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image Caption Generator with CLIP Interrogator

Image Caption Generator with CLIP Interrogator

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Adhiraj Ajaykumar Jagtap Jitendra Musale Sambhaji Nawale Parth Takate Indra Kale Saurabh Waghmare Department of Computer Engineering ABMSP’s Anantrao Pawar College of Engineering and Research Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This research paper presents a cutting-edge system for creating image captions. It combines the strengths of the COCO (Common Objects in Context) model with the CLIP (Contrastive Language-image Pre-training) Interrogator. Our method allows us to generate both short social media captions and in-depth descriptive captions for a broad range of images. By bringing together COCO's skill in spotting objects and CLIP's deeper understanding, the system produces captions that fit the context. We then use a Natural Language processing (NLP) technique to polish these captions making sure they're easy to read and make sense. This two-pronged approach to caption creation makes content more accessible and boosts the impact of social media posts. As a result, the system can adapt to many different uses, from creating content to improving digital accessibility. This groundbreaking method creates fresh opportunities to interact between humans and AI when it comes to understanding and describing visual content. It sets the stage for image captioning systems that are more natural and aware of context down the road.

关键词： Adaptation models visualization Technological innovation Social networking (online) Roads Neural networks Natural language processing Generators Data models Context modeling

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Multi-level Feature Adaptation and Embeddings Alignment for Zero-shot Anomaly Detection

Multi-level Feature Adaptation and Embeddings Alignment for ...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yiqing Liu Huilin Deng Gang Zhao School of Information Science and Technology University of Science and Technology of China HeFei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Anomaly Detection (AD) is an important and challenging task in computer vision. Recently, Contrastive Language-image Pre-training (CLIP) has shown impressive generalization capability in zero-shot AD tasks. However, the inherent global semantic consistency of CLIP fails to accurately identify subtle anomalies, and the lack of fine-grained alignment leads to inaccurate defect localization. In this study, we propose a hierarchical semantic feature adaptation framework designed to better leverage intermediate layer information from the visual encoder, enabling the effective capture of subtle and diverse anomaly-aware semantic features, thus improving CLIP’s adaptation to AD task. We further introduce an alignment and mask refinement co-optimization decoder to locate key anomalous regions from coarse to fine, thereby alleviating the misalignment between visual and textual embeddings. The experimental results demonstrate the effectiveness of our method.

关键词： Location awareness visualization Computer vision Adaptation models Semantics Signal processing Feature extraction Decoding Speech processing Anomaly detection

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Research on the recognition system of visual arc welding

Research on the recognition system of visual arc welding

引用

ieee Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Mingsheng Zhang Jianyu Gu Qiming Li Shiqi Sun Yu Liu Jiangsu Automation Research Institute Lianyungang China College of Materials Engineering North China Institute of Aerospace Engineering Langfang China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

At present, many domestic scholars and research institutions have studied the laser vision welding seam tracking system. By improving the control strategy of the robot and welding seam tracking system, the processing time is optimized and the real-time response of the welding seam tracking system is promoted. However, the practical application of the research results is not yet satisfactory. This paper carries out an experimental study of vision-based arc weld tracking. Its main content contains welding robot kine matics and control systems, weld image processing technology, right angle weld tracking technology, and so on. The research shows that the feature extraction of arc weld based on vision can be completed and arc weld recognition can be realized. It provides some reference for the application of laser vision welding seam tracking systems in industry.

关键词： Productivity visualization image recognition Welding Neural networks Process control Feature extraction Real-time systems Data mining Robots

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INN-based Secure Steganography Using Lost Information as Adversarial Perturbations

INN-based Secure Steganography Using Lost Information as Adv...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Fei Shang Weixiang Zhao Xiangui Kang Z. Jane Wang School of Computer Science and Engineering Guangdong Key Lab of Information Security Sun Yat-sen University Guangzhou China Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently image steganography methods based on invertible neural networks (INNs) demonstrated the capability to automatically embed and extract secret messages while maintaining high visual quality in stego images. However, there remain concerns about security and invertibility of such methods. In this paper, for the first time, we introduce adversarial hiding into INN-based image steganography method to simultaneously perform steganographic embedding and adversarial perturbation generation, resulting in improved security. Our method enhances the invertibility of the INN structure: It utilizes the lost information of the INN to generate perturbations, which are then combined with the gradient of the cover image to produce an adversarial stego image. Also, a learnable noise layer is proposed to mitigate information loss caused by rounding and truncation during image storage. Therefore, the proposed method significantly improves security while enhancing extraction performance of INN-based steganography approach, as supported by our experimental results. For example, the steganalysis detection accuracy of SRNet decreases from 96.86% to 51.77% at a payload of 0.2 bits per pixel (bpp).

关键词： Training Steganography visualization Perturbation methods Noise Neural networks Security image storage Speech processing Payloads

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Learning Rank Constrained Exposure Correction from Unpaired Data

Learning Rank Constrained Exposure Correction from Unpaired ...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhuoyue Gong Qing Zhang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents a novel unpaired learning based exposure correction network that can robustly deal with both under- and over-exposed images. Given an input image in any exposure conditions, we first obtain its intermediate under- and over-exposure corrected versions by predicting dual illuminations for Retinex-based enhancement, the two outputs together with the original input are then fed to a multi-exposure fusion module to adaptively locate the best-exposed regions in the three images and then seamlessly fuse them into a well-exposed output. To ensure that the generated result is visually natural and free of disturbing visual artifacts such as loss of details and contrast degradation, we introduce a novel rank loss. Experiments show that our method outperforms existing methods in terms of both quantitative and qualitative results.

