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When CLIP Meets PHOC: A Dual-Branch Network for Historical Document image Retrieval

When CLIP Meets PHOC: A Dual-Branch Network for Historical D...

引用

2025 ieee International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Jing Wei, Hongxi Zhang, Qing School of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9798350368741

In this paper, we leverage Contrastive Language-image Pre-training (CLIP) for Historical Document image Retrieval (HDIR). We are largely inspired by recent advances on CLIP and its exceptional generalization capabilities, but for the first time, we tailor it to benefit HDIR. We put forward a dual-branch joint learning network based on CLIP and Pyramid Histograms of Characters (PHOC), termed CPNet, which supports out-of-vocabulary word spotting for both query-by-example and query-by-string. To achieve this, we introduce two key components: (i) a rule-based PHOC to enhance text representations by capturing detailed character-level information, and (ii) a visual prompt that allows the CLIP image encoder to adapt and extend its knowledge to our task through additional learnable prompts. Extensive experiments on benchmark datasets, including Kanjur and Geser, demonstrate that the proposed CPNet sets a new state-of-the-art in performance for HDIR, highlighting its potential for advancing research in other text-related retrieval tasks. © 2025 ieee.

关键词： CLIP historical documents images retrieval out-of-vocabulary PHOC

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DEFormer: DCT-driven Enhancement Transformer for Low-light image and Dark Vision

DEFormer: DCT-driven Enhancement Transformer for Low-light I...

引用

2025 ieee International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Yin, Xiangchen Yu, Zhenda Gao, Xin Sun, Xiao Institute of Artificial Intelligence Hefei Comprehensive National Science Center University of Science and Technology of China Hefei230601 China Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230601 China School of Vehicle and Mobility Tsinghua University Beijing100084 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9798350368741

Low-light image enhancement restores the colors and details of a single image and improves high-level visual tasks. However, restoring the lost details in the dark area is still a challenge relying only on the RGB domain. In this paper, we delve into frequency as a new clue into the model and propose a DCT-driven enhancement transformer (DEFormer) framework. First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE) to represent frequency features. Additionally, we propose a cross domain fusion (CDF) to reduce the differences between the RGB domain and the frequency domain. Our DEFormer has achieved superior results on the LOL and MIT-Adobe FiveK datasets, improving the dark detection performance. © 2025 ieee.

关键词： Discrete Cosine Transform Frequency Learning Low-light image Enhancement Transformer

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Realization of Digital Protection Algorithm of National Traditional Culture Based on Virtual Reality Technology

Realization of Digital Protection Algorithm of National Trad...

引用

2024 International conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Chen, Hongxia Hou, Lanbao School of Literature and Media Jingchu University of Technology Jingmen448000 China School of Mathematics and Physics Jingchu University of Technology Jingmen448000 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

National traditional culture and traditional technology are the products of historical precipitation and indispensable precious resources. The establishment of national cultural database is of great significance for the protection and inheritance of cultural heritage. Aiming at the specific functional requirements of traditional cultural digital protection system for image segmentation and retrieval, this paper focuses on the common algorithms of image segmentation and feature matching. This paper analyzes and compares the applicability and processing effect of each algorithm in ethnic patterns. EGBIS image segmentation algorithm and shape context feature matching algorithm are used to achieve high precision image processing. This paper designs and implements a national culture retrieval system that meets the application needs. Combined with the image characteristics of ethnic patterns and the application requirements of the system, this paper defines the concepts of source images, primitives and hyperprimitives, and establishes a relational database based on them. This study has laid a certain theoretical foundation for the study of digital inheritance of national culture. © 2025 SPIE.

关键词： image segmentation image retrieval Databases Feature extraction image processing algorithms and systems image processing visualization Distance measurement Virtual reality visual analytics

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Design and Implementation of image Description Model Using Artificial Intelligence Based Techniques 3rd

Design and Implementation of Image Description Model Using A...

