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11th International Conference on Language Resources and Evaluation (LREC)

作者： Wiedemann, Gregor Heyer, Gerhard Univ Leipzig Dept Comp Sci Leipzig Germany

ISBN: (纸本)9791095546009

In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications. As a first step, the workflow involves scanning and Optical Character Recognition (OCR) of documents. Preservation of document contexts of single page scans is a major requirement in this context. To facilitate workflows involving very large amounts of paper scans, page stream segmentation (PSS) is the task to automatically separate a stream of scanned images into multi-page documents. In a digitization project together with a German federal archive, we developed a novel approach based on convolutional neural networks (CNN) combining image and text features to achieve optimal document separation results. Evaluation shows that our PSS architecture achieves an accuracy up to 93 % which can be regarded as a new state-of-the-art for this task.

关键词： page stream segmentation convolutional neural nets document image classification document management text classification

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Bilingual Word Embeddings for Cross-Lingual Personality Recognition Using convolutional neural nets 18

Bilingual Word Embeddings for Cross-Lingual Personality Reco...

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18th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2017)

作者： Bin Siddique, Farhad Fung, Pascale Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Human Language Technol Ctr Clear Water Bay Hong Kong Peoples R China

ISBN: (纸本)9781510848764

We propose a multilingual personality classifier that uses text data from social media and Youtube Vlog transcriptions, and maps them into Big Five personality traits using a convolutional neural Network (CNN). We first train unsupervised bilingual word embeddings from an English-Chinese parallel corpus, and use these trained word representations as input to our CNN. This enables our model to yield relatively high cross-lingual and multilingual performance on Chinese texts, after training on the English dataset for example. We also train monolingual Chinese embeddings from a large Chinese text corpus and then train our CNN model on a Chinese dataset consisting of conversational dialogue labeled with personality. We achieve an average F-score of 66.1 in our multilingual task compared to 63.3 F-score in cross-lingual. and 63.2 F-score in the monolingual performance.

关键词： Computational Personality Recognition Big Five Multilingual Analysis convolutional neural nets

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Generating Transferable Adversarial Point Clouds via Autoencoders for 3D Object Classification

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IET COMPUTER VISION 2025年第1期19卷

作者： Xu, Mengyao Chen, Hai Zhang, Chonghao Zou, Yuanjun Xu, Chenchu Zhang, Yanping Qian, Fulan Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Anhui Univ Artificial Intelligence Inst Hefei Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China

Recent studies have shown that deep neural networks are vulnerable to adversarial attacks. In the field of 3D point cloud classification, transfer-based black-box attack strategies have been explored to address the challenge of limited knowledge about the model in practical scenarios. However, existing approaches typically rely excessively on network structure, resulting in poor transferability of the generated adversarial examples. To address the above problem, the authors propose AEattack, an adversarial attack method capable of generating highly transferable adversarial examples. Specifically, AEattack employs an autoencoder (AE) to extract features from the point cloud data and reconstruct the adversarial point cloud based on these features. Notably, the AE does not require pre-training, and its parameters are jointly optimised using a loss function during the process of generating adversarial point clouds. The method makes the generated adversarial point cloud not overly dependent on the network structure, but more concerned with the data distribution. Moreover, this design endows AEattack with a broader potential for application. Extensive experiments on the ModelNet40 dataset show that AEattack is capable of generating highly transferable adversarial point clouds, with up to 61.8% improvement in transferability compared to state-of-the-art adversarial attacks.

关键词： computer vision convolutional neural nets feature extraction object recognition

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Zero-shot moving ship segmentation based on segment anything network and optical flow network

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ELECTRONICS LETTERS 2025年第1期61卷

作者： Liu, Wenhui Qiao, Yulong Xing, Zhengyi Zhao, Yue Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

The wake of the ship and the waves on the water surface increased the difficulty of video-based ship segmentation. To address this challenging problem, we propose a zero-shot segmentation method based on the segment anything model (SAM) and optical flow network. Specifically, the dark channel filtering technology is employed as a preprocessing step to partly eliminate the noise from the sea surface. Then, the SAM with the point prompt is utilized to detect and segment ship targets in conjunction with finding the best mask algorithms, in which a supervision mechanism is introduced to determine the ship moving in or out of the surveillance region. Finally, we use the optical flow network to predict the position of the ship target and leverage the segmentation by using the network SAM with bounding boxes as prompts. The experiments on the benchmark dataset IPATCH demonstrate the performance of the proposed zero-shot moving ship segmentation method.

