Two-stage Matching Method Research Articles

VL-NMS: Breaking Proposal Bottlenecks in Two-stage Visual-language Matching

The prevailing framework for matching multimodal inputs is based on a two-stage process: (1) detecting proposals with an object detector and (2) matching text queries with proposals. Existing two-stage solutions mostly focus on the matching step. In this article, we argue that these methods overlook an obvious mismatch between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i.e., query-agnostic), hoping that the proposals contain all instances mentioned in the text query (i.e., query-aware). Due to this mismatch, chances are that proposals relevant to the text query are suppressed during the filtering process, which in turn bounds the matching performance. To this end, we propose VL-NMS, which is the first method to yield query-aware proposals at the first stage. VL-NMS regards all mentioned instances as critical objects and introduces a lightweight module to predict a score for aligning each proposal with a critical object. These scores can guide the NMS operation to filter out proposals irrelevant to the text query, increasing the recall of critical objects, and resulting in a significantly improved matching performance. Since VL-NMS is agnostic to the matching step, it can be easily integrated into any state-of-the-art two-stage matching method. We validate the effectiveness of VL-NMS on three multimodal matching tasks, namely referring expression grounding, phrase grounding, and image-text matching. Extensive ablation studies on several baselines and benchmarks consistently demonstrate the superiority of VL-NMS.

A Spatial Alignment Method for UAV LiDAR Strip Adjustment in Nonurban Scenes

LiDAR strip adjustment is a key prerequisite for subsequent applications based on point cloud data since it inevitably suffers from spatial discrepancies caused by laser ranging errors, mounting errors, etc. Most current LiDAR strip adjustment methods rely on the extraction of structural features which are often unsuitable for non-urban scenes. Alternative strip adjustment methods based on correspondence distance minimalization ignore spatial alignment. To overcome these limitations, this paper presents an accurate spatial alignment method for UAV LiDAR strip adjustment in non-urban scenes. Firstly, we construct a novel point cloud feature descriptor called Spherical Shell Point Feature (SSPF) to extract multi-dimensional non-structural features that are robust to non-urban point clouds. The constructed SSPF is then combined with point coordinates to generate embedded features, which simultaneously consider the point coordinates and spatial alignment. Finally, the embedded features are utilized by a two-stage matching method to match pair-wise points of two adjacent strips. The proposed method is validated on two non-urban datasets collected by two types of LiDARs, which reduces the digital surface model discrepancies by 0.252 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> and 0.221 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> , respectively, and proves its superiority compared to mainstream strip adjustment methods as well.

A Fast and Robust Heterologous Image Matching Method for Visual Geo-Localization of Low-Altitude UAVs

Visual geo-localization can achieve UAVs (Unmanned Aerial Vehicles) position during GNSS (Global Navigation Satellite System) denial or restriction. However, The performance of visual geo-localization is seriously impaired by illumination variation, different scales, viewpoint difference, spare texture, and computer power of UAVs, etc. In this paper, a fast detector-free two-stage matching method is proposed to improve the visual geo-localization of low-altitude UAVs. A detector-free matching method and perspective transformation module are incorporated into the coarse and fine matching stages to improve the robustness of the weak texture and viewpoint data. The minimum Euclidean distance is used to accelerate the coarse matching, and the coordinate regression based on DSNT (Differentiable Spatial to Numerical) transform is used to improve the fine matching accuracy respectively. The experimental results show that the average localization precision of the proposed method is 2.24 m, which is 0.33 m higher than that of the current typical matching methods. In addition, this method has obvious advantages in localization robustness and inference efficiency on Jetson Xavier NX, which completed to match and localize all images in the dataset while the localization frequency reached the best.

Two-stage matching decision-making method in medical service supply chain

ABSTRACT Medical service matching between suppliers and demanders should be emphasised. The supply chain promotes the development of practical matching decision-making methods. However, little is known about the general approaches used to explore matching problems in the context of medical service supply chain management. We put forward an effective two-stage matching decision-making method, including a grouping model and a matching model, that considers patient similarity and bilateral preferences. The grouping model is constructed based on the similarity matrix of patients. Next, specific rules are employed to calculate the integrated preference difference values between patients and doctors. The two-stage matching method considering bilateral preferences is then presented. Finally, a case study is conducted on an online health platform to verify the proposed method.