关键词： Degradation visualization Fuses Lighting Estimation Speech enhancement Signal processing Benchmark testing Acoustics image enhancement

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KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration

KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driv...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chengyuan Li Suyang Zhou Jieping Kong Lei Qi Hui Xue College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods.

关键词： Training visualization image segmentation Large language models Semantics Signal processing Question answering (information retrieval) Speech processing Anomaly detection Biomedical imaging

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A Recent Advancements in Multi-Modal Medical image Fusion Techniques using SWT, NSST, NSCT, and CNN

A Recent Advancements in Multi-Modal Medical Image Fusion Te...

引用

Cognitive Computing in Engineering, communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

image fusion is a method used in image processing to provide a more complete representation by amalgamating features and data from many images. Multimodal medical image fusion involves the integration of medical images from many imaging modalities, including computed tomography (CT) scans, positron emission tomography (PET), and magnetic resonance imaging (MRI), into one single dataset. This integration enhances the visualisation of anatomical structures and clinical situations, hence improving diagnostic accuracy by leveraging the strengths of each medium. This study employs MRI, CT, and PET scans as experimental modalities. This review aims to compare various multi modal medical image approach based on Stationary Wavelet Transform (SWT), Non-Subsampled Shearlet Transform (NSST), Convolutional Neural Network (CNN) and NonSubsampled Contourlet Transform (NSCT). This study examines the latest conventional and non-conventional research conducted within these disciplines. It further evaluates these approaches according to diverse image quality metrics and many quantitative assessments. According to this comparison, CNN-based fusion demonstrates superior results, as the overall visual and parametric quality of the fusion outcomes surpasses that of the other approaches evaluated.

关键词： image quality Wavelet transforms visualization Reviews Magnetic resonance imaging Computed tomography Convolutional neural networks Positron emission tomography Medical diagnostic imaging image fusion

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TROI: Cross-Subject Pretraining with Sparse Voxel Selection for Enhanced fMRI visual Decoding

TROI: Cross-Subject Pretraining with Sparse Voxel Selection ...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ziyu Wang Tengyu Pan Zhenyu Li Ji Wu Xiuxing Li Jianyong Wang Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

fMRI (functional Magnetic Resonance Imaging) visual decoding involves decoding the original image from brain signals elicited by visual stimuli. This often relies on manually labeled ROIs (Regions of Interest) to select brain voxels. However, these ROIs can contain redundant information and noise, reducing decoding performance. Additionally, the lack of automated ROI labeling methods hinders the practical application of fMRI visual decoding technology, especially for new subjects. This work presents TROI (Trainable Region of Interest), a novel two-stage, data-driven ROI labeling method for cross-subject fMRI decoding tasks, particularly when subject samples are limited. TROI leverages labeled ROIs in the dataset to pretrain an image decoding backbone on a cross-subject dataset, enabling efficient optimization of the input layer for new subjects without retraining the entire model from scratch. In the first stage, we introduce a voxel selection method that combines sparse mask training and low-pass filtering to quickly generate the voxel mask and determine input layer dimensions. In the second stage, we apply a learning rate rewinding strategy to fine-tune the input layer for downstream tasks. Experimental results on the same small sample dataset as the baseline method for brain visual retrieval and reconstruction tasks show that our voxel selection method surpasses the state-of-the-art method MindEye2 with an annotated ROI mask.

关键词： Training visualization Noise Training data Functional magnetic resonance imaging Brain modeling Decoding Labeling Speech processing Optimization

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Lossless Phase Conversion Method for Object Wave-based Hologram Compression

Lossless Phase Conversion Method for Object Wave-based Holog...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hiroki Kojima Ryota Koiso Ryosuke Watanabe Keisuke Nonaka KDDI Research Inc. Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

An object wave is a suitable intermediate representation format for transmitting holographic application data because it is compatible with various playback devices. However, compressing the phase component of the object wave presents challenges due to its unique signal characteristics, i.e., periodicity and randomness. Hence, object waves significantly differ from ordinary 2D images such as camera-captured and screen pictures. To overcome the challenges, we propose a conversion method for lossless phase image coding using differential pulse-code modulation. Specifically, we calculate phase differences using adjacent pixels in two directions, considering periodic cycles of 2π, and then input them into the existing image coding. This method ensures robustness against randomness by smoothing the phase difference using the average of adjacent pixels. Experimental results show that lossless phase coding with the proposed conversion method achieves coding efficiency improvements of 6.50% over lossless phase coding without the proposed method.

关键词： image coding Smoothing methods Phase modulation Transform coding Encoding Robustness Acoustics Speech processing

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SlotFusion: Object-Centric Audiovisual Feature Fusion with Slot Attention for Remote Sensing Scene Recognition

SlotFusion: Object-Centric Audiovisual Feature Fusion with S...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Fangzhou Han Tianyi Yu Lamei Zhang Lingyu Si Yiqi Zhang Dept. of Information Engineering Harbin Institute of Technology Harbin China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Despite significant advancements in remote sensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundancy of ground objects in remote sensing images, accurately aligning audio features with image features during the fusion process is a critical challenge. In this paper, we introduce an object-centric feature fusion method named SlotFusion. By employing a slot attention-based feature decoupling module and a slot-based audiovisual feature fusion module, we transform modality features with complex semantic information into a set of slot features corresponding to object units and use gated activation units to adaptively implement object-centric feature fusion. Experiments on the Audio visual Aerial Scene Recognition dataset (ADVANCE) demonstrate that the proposed SlotFusion significantly improves remote sensing scene recognition performance, with a 7.04% increase in overall accuracy compared to previous methods, achieving state-of-the-art results.

关键词： visualization Semantics Termination of employment Transforms Interference Signal processing Logic gates Robustness Speech processing Remote sensing

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