Ruihan Chen, Qiming Li, Xiaocheng Feng, Weihong Zhong, Xiaoliang Yang, Yuxuan Gu, Zekun Zhou, Yunfei Lu, Haoyu Ren, Kun Chen, Dandan Tu, Bing Qin. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Weitao Ma, Xiyuan Du, Xiaocheng Feng, Lei Huang, Yichong Huang, Huiyi Zhang, Xiaoliang Yang, Baohang Li, Xiachong Feng, Ting Liu, Bing Qin. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Vehicle re-identification (re-ID) is to identify the same vehicle captured by different cameras with non-overlapping views, which plays an important role in intelligent transportation system and traffic safety. Compared with face recognition and person re-ID tasks, it is difficult to train an effective vehicle re-ID model since different vehicles of the same vehicle model, such as Mercedes-Benz C300, may exhibit strong inter-class similarity. To handle this difficulty, we propose a multi-grain ranking loss with the auxiliary of vehicle model, which models the vehicle re-ID task as two sub-tasks with different granularities including matching vehicles in the same vehicle model and different vehicle models. In particular, we infer the vehicle model labels online for the unlabeled training samples by clustering. The experimental results on two benchmarks demonstrate the proposed method can achieve state-of-the-art performance.

Vehicle re-identification (ReID) aims to identify a target vehicle in different cameras with non-overlapping views, and it plays an important role when the car licence plate recognition is unavailable or unreliable. Compared with face recognition and person ReID tasks, it is difficult to train an effective vehicle ReID model due to two reasons: the different views greatly affect the visual appearance of a vehicle, and different vehicles may exhibit fairly similar visual appearance when their images are captured from one unified single view. To handle these training difficulties, we introduce several latent groups to represent multiple views. Then, the vehicle ReID problem is modeled as two sub tasks, including matching vehicles in a same view and across different views. A fine-grain ranking loss and a relative coarse-grain ranking loss are proposed to each task respectively. Extensive experimental analyses and evaluations on two benchmarks demonstrate the proposed method can achieve state-of-the-art performance.

Summary Fingerprinting positioning is used for indoor location estimation because of low cost and open access properties, in which a radio map is built by calibrating signal‐strength values at several training locations in the offline phase. However, due to the sophisticated propagation of radio signals, the received signal strength (RSS) in wireless network change as the environment changes, and the radio map built in the offline phase may be out of date. Furthermore, the recalibration of signal‐strength values for each environment change is laborious and time consuming. In this paper, we present a novel algorithm to reconstruct a radio map using real‐time signal‐strength readings received at some reference points. We first demonstrate that different features of signal propagation are obtained in different regions of indoor environment. Then, the indoor environment is divided into several regions by clustering the path‐loss parameters of each reference point. In addition, the relationship between the RSS of reference points and calibration nodes is established with robust linear regression. Finally, the real‐time radio map is updated dynamically according to robust regression and the real‐time RSSs of the calibration nodes. The experimental results show the usefulness of the proposed method and the accuracy of the localization can be improved.

Automatic target recognition (ATR) of synthetic aperture radar (SAR) images is performed on either global or local features. The global features can be extracted and classified with high efficiency. However, they lack the reasoning capability thus can hardly work well under the extended operation conditions (EOCs). The local features are often more difficult to extract and classify but they provide reasoning capability for EOC target recognition. To combine the efficiency and robustness in an ATR system, a hierarchical fusion of the global and local features is proposed for SAR ATR in this paper. As the global features, the random projection features can be efficiently extracted and effectively classified by sparse representation-based classification (SRC). The physically relevant local descriptors, i.e., attributed scattering centers (ASCs), are employed for local reasoning to handle various EOCs like noise corruption, resolution variance, and partial occlusion. A one-to-one correspondence between the test and template ASC sets is built by the Hungarian algorithm. Then, the local reasoning is performed by evaluating individual matched pairs as well as the false alarms and missing alarms. For the test image to be recognized, it is first classified by the global classifier, i.e., SRC. Once a reliable decision is made, the whole recognition process terminates. When the decision is not reliable enough, it is passed to the local classifier, where a further classification by ASC matching is carried out. Therefore, by the hierarchical fusion strategy, the efficiency of global features and the robustness of local descriptors to various EOCs can be maintained jointly in the ATR system. Extensive experiments on the moving and stationary target acquisition and recognition data set demonstrate that the proposed method achieves superior effectiveness and robustness under both SOC and typical EOCs, i.e., noise corruption, resolution variance, and partial occlusion, compared with some other SAR ATR methods.

The quality of synthetic aperture radar (SAR) images and the completeness of the template database are two important factors in template-based SAR automatic target recognition. This letter gives a solution to the two factors by multilevel reconstruction of SAR targets using attributed scattering centers (ASCs). The ASCs of original SAR images are extracted to reconstruct the target's image, which not only reduces the noise and background clutters but also keeps the electromagnetic characteristics of the target. Template database are reconstructed at multilevels to simulate various extents of ASC absence in the extended operation conditions. Therefore, the quality of SAR images as well as the completeness of the template database is augmented. Features are extracted from the augmented SAR images, and the classifier is trained by the augmented database for target recognition. Experimental results on the moving and stationary target acquisition and recognition data set demonstrate the validity of the proposed method.