引用

3rd International conference on Computational Electronics for Wireless communications, ICCWC 2023

作者： Ingale, Sumedh Bamnote, G.R. Prof. Ram Meghe Institute of Technology & Research Badnera Amravati India

ISBN: (纸本)9789819719457

The process that produces written descriptions that effectively represent the meaning and context of an image is known as image captioning. To integrate visual and textual data, it needs to blend computer vision and natural language processing methods. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), such as long-short-term memory (LSTM) networks, are two methods used for captioning images. High-level visual features from the input image are extracted by the CNN, and the RNN uses those features to generate the matching captions. Residual Network is a deep CNN architecture with exceptional results in a range of computer vision tasks, including the classification of images. ResNet has been used as the foundation for extracting picture features. ResNet and CNN abstracts an image's visual information via feature extraction and uses neural networks with recurrent architecture to provide meaningful, contextually appropriate captions. Machines are now able to comprehend and speak about the visual world in a manner similar to humans because of the integration of computer vision and natural language processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Towards Maximizing Semantic Coverage for image-Text Retrieval

Towards Maximizing Semantic Coverage for Image-Text Retrieva...

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2025 ieee International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Xu, Junhao Liu, Zheng Chen, Zhumin Shen, Fei School of Computer Science and Technology Shandong University China School of Computer Science and Technology Shandong University of Finance and Economics China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798350368741

To establish semantic associations between images and texts, existing image-Text Retrieval (ITR) methods primarily focus on fixed-scale fragments, which only identify explicit semantic categories. Consequently, semantic coverage is constrained, leading to the omission of certain semantic associations. To enlarge the semantic coverage, we propose the Semantic Coverage-Aware Network (SCA-Net). First, explicit semantic categories are identified by SCA-Net through analyzing the semantic membership of visual and textual fragments, thereby establishing more precise explicit semantic associations. Second, implicit semantic categories are identified by SCA-Net via adaptively aggregating visual and textual fragments across various scales using a co-occurrence-aware router, thereby significantly expanding the semantic coverage and establishing complete semantic associations. Third, image-text similarity is calculated using the attention mechanism over a broader range of semantic coverage. Extensive experiments demonstrate that SCA-Net significantly enhances ITR performance compared to state-of-the-art methods by maximizing semantic coverage. © 2025 ieee.

关键词： Co-occurrence-aware router image-text retrieval Semantic association Semantic category Semantic coverage

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FiTGAN: Content Fusion with Style Transformation for Few-shot image Generation

FiTGAN: Content Fusion with Style Transformation for Few-sho...

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2025 ieee International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhou, Yingbo Zhang, Pengyu Ye, Yutong Yue, Zhihao Wei, Xian Chen, Mingsong MoE Eng. Research Center of SW/HW Co-Design Tech. and App. East China Normal University China

ISBN: (纸本)9798350368741

Due to the semantic entanglement in fusion strategies or unstable training in complicated image transformations, existing few-shot image generation methods still suffer from low generation quality and diversity. To tackle the above problems, we propose a novel fusion- and transformation-based framework named content Fusion with style Transformation Generative Adversarial Network (FiTGAN) for few-shot image generation. The basic assumption is that any image consists of a collection of content-related and style-related features. FiTGAN disentangles internal representations with two independent encoders and combines the fused contents and transformed styles to generate new images. Specifically, we design a multi-scale content fusion strategy and a reparameterized style transformation mechanism to learn more fine-grained semantics without changing category-relevant attributes. Furthermore, we formulate a content reconstruction loss and a style divergence loss to provide better training stability and generation performance. Comprehensive experiments on three well-known datasets demonstrate that FiTGAN can not only produce more realistic and diverse images for few-shot image generation but also achieve better classification accuracy for downstream visual applications with limited data. © 2025 ieee.

关键词： Content Fusion Few-shot image Generation Generative Adversarial Network Style Transformation

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image Recognition for Wildlife Conservation

Image Recognition for Wildlife Conservation

引用

2025 ieee International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Charanya, P. Sridharan, S. Yuvan Shankar, S. Sriram, M. Yashvanth, K.P. Department of Artificial Intelligence and Machine Learning Coimbatore India Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

This study adopts an empirical approach to evaluate the efficacy of image processing methods in conservation efforts for animals. The initial phase involves the collection of data from various sources within the natural ecosystem, including images captured through camera traps, drones, and other monitoring devices. This comprehensive and diverse dataset is then processed using advanced image processing Tools. A key strength of this methodology lies in its use of Machine Learning algorithms, particularly YOLO v8 (You Only Look Once) and Support Vector Machines (SVMs). These techniques are suitable for wildlife conservation because of their capacity to analyze complex visual data and detect patterns indicative of species presence, behavior, and habitat conditions. The paper outlines the application of YOLO v8 and SVMs to the collected image data, enabling accurate identification, real-time detection, and classification of wildlife species. This capability is essential for monitoring populations, tracking movements, and identifying potential threats, thus facilitating timely conservation actions. Furthermore, the research extends its evaluation by comparing the results of the Machine Learning approach with traditional ecological survey methods, offering a comprehensive assessment of the effectiveness of image processing in wildlife conservation. © 2025 ieee.

