point cloud registration affirms U-Match's remarkable capabilities.

Accurately matching local features between a pair of images corresponding to the same 3D scene is a challenging computer vision task. Previous studies typically utilize attention-based graph neural networks (GNNs) with fully-connected graphs over keypoints within/across images for visual and geometric information reasoning. However, in the background of local feature matching, a significant number of keypoints are non-repeatable due to factors like occlusion and failure of the detector, and thus irrelevant for message passing. The connectivity with non-repeatable keypoints not only introduces redundancy, resulting in limited efficiency (quadratic computational complexity w.r.t . the keypoint number), but also interferes with the representation aggregation process, leading to limited accuracy. Aiming at the best of both worlds on accuracy and efficiency, we propose MaKeGNN, a sparse attention-based GNN architecture which bypasses non-repeatable keypoints and leverages matchable ones to guide compact and meaningful message passing. More specifically, our Bilateral Context-Aware Sampling (BCAS) Module first dynamically samples two small sets of well-distributed keypoints with high matchability scores from the image pair. Then, our Matchable Keypoint-Assisted Context Aggregation (MKACA) Module regards sampled informative keypoints as message bottlenecks and thus constrains each keypoint only to retrieve favorable contextual information from intra- and inter-matchable keypoints, evading the interference of irrelevant and redundant connectivity with non-repeatable ones. Furthermore, considering the potential noise in initial keypoints and sampled matchable ones, the MKACA module adopts a matchability-guided attentional aggregation operation for purer data-dependent context propagation. By these means, MaKeGNN outperforms the state-of-the-arts on multiple highly challenging benchmarks, while significantly reducing computational and memory complexity compared to typical attentional GNNs.

In two-view correspondence learning, prevalent multi-layer perceptron (MLP)-based methods struggle with context capturing. To remedy this issue, recent advances innovatively stack convolutional neural network (CNN)-based Resblocks sequentially, showing an inherent proficiency in local context extraction. Yet, such non-issue-specific designs inherit the drawback of CNN’s difficulty in aggregating global context, leading to performance bottlenecks. To address this problem, this prospective study further explores the potential of the CNN-based framework and proposes MC-Net, a top-performing network that integrates both local and global context elegantly and seamlessly. Specifically, considering that sparse motion vectors and a dense motion field can be converted into each other through interpolation and sampling, we first transform unordered matches into image-structured data by estimating the dense motion field implicitly. Then, we design a hierarchical rectifying module to rectify the error of each ordered motion vector with CNN at multiple levels, enabling MC-Net to perceive global context from coarse-level features and local context from fine-level features simultaneously, which facilitates to tackle the discontinuities of the motion field in case of large scene disparity. Finally, we reconstruct comprehensive context-embedded features from rectified motion fields at all levels. Also, instead of using the residuals between rectified and pre-rectified motion vectors at the same layer to reject outliers as in previous studies, which seriously affects the inlier prediction accuracy, we rethink this operation meticulously and modify it to the difference between motion vectors obtained from each layer’s reconstruction and ones from the first layer before transformation, ensuring purer residuals and enhancing the matching performance without extra computational burden. Extensive experiments show that MC-Net outperforms state-of-the-arts on multiple domains and datasets.

Existing two-view multi-model fitting methods typically follow a two-step manner, i.e ., model generation and selection, without considering their interaction. Therefore, in the first step, these methods have to generate a considerable number of instances in order to cover all desired ones, which not only offers no guarantees, but also introduces unnecessary expensive calculations. To address this challenge, this study presents a new algorithm, termed as D2Fitting, that incrementally explores dominant instances. Particularly, rather than viewing model generation and selection as two disjoint parts, D2Fitting fully considers their interaction, and thus performs these two subroutines alternatively under a simple yet effective optimization framework. This design can avoid generating too many redundant instances, thus reducing computational overhead and allowing the proposed D2Fitting being real-time. Meanwhile, we further design a novel density-guided sampler to sample high-quality minimal subsets during the model generation process, so as to fully exploit the spatial distribution of the input data. Also, to mitigate the influence of noise on the subsets sampled by the proposed sampler, a global-residual optimization strategy is investigated for the minimal subset refinement. With all the ingredients mentioned above, the proposed D2Fitting can accurately estimate the number and parameters of geometric models and efficiently segment the input data simultaneously. Extensive experiments on several public datasets demonstrate the significant superiority of D2Fitting over several state-of-the-arts.

We present a simple yet tough-to-beat method dubbed SGNNet, for correspondence learning. Instead of focusing on devising sophisticated geometric extractors to explore the global or local contextual information involving all sparse correspondences as most existing studies have done, which may be biased by heavy outliers, we propose to first delve into elaborate contextual information encoded in several specific reliable correspondences, and later leverage it to achieve per-correspondence representation updating. To this end, the proposed network contains three pivotal modules: 1) dynamic seeding module, which aims to dynamically sample a set of reliable matches from the putative set as seeds to guide the network learning; 2) intraseed attention module (ISAM), which intends to capture the geometrical relations among seed matches and further leverage them to enhance seed features; and 3) dynamic unseeding module, which is designed to sufficiently aggregate favorable contextual information from seed matches and broadcast it back to features of original matches. With all the aforementioned components, the proposed SGNNet is capable of rejecting outliers from putative correspondences effectively. Extensive experiments indicate that our method beats current solid baselines and sets new SOTA scores across multiple domains and datasets. Notably, SGNNet attains an AUC@ $5^{\circ}$ of 56.43% on YFCC100M without RANSAC, surpassing the most cutting-edge model by 4.51 absolute percentage points and exceeding the 55% AUC@ $5^{\circ}$ bar for the first time. Project page: https://github.com/ZizhuoLi/SGNNet.