A high level of HER2 expression in breast cancer correlates with a higher tumor growth rate, high metastatic potential, and a poor long-term patient survival rate. Pertuzumab, a human monoclonal antibody, can reduce the effect of HER2 overexpression by preventing HER2 dimerization. In this study, a combination protocol of molecular dynamics modeling and MM/GBSA binding free energy calculations was applied to design peptides that interact with HER2 based on the HER2/pertuzumab crystal structure. Based on a β hairpin in pertuzumab from Glu46 to Lys65-which plays a key role in interacting with HER2-mutations were carried out in silico to improve the binding free energy of the hairpin that interacts with the Phe256-Lys314 of the HER2 protein. Combined the use of one-bead-one-compound library screening, among all the mutations, a peptide (58F63Y) with the lowest binding free energy was confirmed experimentally to have the highest affinity, and it may be used as a new probe in diagnosing and treating HER2-positive breast cancer.

Recognizing targets in synthetic aperture radar (SAR) images is an important, yet challenging problem in SAR image interpretation. In traditional methods, the 2-D image data is rearranged into vectors and regressed to its label by a vector where the structure information is lost. Multiple rank regression (MRR) method directly manipulation on matrix data by applying a multiple-rank left projecting vectors and right projecting vectors, and the matrix data can be regressed to its label for each category. In this paper, a multiple rank regression (MRR) based method is developed for the classification of SAR images. Firstly, SAR image samples are preprocessed to weak the nonideal factors, then train image samples are used to train the regression vectors for each class. In the test stage, the test image is regression by the trained vectors and is classified to the class with the highest regression label. Experimental results based on the moving and stationary target acquisition and recognition database verified the effectiveness of the proposed method.

The pose of a rotational symmetry target in wideband radar systems is the angle between its symmetry axis and the radar line of sight (LOS). It is one of the important parameters in target attitude measurement. Pose estimation has been applied in many fields, such as target tracking and target recognition. On the goal of estimating the target pose from the wideband radar measurements, a pose estimation algorithm based on the three-dimensional electromagnetic model (3-D em-model) is proposed. The 3-D em-model is an effective way to feature target's electromagnetic scattering behavior. It can recover radar echo of the target fast and accurately. The proposed algorithm utilizes the 3-D em-model of the target to generate high resolution range profiles and then matches the profiles generated by 3-D em-model with profile obtained from the high-resolution radar to estimate the pose information. Experiments using data predicted by a high-frequency electromagnetic code prove the effectiveness of the proposed algorithm.

In wideband stepped-frequency radar systems, relative motions between radar and target can induce high distortions of high-resolution range profiles (HRRPs), such as range migration, shape deformation, and signal-to-noise-ratio (SNR) loss. These distortions, if ignored, can lead to unacceptable performance deterioration in detection. To solve this problem, a new algorithm for detecting moving range-spread targets (RSTs) is proposed in this letter. The proposed detector utilizes the waveform contrast of the HRRP to perform both motion compensation and constant false-alarm rate target detection, and it is simple and robust even for low-SNR scenarios. Simulated experiments are carried out to verify the effectiveness and advantages of the proposed detector.

The pose of a rotational symmetry target in a wideband radar is the angle between its symmetry axis and the radar line of sight. It is a required parameter for target absolute attitude measurement using the bistatic radar measuring system. In this letter, a 3-D electromagnetic model (3-D EM model)-based method is proposed for estimating the target pose from fully polarimetric wideband radar measurements. The 3-D EM model is established offline based on the target geometric structure. Scattering features at different target poses can be accurately predicted by this model. In the proposed algorithm, a synthetic high-resolution range profile (HRRP), which is proved to be only related to the target pose, is first generated from the sum of echoes in radar copolarization channels. Then, by matching this synthetic HRRP with the synthetic HRRPs recovered by the 3-D EM model, the target pose is obtained. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in an anechoic chamber verify the validity of the method.

In this letter, a novel algorithm for attitude measurement based on a 3-D electromagnetic model (3-D em-model) is proposed. The 3-D em-model is established offline based on the geometric structure of the target, and it can be used to predict the scattering features at different target attitudes. In order to measure the attitude of the air target, we design a bistatic step frequency radar system. The directions of the two radars' lines of sight (LOSs) relative to the target are acquired by matching the high-resolution range profiles (HRRPs) from the target echoes to the HRRPs generated from the 3-D em-model. Since the directions of two radars' LOSs relative to the Earth are already known, the absolute attitude of the target can be acquired. The innovative contributions of this letter are as follows: 1) A comprehensive theoretical analysis of air target attitude measurement based on its own 3-D em-model is proposed; 2) the method can be applied to different kinds of air targets such as aircraft, satellite, missile, etc.; 3) the proposed attitude measurement method does not require target motion model in advance; and 4) the proposed algorithm can be applied to any kind of step frequency waveforms. Experiments using both data predicted by a high-frequency electromagnetic code and data measured in the chamber verify the validity of the method.