关键词： Invertebrates

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image Captioning with Multiple Perspectives—A visual Context-Based Approach 5th

Image Captioning with Multiple Perspectives—A Visual Contex...

引用

5th International conference on Computing and Network communications, CoCoNet 2023

作者： Ashwin, G. Chaitanya, V. Kishan Rohith Nair, Priyanka C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (纸本)9789819747108

This study tackles the difficult issues of image captioning while negotiating the complexity of visual data processing. The complexity of visual data and the associated processing requirements make image captioning a daunting task. This research describes an effective method for image captioning that makes advantage of attentional mechanisms. Several models, including Transformer, VGG16, VGG19, Inception, RNN decoders, and Bahdanau’s attention mechanism, are contrasted and analyzed to demonstrate the benefits of integrating attention processes. By allowing the model to focus on the appropriate visual component, attention improves the accuracy of the generated captions. Transformer models, in particular, outperform other methods by capturing complicated relationships and delivering accurate output, and they have the highest BLEU Score of 70. The findings emphasize the significance of attention mechanisms and the relevance of selecting a suitable model architecture to maximize picture captioning performance. The variety of potential applications emphasizes the benefits and potential impact of image captioning in a broad range of scenarios. Several use cases are found beneficial, including supporting the visually impaired, improving product descriptions in e-commerce, assisting medical diagnosis, improving image search ability, and allowing effective communication and comprehension of visual information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Evaluation of Transfer Learning for visual Multiple Target Tracking 1

Evaluation of Transfer Learning for Visual Multiple Target T...

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1st International conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Jadhav, Anisha Anil California State University Department of Computer Science Monterey Bay Seaside United States

ISBN: (纸本)9798331518578

Deep learning has revolutionized high-level image processing tasks, notably image classification and segmentation, by effectively handling multi-dimensional features in image space. This report investigates the application of transfer learning, utilizing features extracted from a pre-trained VGG deep neural network to visual multiple-target tracking (MTT). Traditional feature detectors like the Lucas-Kanade Tracker and Good Features to Track (GFTT) algorithm are computationally efficient but limited in dynamic, cluttered environments, such as those encountered by unmanned aerial systems (UAS). Replacing GFTT with deep feature detectors from VGG is proposed to assess tracking accuracy and improve computational efficiency. Our experiments reveal that, though promising, deep feature extraction results in lower frame rates and less smooth trajectories than traditional methods. This study highlights the potential and current limitations of integrating deep learning features into real-time visual tracking systems. Plans include optimizing the deep learning model for real-time processing, automating filter selection for feature extraction, and exploring additional pre-trained networks to improve tracking performance and efficiency. © 2025 ieee.

关键词： image segmentation

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Bridging the Machine-Human Gap in Blurred-image Classification via Entropy Maximisation 6

Bridging the Machine-Human Gap in Blurred-Image Classificati...

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6th ieee International conference on image processing, Applications and Systems, IPAS 2025

作者： Sansano-Sansano, Emilio Martinez-Garcia, Marina Portilla, Javier Universitat Jaume I INIT Castellón de la Plana Spain Universitat Jaume I IMAC Castellón de la Plana Spain CSIC Instituto de Óptica Madrid Spain

ISBN: (纸本)9798331506520

Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-based range-constrained entropy merit function, from which we devise a zero-phase, circular symmetric blind deblurring method. We apply it as a pre-processing step for image classification and test it using pre-trained classification models and images blurred by Gaussian kernels. We compare our method to state-of-the-art restoration methods, showing its superiority, effectively bridging the machine-human gap for most models and blur levels. Our results also rank higher than the competitors in no-reference and full-reference image quality metrics. Notwithstanding the limitation to zero-phase blur, this work shows that, for image pre-processing aimed at visual tasks, it may be advantageous to use merit functions based on vision science and information theory, rather than on the expected error to the latent image. © 2025 ieee.

关键词： Constrained optimization

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