A novel two-stage method for matching the technology suppliers and demanders based on prospect theory and evidence theory under intuitionistic fuzzy environment

Matching the technology suppliers and demanders in the market is essential because such a matching facilitates the technology commercialization. Under the intuitionistic fuzzy environment, this paper proposes a novel two-stage matching method to match the technology suppliers and demanders by fully considering the characteristics of the real matching process. In the first stage, following the prospect theory and evidence theory, the satisfactions of the two parties with the attributes’ performances are calculated. These satisfactions are further used as the basis for the ranking of cooperation preferences. In the second stage, the price satisfaction under the influence of the cooperation preferences is considered, and the final matching pairs are obtained by maximizing the price satisfaction. We also conduct a numerical example to show the application of the novel two-stage method and compare its performance with the single-stage method The results show that the two-stage method can better identify both sides’ cooperation intentions and enhance their transaction satisfactions.

Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation

Fingerprinting acoustic localization usually requires tremendous time and effort for database construction in sampling phase and reference points (RPs) matching in positioning phase. To improve the efficiency of this acoustic localization process, an iterative interpolation method is proposed to reduce the initial RPs needed for the required positioning accuracy by generating virtual RPs in positioning phase. Meanwhile, a two-stage matching method based on cluster analysis is proposed for computation reduction of RPs matching. Results reported show that, on the premise of ensuring positioning accuracy, two-stage matching method based on feature clustering partition can reduce the average RPs matching amount to 30.14% of the global linear matching method taken. Meanwhile, the iterative interpolation method can guarantee the positioning accuracy with only 27.77% initial RPs of the traditional method needed.

An Efficient Parallel Approach for Sclera Vein Recognition

Sclera vein recognition is shown to be a promising method for human identification. However, its matching speed is slow, which could impact its application for real-time applications. To improve the matching efficiency, we proposed a new parallel sclera vein recognition method using a two-stage parallel approach for registration and matching. First, we designed a rotation- and scale-invariant Y shape descriptor based feature extraction method to efficiently eliminate most unlikely matches. Second, we developed a weighted polar line sclera descriptor structure to incorporate mask information to reduce GPU memory cost. Third, we designed a coarse-to-fine two-stage matching method. Finally, we developed a mapping scheme to map the subtasks to GPU processing units. The experimental results show that our proposed method can achieve dramatic processing speed improvement without compromising the recognition accuracy.

A Frame-Based Video Signature Method for Very Quick Video Identification and Location

A video signature is a set of feature vectors that compactly represents and uniquely characterizes one video clip from another for fast matching. To find a short duplicated region, the video signature must be robust against common video modifications and have a high discriminability. The matching method must be fast and be successful at finding locations. In this paper, a frame-based video signature that uses the spatial information and a two-stage matching method is presented. The proposed method is pair-wise independent and is robust against common video modifications. The proposed two-stage matching method is fast and works very well in finding locations. In addition, the proposed matching structure and strategy can distinguish a case in which a part of the query video matches a part of the target video. The proposed method is verified using video modified by the VCE7 experimental conditions found in MPEG-7. The proposed video signature method achieves a robustness of 88.7% under an independence condition of 5 parts per million with over 1,000 clips being matched per second.

A Frame-Based Video Signature Method for Very Quick Video Identification and Location

A video signature is a set of feature vectors that compactly represents and uniquely characterizes one video clip from another for fast matching. To find a short duplicated region, the video signature must be robust against common video modifications and have a high discriminability. The matching method must be fast and be successful at finding locations. In this paper, a frame-based video signature that uses the spatial information and a two-stage matching method is presented. The proposed method is pair-wise independent and is robust against common video modifications. The proposed two-stage matching method is fast and works very well in finding locations. In addition, the proposed matching structure and strategy can distinguish a case in which a part of the query video matches a part of the target video. The proposed method is verified using video modified by the VCE7 experimental conditions found in MPEG-7. The proposed video signature method achieves a robustness of 88.7% under an independence condition of 5 parts per million with over 1,000 clips being matched per second.