关键词： artificial intelligence computer vision convolutional neural nets image segmentation ships video signal processing

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TLEAR-Net: A Network for Defect Detection in Train Wheelset Treads Based on Transfer Learning and Edge Adaptive Reinforcement Attention

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IET IMAGE PROCESSING 2025年第1期19卷

作者： Hu, Xinliang He, Jing Zhang, Changfan Cheng, Xiang Chenzhou Vocat Tech Coll Sch Modern Equipment Mfg Chenzhou Hunan Peoples R China Hunan Univ Technol Sch Elect & Informat Engn Zhuzhou Hunan Peoples R China Hunan Railway Profess Technol Coll Dept Intelligent Control Zhuzhou Hunan Peoples R China

As a critical load-bearing and running component of railway systems, the wheelset's operational safety fundamentally depends on precise detection and localisation of tread defects. Current deep learning-based detection methods face significant challenges in extracting discriminative edge features under small-sample conditions, leading to suboptimal defect localisation accuracy. To address these limitations, this study proposes TLEAR-Net, a novel defect detection framework integrating transfer learning with an edge-adaptive reinforcement attention mechanism. The methodology employs RetinaNet as the baseline architecture, enhanced through multi-stage domain adaptation using COCO 2017 pretraining and parameter-shared ResNet-50 backbone optimisation to bridge cross-domain feature discrepancies. An innovative edge-adaptive reinforcement (EAR) attention module is developed to selectively amplify defect boundary features through learnable gradient operators and hybrid spatial-channel attention mechanisms. Comprehensive evaluations on a proprietary data set annotated defect samples demonstrate the framework's superior performance, achieving state-of-the-art detection accuracy (89.22% mAP) while maintaining real-time processing capability (42.45 FPS).

关键词： big data CCD image sensors convolutional neural nets crack detection decision making edge detection learning (artificial intelligence) neural net architecture non-destructive testing object detection

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UAV-Based Real-Time Object Detection Network Using Structured Pruning Strategy

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ELECTRONICS LETTERS 2025年第1期61卷

作者： Zhao, Donghui Mo, Bo Beijing Inst Technol Beijing Peoples R China

Real-time object detection networks based on UAV have been used in various fields. However, some challenges need to be solved: (1) Conventional detection algorithms are not suitable for small targets;(2) The computational capacity of the UAV platform is limited;(3) The sample distribution in the aerial dataset shows the characteristics of long-tail distribution. Categories at the tail end often need to be better learned. To address these challenges, we propose the AIR-YOLO-pruned method, a lightweight UAV-based object detection method built on the YOLOv8. In this paper, we propose the AIR-YOLO which is suitable for small object detection. We introduce the gradient adaptive allocation loss to enhance the model's learning ability for tail categories. To eliminate redundant components in AIR-YOLO, we design a kind of structured pruning strategy. Experiment results indicate that our AIR-YOLOn-pruned method, with competitive computational cost, achieves a 17% improvement in accuracy compared to YOLOv8n.

关键词： convolutional neural nets image and vision processing and display technology image matching image processing image recognition

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Channel selection and local attention transformer model for semantic segmentation on UAV remote sensing scene

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IET IMAGE PROCESSING 2025年第1期19卷

作者： Liu, Da Long, Hao Liu, Zhenbao Beijing Union Univ Beijing Key Lab Informat Serv Engn Beijing Peoples R China Beijing Union Univ Coll Robot Beijing Peoples R China Northwestern Polytech Univ Sch Civil Aviat Xian Peoples R China

Compared with common urban landscape semantic segmentation, unmanned aerial vehicle (UAV) image semantic segmentation is more challenging because small targets have very low pixel percentages and multi-scale features due to the influence of flight altitude. Yet, the commonly used successive grid downsampling strategy in the current transformer-based methods omits some important features of small targets. Furthermore, due to the complex background interference, it can lead to even worse results. In reaction to this, existing strategies aim to maintain superior resolution. Nevertheless, the application of this method incurs considerable computational costs, which brings challenges for the practical applications of UAVs. So it is significant to design a novel framework to balance retaining more pixels representing small objects during downsampling and reducing computational costs. For this, the Channel Selection and the Local Attention Transformer Model (CSLFormer) are proposed. During the overlap patch embedding process of feature maps, the model allocates half of the important channels to global attention and local attention. These two types of attention focus on different aspects: one learns the relationships and importance among various patches, while the other emphasizes the features of individual patches. The method shows superior performance on two public datasets: AeroScapes and Vaihingen, achieving mean intersection over union (mIoU) of 75.57% and 78.93%, respectively. The proposed CSLFormer has been released on GitHub: .

