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检索条件"任意字段=2024 Conference on Visual Communications and Image Processing"

共 6970 条记录，以下是71-80 订阅

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REFERRING image SEGMENTATION WITH TWO-STAGE MULTI-MODAL INTERACTION 31

REFERRING IMAGE SEGMENTATION WITH TWO-STAGE MULTI-MODAL INTE...

引用

2024 International conference on image processing

作者： Wang, Zhenhua Ye, Linwei Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou Peoples R China

ISBN: (纸本)9798350349405;9798350349399

The objective of referring image segmentation is to extract referred entities from an image using a particular natural language sentence. The main idea for this task is interacting textual and visual features to build multi-modal relationships. The prior state-of-the-art methods mainly focus on local multi-level intermediate feature interaction or global text-to-image alignment, which might result in insufficient interaction for capturing global multi-modal information exchange or fine-grained referred object details, respectively. To overcome this issue, we introduce a referring image segmentation framework with two-stage multi-modal interaction. Specifically, we devise an innovative multi-level cross-modal fusion module to effectively facilitate the interaction of intermediate features of linguistic and visual modalities for fine-grained details of referred objects. Besides, we further align the linguistic and visual information by introducing an elaborate global alignment module for accurately localizing the entire referred objects. The comprehensive experiments conducted on three referring image segmentation datasets illustrate that our proposed two-stage multi-modal interaction framework exhibits a marked superiority over the contemporary state-of-the-art approaches.

关键词： Vision and Language Referring image Segmentation

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TOWARDS GENERALIZABLE REFERRING image SEGMENTATION VIA TARGET PROMPT AND visual COHERENCE 31

TOWARDS GENERALIZABLE REFERRING IMAGE SEGMENTATION VIA TARGE...

引用

2024 International conference on image processing

作者： Liu, Yajie Ge, Pu Ma, Haoxiang Fan, Shichao Liu, Qingjie Huang, Di Wang, Yunhong Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Referring image segmentation (RIS) aims to segment objects in an image conditioning on free-form text descriptions. Despite the overwhelming progress, it still remains challenging for current approaches to perform well on cases with various text expressions or with unseen visual entities, limiting its further application. In this paper, we present a novel RIS approach, which substantially improves the generalization ability by addressing the two dilemmas mentioned above. Specially, to deal with unconstrained texts, we propose to boost a given expression with an explicit and crucial prompt, which complements the expression in a unified context, facilitating target capturing in the presence of linguistic style changes. Furthermore, we introduce a multi-modal fusion aggregation module with visual guidance from a powerful pretrained model to leverage spatial relations and pixel coherences to handle the incomplete target masks and false positive irregular clumps which often appear on unseen visual entities. Extensive experiments are conducted in the zero-shot cross-dataset settings and the proposed approach achieves consistent gains compared to the state-of-the-art, e.g., 4.15%, 5.45%, and 4.64% mIoU increase on RefCOCO, RefCOCO+ and ReferIt respectively, demonstrating its effectiveness.

关键词： Referring image segmentation generalization zero-shot cross-dataset

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Enhanced FISTA For Non-Blind image Deblurring 15

Enhanced FISTA For Non-Blind Image Deblurring

引用

15th International conference on Signal processing and communications (SPCOM)

作者： Kumar, Avinash Sahoo, Sujit Kumar Indian Inst Technol Goa Sch Elect Sci Ponda 403401 India

ISBN: (纸本)9798350350463;9798350350456

The image deblurring problem is an active area of research in image processing. The Fast Iterative Shrinkage Thresholding Algorithm (FISTA) has garnered significant attention for solving deblurring problems with l(1)- based sparsity constraints. This paper proposes a new l(1)- based algorithm called Enhanced FISTA (EFISTA) that incorporates accelerated gradient descent and an appropriate proximal operation. We have studied the impact of accelerated gradient descent in noisy conditions, which helps us identify the importance of a well-designed proximal operation to mitigate noise interference. The experimental results show that EFISTA exhibits superior execution speed while maintaining reconstruction performance comparable to its predecessors. This highlights the robustness and efficiency of EFISTA in addressing image deblurring challenges, particularly at high noise levels.

关键词： Inverse Problems image deblurring Denoising FISTA EFISTA Gradient Descent Convergence Speed Execution Time

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image Transformation for IoT-based Anomaly Detection 32

Image Transformation for IoT-based Anomaly Detection

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32nd IEEE Signal processing and communications Applications conference (SIU)

作者： Bamus, Imran Okay, Feyza Yildirim Gazi Univ Comp Engn Dept Ankara Turkiye

ISBN: (纸本)9798350388978;9798350388961

In the rapidly evolving environment of the Internet of Things (IoT), anomaly detection plays an important role in ensuring the security and reliability of connected systems. Traditional approaches often struggle to capture subtle patterns and anomalies in complex IoT data. This paper introduces a new perspective by utilizing image transformation techniques, specifically the Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plot (RP), coupled with deep learning models for better anomaly detection. This study presents a comparative study to evaluate the performance of these image transformation methods and assess their effectiveness in detecting anomalies in IoT data. The comparative results show that RP with CNN has the superior performance compared to other image transformation techniques.

