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共 27256 条记录，以下是291-300 订阅

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MamTrack: Vision-language Tracking with Mamba Fusion 24

MamTrack: Vision-Language Tracking with Mamba Fusion

引用

8th international conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Chen, Donghua Zhang, Hong Song, Jianbo Feng, Yachun Yang, Yifan Image Processing Center Beihang University Beijing China School of Mechanical Engineering & Automation Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China

ISBN: (纸本)9798400718182

Vision-language tracking models aim to improve target tracking performance by fusing visual features and language description of the target, making it more useful and robust for a wider range of applications. Transformer excel at learning global information but are hindered by their quadratic complexity. The recently proposed State Space Model (SSM), Mamba, offers a promising solution to this issue by enabling global awareness with linear complexity. We consider the vision-language tracking task as a sequence generation task of multimodal tokens and propose an efficient vision-language tracking method, MamTrack. We extend the Mamba block and design an adaptive fusion strategy for Mamba’s different scanning directions to reduce channel redundancy. We introduce the CLIP language model to update text descriptions in real-time, enhancing the robustness of language descriptions in the tracking task. We compare our method with other state-of-the-art approaches on the TNL2K, LaSOT, and OTB99-Lang datasets, where our method achieves leading performance. © 2024 Copyright held by the owner/author(s).

关键词： Visual languages

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WTConv-Adapter: Wavelet Transform Convolution Adapter for Parameter Efficient Transfer Learning of Object Detection ConvNets 8

WTConv-Adapter: Wavelet Transform Convolution Adapter for Pa...

引用

8th international conference on Video and Image processing, ICVIP 2024

作者： Jia, Guanwei Xu, Lizhi Zhu, Liqiang Science and Technology Department Guoneng Shuohuang Railway Development Co. Ltd. Cangzhou062350 China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

In the field of natural language processing, utilizing large-scale pre-trained foundational models for parameter-efficient transfer has become a standard paradigm to enhance training efficiency and model performance. However, its application in computer vision, particularly in object detection tasks, remains to be further explored. This study introduces WTConv-Adapter, a parameter-efficient transfer module for convolutional networks in object detection. The module integrates traditional convolution with wavelet transform convolution, achieving a lightweight design while maintaining a large receptive field and multi-frequency response capabilities. Experiments on object detection under complex lighting conditions demonstrate the significant advantages of introducing wavelet convolution in parallel, leading to improved detection performance. Additionally, its transferability was tested and compared with backbone freezing, full fine-tuning, and Conv-Adapter, showing favorable results in both parameter efficiency and performance (achieving a 1.8% performance improvement over full fine-tuning with fewer parameters and training iterations). Moreover, its performance in the presence of pre-processing networks was evaluated, revealing strong adaptability to changes in input feature distributions. © 2025 SPIE.

关键词： Wavelet transforms

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Camouflaged Object Detection with CNN-Transformer Harmonization and Calibration

Camouflaged Object Detection with CNN-Transformer Harmonizat...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhao, Yilin Zhang, Qing Li, Yuetong College of Information Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350368741

Camouflaged object detection (COD) aims to segment objects that visually blend into their surroundings. However, the subtle differences between camouflaged objects and the background make this task highly challenging. Therefore, how to represent and learn local details and global contexts is crucial for improving detection performance. In this paper, we propose a novel COD network which synergistically leverages the distinct but complementary local and global knowledge to capture the camouflaged objects and identify imperceptible boundaries. Specifically, we design a Feature Coherence Harmonization module to integrate intra-layer features by bridging the knowledge gap between convolutional neural network (CNN) features, which focus on local patterns, and Transformer features, which capture global relationships. Furthermore, we propose a Cross-layer Feature Calibration Module that adaptively aligns inter-layer features, progressively aggregating diverse information to achieve an accurate prediction. Experimental results on COD benchmark datasets demonstrate that the proposed network significantly outperforms state-of-the-art approaches. © 2025 IEEE.

关键词： Camouflaged object detection inter-layer aggregation intra-layer integration

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Medical Prescription Generator using natural language processing 3

Medical Prescription Generator using Natural Language Proces...

