Xiaofeng Zhang, Yihao Quan, Chen Shen, Chaochen Gu, Xiaosong Yuan, Shaotian Yan, Jiawei Cao, Hao Cheng, Kaijie Wu, Jieping Ye. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Polygon-based object representations efficiently model object boundaries but are limited by high optimization complexity, which hinders their adoption compared to more flexible pixel-based methods. In this paper, we introduce a novel vertex regression loss grounded in Fourier elliptic descriptors, which removes the need for rasterization or heuristic approximations and resolves ambiguities in boundary point assignment through frequency-domain matching. To advance polygon-based instance segmentation, we further propose EFDTR (\textbf{E}lliptical \textbf{F}ourier \textbf{D}escriptor \textbf{Tr}ansformer), an end-to-end learnable framework that leverages the expressiveness of Fourier-based representations. The model achieves precise contour predictions through a two-stage approach: the first stage predicts elliptical Fourier descriptors for global contour modeling, while the second stage refines contours for fine-grained accuracy. Experimental results on the COCO dataset show that EFDTR outperforms existing polygon-based methods, offering a promising alternative to pixel-based approaches. Code is available at \url{https://github.com/chrisclear3/EFDTR}.

Segment Anything Model (SAM), an advanced universal image segmentation model trained on an expansive visual dataset, has set a new benchmark in image segmentation and computer vision. However, it faced challenges when it came to distinguishing between shadows and their backgrounds. To address this, we proposed ShadClips, which consists of SAM-optimizer and SONet. It has dramatically enhanced SAM’s ability to segment shadow images, differentiating between the background and both soft and hard shadows adeptly. Due to its dependence on pixel point inputs, the SAM-Optimizer interface could do better. This method presents challenges, especially when dealing with long, extended shadows. To make the user experience more intuitive and effective, we incorporated the capabilities of CLIPs. Therefore, simple text descriptions like “A photo of a shadow” can be used to guide the SAM-Optimizer, allowing it to select the most relevant shadow mask from SAM’s comprehensive category list. Meanwhile, we introduce SONet to shadow removal. A large number of experiments on ISTD/SRD prove that the proposed method is effective and satisfactory. The source code of the ShadClips can be accessed from https://github.com/zhangbaijin/SAM-helps-Shadow .

Automatic diatom detection refers to the task of identifying and characterizing diatoms based on artificial intelligence. It will replace traditional time-consuming and laborious manual microscopy method of diatom observation to greatly accelerate the process of diatom research and some diatomrelated studies, such as diatom abundance statistics, using diatom properties for environmental monitoring and paleoenvironmental reconstruction. However, complex background interference and the detection of slender diatoms with different integrity are two major challenges for automatic diatom detection. To solve the mentioned-above issues, we propose an oriented object detector for automatic diatom detection based on RepPoints, called OOD-RepPoints. Specifically, for encouraging the network to adaptively capture the feature of slender diatoms, we design a cascaded feature refinement head (CFRH) which consists of points generation stage and points refinement stage, to progressively optimize the extraction of slender diatom features. Furthermore, to fit the shape of diatoms well, especially for slender diatoms, we propose a tailored label assignment strategy for our CFRH, which contains a short side assigner (SSA) for points generation stage and an adaptive IoU thresholds assigner (AITA) for points refinement stage. Besides, we contribute a so called O-Diatom dataset for automatic diatom detection. The dataset has 1711 images which contains 3949 diatoms and provides finely manual oriented bounding box annotations. Extensive experiments demonstrated our method achieve state of the art performance and can reach mAP 89.9% which is highest on O-Diatom, and shows competitive results on slender categories of publicly available datasets (i.e., DOTA and HRSC2016).

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at:https://github.com/TongkunGuan/RFN.

In recent years, cervical cancer has been one of the most common diseases in women's cancer. The advanced diagnosis of cervical precancerous lesions is essential for preventing cervical cancer. Its effectiveness and efficiency can be greatly improved by computer aided diagnosis, while challenged by the imprecise conclusions and uninterpretable process of diagnosis. To solve this problem, we propose a novel deep learning-based interpretable diagnosis system for pathology images, consisting of three interrelated models: an image model, an attention model and a conclusion model. Computer aided diagnosis improves the effectiveness and efficiency of the proposed image model uses a convolutional neural network (CNN) to ex-tract semantic features. Combining the model with the semantic attribute attention model, it aims to capture the discriminant relationship between se-mantic attributes by predicting the conclusion label through long-term and short-term memory (LSTM). The network is trained in an end-to-end manner, with different weights for each model. Experimental results on cervical intraepithelial neoplasia images, diagnostic reports and label datasets show that the proposed method achieves a significant improvement over traditional methods with a better interpretability.

Modern Industrial Internet-of-Things (IIoT) requires reliable interaction and integration of data in the network. Thus, time sensitive networking (TSN) is a promising technology due to deterministic latency guarantee mechanisms for data transmission. However, the multiple mechanisms are based on the networkwide precise time synchronization. In this paper, to achieve precise and reliable clock synchronization of TSN, we propose a hierarchical timing-frequency synchronization mechanism. Specifically, the network is divided into two layers according to the network clock synchronization function. The top layer adopts tree-based synchronization to provide a global clock and an interface with external standard clock synchronization, and the underlying one adopts a distributed synchronization protocol to improve the reliability, which obtains the reference clock by multiple nodes to avoid the single point of failure. To improve synchronization efficiency, a time-frequency fusion synchronization mechanism is proposed, which comprehensively considers the synchronization time slot difference and the time-frequency coupling relationship to improve the synchronization speed under the same synchronization period. Simulation results show that the proposed synchronization mechanism has higher synchronization efficiency than the traditional methods, and improves the clock synchronization speed and accuracy significantly.

As point cloud is a typical and significant type of geometric 3D data, deep learning on the classification and segmentation of point cloud has received widely interests recently. However, the critical problems to process the irregularity of point cloud and feature extraction of shape pattern have not yet been fully explored. In this paper, a geometric deep learning architecture based on our PointDoN module is presented. Inspired by the Difference of Normals (DoN) in traditional point clouds processing, our PointDoN module is a feature aggregation module combining DoN shape pattern descriptor with both 3D coordinates and extra features (such as RGB colors). Our PointDoN-based architecture can be flexibly applied to multiple point cloud processing tasks such as 3D shape classification and scene semantic segmentation. Experiments demonstrate that PointDoN model achieves state-of-the-art results on multiple types of challenging benchmark datasets.