Learning local features is a fundamental task for many computer vision applications. Existing methods often struggle to maintain robustness and accuracy in extracting local features, especially in complex environments with numerous interfering objects. Although some studies have integrated semantic information into local feature extraction networks to enhance discrimination, their effectiveness remains limited. Therefore, this paper fully considers the importance of semantic information for feature extraction and proposes a semantically enhanced local feature extraction network framework. This framework includes a local feature network, a semantic segmentation network, and a reinforcement learning framework. Semantic information is incorporated into feature heatmaps and feature descriptors to improve the accuracy of feature points. Subsequently, the local feature network is continuously optimized by a reinforcement learning algorithm based on semantic information and matching ground truth to enhance robustness, ensuring that the final local features achieve optimal performance. Extensive experiments on three publicly available datasets validate the effectiveness of the proposed local feature network.

The essence of the cross-view geo-localization task is to accurately identify the same object across images captured from different viewpoints. Due to variations in image acquisition methods and viewing angles, the content information of the images can differ significantly, which may result in localization failure. Therefore, cross-view geo-localization remains a challenging task. To solve this issue, a joint representation learning network based on feature center region diffusion and edge radiation is proposed in this article. First, to extract the crucial information from the global features, we design the central diffusion module that identifies important regions within the features and enhances feature robustness. Additionally, we design an edge radiation mechanism that expands the receptive field and further highlights crucial information in the image to support the central diffusion module in achieving more stable performance. On this basis, we propose an adaptive triple InfoNCE loss function to assist network training, improving the discriminability of the extracted features. Finally, the proposed network is tested on two mainstream datasets, and experimental results demonstrate that the proposed model outperforms the state-of-the-art methods, which can prove its effectiveness.

Learning-based local feature extraction algorithms have advanced considerably in terms of robustness. While excelling at enhancing feature robustness, some outstanding algorithms tend to neglect discriminability—a crucial aspect in vision tasks. With the increase of deep learning convolutional layers, we observe an amplification of semantic information within images, accompanied by a diminishing presence of spatial structural information. This imbalance primarily contributes to the subpar feature discriminability. Therefore, this paper introduces a novel network framework aimed at imbuing feature descriptors with robustness and discriminative power by reinforcing spatial structural information. Our approach incorporates a spatial structure enhancement module into the network architecture, spanning from shallow to deep layers, ensuring the retention of rich structural information in deeper layers, thereby enhancing discriminability. Finally, we evaluate our method, demonstrating superior performance in visual localization and feature-matching tasks.

Cross-view geo-localization refers to recognizing images of the same geographic target obtained from different platforms (such as drone-view, satellite-view and ground-view). However, cross-view geo-localization is challenging as image capture using different platforms coupled with extreme viewpoint variations can cause significant changes to the visual image content. Existing methods mainly focus on mining the fine-grained features or the contextual information in neighboring areas, but ignore the complete information of the entire image and the association of contextual information of adjacent regions. Therefore, a multi-branch joint representation learning network model based on information fusion strategies is proposed to solve this cross-view geo-localization problem. Firstly, we obtain feature information from the image through global information fusion branch and local information fusion branch to help the network learn the discernable information in the different images. In addition, a local-guided-global information fusion branch is introduced to make local information assist global features to enhance the learning of potential information in the images. Secondly, we introduced different information fusion strategies in each branch to increase the extraction of contextual information through expanding the global receptive field, thus improving the performance of the model. Finally, a series of experiments is carried out on four prevailing benchmark datasets, namely University-1652, SUES-200, CVUAS and CVACT datasets. The quantitative comparisons from the experiments clearly indicate that the proposed network framework has great performance. For example, compared with some state-of-the-art methods, the quantitative improvements of the R@1 and AP on the University-1652 datasets are 1.91%, 2.18% and 1.55%, 2.99% in both tasks, respectively.

Cross-view geo-localization refers to the task of matching the same geographic target using images obtained from different platforms, such as drone-view and satellite-view. However, the view angle of images obtained through different platforms will vary greatly, which can bring great challenges to the cross-view geo-localization task. Therefore, we propose a multi-level feedback joint representation learning network based on adaptive area elimination to solve the cross-view geo-localization problem. In our network model, we first process the extracted global features to obtain part-level and patch-level features. We then utilize these features as feedback to the global features to extract the contextual information in the global features and improve the robustness of the extracted features. In addition, as images obtained from different platforms differ, there will always be some interference when matching images. Therefore, we introduce an adaptive area elimination strategy to erase the interference information in the global features and assist the model in obtaining crucial information. On this basis, the feature correlation loss function is designed to constrain learning when using global feature information, thereby eliminating the possible interference, which can improve the network model performance. Finally, a series of experiments is carried out using two well-known benchmarks, namely University-1652 and SUES-200, and the experimental results show that the proposed network model achieves competitive results, thereby demonstrating the effectiveness of proposed model.

Due to seasonal and illumination variance, long-term visual localization tasks in dynamic environments is a crucial problem in the field of autonomous driving and robotics. At present, image-based retrieval is an effective method to solve this problem. However, it is difficult to completely distinguish changes in the same location over times by relying on content information alone. In order to solve these above problems, a double-domain network model combining semantic information and content information is proposed for visual localization task. In addition, this approach only needs to use the virtual KITTI 2 dataset for training. To reduce the domain difference between real scene and virtual image, the cross-predictive semantic segmentation mechanism is introduced to solve this problem. In addition, the obtained model achieves good domain adaptation and further has well generalization on other real datasets by introducing a domain loss function and a triplet semantic loss function. A series of experiments on the Extended CMU-Seasons dataset and the Oxford RobotCar-Seasons dataset demonstrates that the proposed network model outperformes the state-of-the-art baselines for retrieval-based visual localization in challenging environments.