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Paithankar, Siddhesh Patil, Snehal Shivgan, Ashlesha Mahabudhe, Mandar Dharmadhikari, Pramod MIT Academy of Engineering School of Computer Engineering Pune India MIT Academy of Engineering School of Electrical Engineering Pune India

ISBN: (纸本)9798331523893

With the increasing population in India and the limited availability of medical professionals, time efficiency in diagnosis and prescription generation has become crucial. This paper proposes an automated system leveraging natural language processing (NLP) and natural language Generation (NLG) to assist doctors in generating patient history and prescription suggestions. The system utilizes previously typed words to predict the next word, drawing on a medical notes dataset tailored to the doctor's specialization. By giving keywords or short descriptions about a patient's condition, the system processes the input using NLP for contextual under-standing and employs NLG to generate detailed histories and prescriptions in a standardized format. This dual approach ensures that the output aligns with medical conventions while maintaining accuracy and efficiency. The system is trained on extensive medical datasets, optimizing its predictions over time through machine learning techniques. Our solution not only enhances accuracy but also reduces the time doctors spend on manual documentation, improving overall efficiency and patient care. © 2025 IEEE.

关键词： healthcare automation machine learning medical diagnostics NLG NLP prescription generation

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Transformer Based Classification of Closely Related Dravidian languages in Code-Mixed Data

Transformer Based Classification of Closely Related Dravidia...

引用

2025 international conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Saji, Anandhu Shamsudeen, Fousia M Centre for Artificial Intelligence Tkm College of Engineering Kerala Kollam India Tkm College of Engineering Dept. of Computer Application Kerala Kollam India

ISBN: (纸本)9798331507541

In recent years, code mixing, the blending of words and phrases from multiple languages within a single conversation has become increasingly common in multilingual contexts, particularly in India. This work investigates the effectiveness of transformer based models in identifying languages within code mixed data that includes English and three closely related Dravidian languages: Malayalam, Kannada, and Tamil, all written in English script. A comprehensive dataset was developed with five distinct labels: Tn-en (Tamil-English), Kn-en (Kannada-English), Ml-en (Malayalam-English), and En (English). Transformer models, including BERT, CamemBERT, mBERT, DistilBERT, and ELECTRA, were employed for classification, with CamemBERT achieving the highest validation accuracy of 97.62% due to its ability to handle morphologically rich languages effectively. The findings highlight the potential of transformer models in enhancing natural language processing (NLP) applications for code-mixed text, particularly in managing the complexities of closely related multilingual environments. © 2025 IEEE.

关键词： natural language processing systems

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Distill To Detect: Amplifying Anomalies in Backdoor Models through knowledge Distillation

Distill To Detect: Amplifying Anomalies in Backdoor Models t...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Hu, Chang Teng, Xuyang Xing, Wenpeng Chen, Han Ye, Chenhao Han, Meng School of Communication Engineering Hangzhou Dianzi University Hangzhou China Binjiang Institute of Zhejiang University Zhejiang University Hangzhou China GenTel.io Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

Backdoor attacks represent a significant threat to the security of deep learning models. Due to the stealthiness of backdoor attacks, effectively detecting whether a model has been compromised by such attacks remains a major challenge. Previous backdoor detection methods either rely on backdoor datasets to identify anomalies in backdoor samples or depend on prior knowledge of existing backdoor attacks. This results in difficulties in detecting backdoors when backdoor samples are unavailable and leads to poor generalization capabilities when addressing new attack methods. This work proposes a novel approach for detecting backdoor attacks called Distill To Detect (D2D), that does not depend on backdoor samples or any prior knowledge. It utilizes knowledge distillation to amplify more general and universal backdoor anomalies exhibited on clean samples for detection. This approach is not only more efficient but also exhibits strong generalization capabilities, enabling the detection of most backdoor attacks with low time and computational costs. We tested our approach against six backdoor attacks and three different model architectures, demonstrating the effectiveness of our proposed method. © 2025 IEEE.

关键词： AI security backdoor attack backdoor defense deep learning knowledge distillation

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ROUGE-SciQFS: A ROUGE-based Method to Automatically Create Datasets for Scientific Query-Focused Summarization 31

ROUGE-SciQFS: A ROUGE-based Method to Automatically Create D...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Ramirez-Orta, Juan Milios, Evangelos Maguitman, Ana Soto, Axel J. Faculty of Computer Science Dalhousie University Canada Institute for Computer Science and Engineering UNS - CONICET Department of Computer Science and Engineering Universidad Nacional del Sur Argentina