关键词： aircraft computer vision convolutional neural nets feedforward neural nets image segmentation

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Autoencoder-based unsupervised one-class learning for abnormal activity detection in egocentric videos

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IET COMPUTER VISION 2025年第1期19卷

作者： Hu, Haowen Hachiuma, Ryo Saito, Hideo Keio Univ Grad Sch Sci & Technol Yokohama Japan

In recent years, abnormal human activity detection has become an important research topic. However, most existing methods focus on detecting abnormal activities of pedestrians in surveillance videos;even those methods using egocentric videos deal with the activities of pedestrians around the camera wearer. In this paper, the authors present an unsupervised auto-encoder-based network trained by one-class learning that inputs RGB image sequences recorded by egocentric cameras to detect abnormal activities of the camera wearers themselves. To improve the performance of network, the authors introduce a 're-encoding' architecture and a regularisation loss function term, minimising the KL divergence between the distributions of features extracted by the first and second encoders. Unlike the common use of KL divergence loss to obtain a feature distribution close to an already-known distribution, the aim is to encourage the features extracted by the second encoder to have a close distribution to those extracted from the first encoder. The authors evaluate the proposed method on the Epic-Kitchens-55 dataset and conduct an ablation study to analyse the functions of different components. Experimental results demonstrate that the method outperforms the comparison methods in all cases and demonstrate the effectiveness of the proposed re-encoding architecture and the regularisation term.

关键词： cameras computer vision convolutional neural nets image reconstruction neural net architecture

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LCM-YOLO: A Small Object Detection Method for UAV Imagery Based on YOLOv5

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IET IMAGE PROCESSING 2025年第1期19卷

作者： Liu, Shaodong Shao, Faming Chu, Weijun Zhang, Heng Zhao, Dewei Xue, Jinhong Liu, Qing Army Engn Univ PLA Coll Field Engn Nanjing Peoples R China

This study addresses the challenges of detecting small targets and targets with significant scale variations in UAV aerial images. We propose an improved YOLOv5 model, named LCM-YOLO, to tackle these challenges. Initially, a local fusion mechanism is introduced into the C3 module, forming the C3-LFM module to enhance feature information acquisition during feature extraction. Subsequently, the CCFM is employed as the neck structure of the network, leveraging its lightweight convolution and cross-scale feature fusion characteristics to effectively improve the model's ability to integrate target features at different levels, thereby enhancing its adaptability to scale variations and detection performance for small targets. Additionally, a multi-head attention mechanism is integrated at the front end of the detection head, allowing the model to focus more on the detailed information of small targets through weight distribution. Experiments on the VisDrone2019 dataset show that LCM-YOLO has excellent detection capabilities. Compared to the original YOLOv5 model, its mAP50 and mAP50-95 metrics are improved by 7.2% and 5.1%, respectively, reaching 40.7% and 22.5%. This validates the effectiveness of the LCM-YOLO model for detecting small and multi-scale targets in complex backgrounds.

关键词： computer vision convolutional neural nets feature extraction image recognition

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SF-YOLO: A Novel YOLO Framework for Small Object Detection in Aerial Scenes

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IET IMAGE PROCESSING 2025年第1期19卷

作者： Sun, Meng Wang, Le Jiang, Wangyu Dharejo, Fayaz Ali Mao, Guojun Timofte, Radu Fujian Univ Technol Coll Comp Fujian Prov Key Lab Big Data Min & Applicat Fuzhou Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou Peoples R China Fujian Univ Technol Technol Innovat Ctr Factored Transact Data Tourist Minist Culture & Tourism Fuzhou Peoples R China Univ Wurzburg Comp Vis Lab CAIDAS Wurzburg Germany Univ Wurzburg IFI Wurzburg Germany

Object detection models are widely applied in the fields such as video surveillance and unmanned aerial vehicles to enable the identification and monitoring of various objects on a diversity of backgrounds. The general CNN-based object detectors primarily rely on downsampling and pooling operations, often struggling with small objects that have low resolution and failing to fully leverage contextual information that can differentiate objects from complex background. To address the problems, we propose a novel YOLO framework called SF-YOLO for small object detection. Firstly, we present a spatial information perception (SIP) module to extract contextual features for different objects through the integration of space to depth operation and large selective kernel module, which dynamically adjusts receptive field of the backbone and obtains the enhanced features for richer understanding of differentiation between objects and background. Furthermore, we design a novel multi-scale feature weighted fusion strategy, which performs weighted fusion on feature maps by combining fast normalized fusion method and CARAFE operation, accurately assessing the importance of each feature and enhancing the representation of small objects. The extensive experiments conducted on VisDrone2019, Tiny-Person and PESMOD datasets demonstrate that our proposed method enables comparable detection performance to state-of-the-art detectors.

关键词： computer vision convolutional neural nets convolution feature extraction object detection

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