ISBN: (纸本)9798891761964

So far, the task of Scientific Query-Focused Summarization (Sci-QFS) has lagged in development when compared to other areas of Scientific natural language processing because of the lack of data. In this work, we propose a methodology to take advantage of existing collections of academic papers to obtain large-scale datasets for this task automatically. After applying it to the papers from our reading group, we introduce a novel dataset for Sci-QFS composed of 8,695 examples, each one with a query, the sentences of the full text from a paper and the relevance labels for each. After testing several classical and state-of-the-art embedding models on this data, we found that the task of Sci-QFS is far from being solved, although it is relatively straightforward for humans. Surprisingly, we found that classical methods outperformed modern pre-trained Deep language Models (sometimes by a large margin), showing the need for large datasets to better fine-tune the latter. We share our experiments, data and models at https://***/jarobyte91/rouge_sciqfs. © 2025 Association for Computational Linguistics.

关键词： Structured Query language

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Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMs 9

Forecasting Credit Ratings: A Case Study where Traditional M...

引用

Joint Workshop of the 9th Financial Technology and natural language processing, FinNLP 2025, the 6th Financial Narrative processing, FNP 2025, and the 1st Workshop on Large language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st international conference on Computational Linguistics, COLING 2025

作者： Drinkall, Felix Pierrehumbert, Janet B. Zohren, Stefan Department of Engineering Science University of Oxford United Kingdom The Alan Turing Institute United Kingdom Faculty of Linguistics University of Oxford United Kingdom

ISBN: (纸本)9798891762091

Large language Models (LLMs) have been shown to perform well for many downstream tasks. Transfer learning can enable LLMs to acquire skills that were not targeted during pretraining. In financial contexts, LLMs can sometimes beat well-established benchmarks. This paper investigates how well LLMs perform at forecasting corporate credit ratings. We show that while LLMs are very good at encoding textual information, traditional methods are still very competitive when it comes to encoding numeric and multimodal data. For our task, current LLMs perform worse than a more traditional XGBoost architecture that combines fundamental and macroeconomic data with high-density text-based embedding features. We investigate the degree to which the text encoding methodology affects performance and interpretability. The dataset reconstruction and model code from this paper is provided1. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Bayesian Detection for Maritime Radar Targets in CG-LN Sea Clutter without Training Data 16

Bayesian Detection for Maritime Radar Targets in CG-LN Sea C...

引用

16th international conference on Signal processing Systems, ICSPS 2024

作者： Xue, Jian Feng, Zhuo Li, HongEn Pan, MeiYan School of Communications and Information Engineering Xi’an University of Posts and Telecommunications China Xi'an Electronic Engineering Research Institute China

ISBN: (纸本)9781510689251

This paper addresses the problem of Bayesian adaptive detection for maritime radar targets in compound Gaussian sea clutter with random texture and speckle covariance matrix. To adaptively match sea clutter characteristics well, the prior distributions of sea clutter components are employed. Specially, we model the sea clutter texture component as a lognormal random variable, and the sea clutter speckle covariance matrix as an inverse complex Wishart random matrix. A Bayesian radar target detector is designed based on the criteria of the generalized likelihood ratio test. The proposed detector incorporates the prior knowledge of sea clutter, and, importantly, does not rely on training data. Experimental results on simulated and measured radar data show that the proposed detector outperforms its competitors when the number of training data is limited. © 2025 SPIE.

关键词： Inverse problems

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Examine the filter bubble with a focus on emotion and content 24

Examine the filter bubble with a focus on emotion and conten...

引用

8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Kita, Shusaku Mine, Tsunenori Arakawa, Yutaka Department of Information Science and Technology Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Department of Advanced Information Technology Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

ISBN: (纸本)9798400717383

Recommendation systems (RSs) have become popular in various Internet services, enabling the delivery of content tailored to user preferences. While RSs are designed to update recommended content to align more closely with user preferences based on browsing history and registration information, RSs may still present biased information due to inherent characteristics. This phenomenon is known as the "filter bubble,"a crucial aspect of the discourse on "informational health."Previous studies have examined the filter bubble phenomenon, focusing on the potential for bias in recommended content. However, there is no consensus on the definition of "bias,"and no agreement on the criteria for identifying a filter bubble state. In addition, the previous methods rely primarily on topic diversity as a metric, overlooking other important indicators. This study presents a more comprehensive approach to defining the state of the filter bubble. Our methodology employs two key indicators: content and emotion. We also evaluate the effectiveness of the proposed approach. © 2024 Copyright held by the owner/author(s).

关键词： Sentiment analysis

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