关键词： image transformation IoT anomaly detection deep learning

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Autonomous visual Moderation System (AutoMod) 31

Autonomous Visual Moderation System (AutoMod)

引用

31st IEEE conference on Signal processing and communications Applications (SIU)

作者： Bahtiyar, Huseyin Katipoglu, Bahadir Irfan Yuksel, Eda Toroslu, Ismail Hakki Karagoz, Pinar VLMedia R&D Dept Gumus Blok K1-1 ODTU TEKNOKENT Ankara Turkiye Middle East Tech Univ METU Dept Comp Engn TR-06800 Ankara Turkiye

ISBN: (纸本)9798350343557

Automod, the content moderation system, is an artificial intelligence solution that enables the detection of similarities and inconsistencies in visual content (image, video, etc.). It is designed as a content moderation system to detect the similarity and inconsistencies of user-generated visual content (images and videos). With the similarity module installed, labor savings of 15% were achieved, and F1 score results of 90% and higher were achieved for nonconformity detection models. More than 100.000 images can be evaluated daily, and the system's load was tested. Similarly, keyframes obtained from at least 65.000 video content that can be evaluated daily were passed through nonconformity models, and load test was applied.

关键词： image processing Machine Learning Deep Learning

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Alignment of image-Text and Video-Text Datasets 31

Alignment of Image-Text and Video-Text Datasets

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31st IEEE conference on Signal processing and communications Applications (SIU)

作者： Ozkose, Yunus Emre Gokce, Zeynep Duygulu, Pinar Hacettepe Univ Bilgisayar Muhendisligi Ankara Turkiye

ISBN: (纸本)9798350343557

In this study, the alignment of video-text and image-text datasets is studied. Firstly, similarities are calculated over the texts in the two data sets. A retrieval setup with visual similarities is then applied to the subset which is created via calculated text similarities. A BERT-based embedding vector method is applied to the raw and pure texts. As a visual feature, object-based and CLIP-based methods are used to define video frames. According to the results, alignment with CLIP features achieves the best results in the subset created by filtering using raw text.

关键词： dataset alignment deep learning machine learning

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PERCEPTUAL LEARNED image COMPRESSION VIA END-TO-END JND-BASED OPTIMIZATION 31

PERCEPTUAL LEARNED IMAGE COMPRESSION VIA END-TO-END JND-BASE...

引用

2024 International conference on image processing

作者： Pakdaman, Farhad Nami, Sanaz Gabbouj, Moncef Tampere Univ Fac Informat Technol & Commun Sci Tampere Finland

ISBN: (纸本)9798350349405;9798350349399

Emerging Learned image Compression (LC) achieves significant improvements in coding efficiency by end-to-end training of neural networks for compression. An important benefit of this approach over traditional codecs is that any optimization criteria can be directly applied to the encoder-decoder networks during training. Perceptual optimization of LC to comply with the Human visual System (HVS) is among such criteria, which has not been fully explored yet. This paper addresses this gap by proposing a novel framework to integrate Just Noticeable Distortion (JND) principles into LC. Leveraging existing JND datasets, three perceptual optimization methods are proposed to integrate JND into the LC training process: (1) Pixel-Wise JND Loss (PWL) prioritizes pixel-by-pixel fidelity in reproducing JND characteristics, (2) image-Wise JND Loss (IWL) emphasizes on overall imperceptible degradation levels, and (3) Feature-Wise JND Loss (FWL) aligns the reconstructed image features with perceptually significant features. Experimental evaluations demonstrate the effectiveness of JND integration, highlighting improvements in rate-distortion performance and visual quality, compared to baseline methods. The proposed methods add no extra complexity after training.

关键词： Just Noticeable Distortion (JND) Human visual System ( HVS) learned compression perceptual optimization

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Method of Alternating Proximations for Solving Linear Inverse Problems 15

Method of Alternating Proximations for Solving Linear Invers...

引用

15th International conference on Signal processing and communications (SPCOM)

作者： Kamath, Abijith Jagannath Reddy, Nareddy Kartheek Kumar Seelamantula, Chandra Sekhar Indian Inst Sci Dept Elect Engn Bengaluru 560012 India

ISBN: (纸本)9798350350463;9798350350456

Signal/image reconstruction problems are typically posed as linear inverse problems with the objective of minimizing a least-squares (LS) loss function and a regularization term derived from a suitable prior. Several decades of work have gone into the design and efficient application of various penalty functions, whilst the design of the data-fidelity loss function has remained relatively unexplored. This paper proposes a distance-minimization approach to the design of the data-fidelity loss for solving linear inverse problems and discusses proximal algorithms for solving them. We discuss the implications of using the proposed formulation as opposed to the standard LS in terms of its effect on noise and convergence. We consider solving sparse signal recovery and image restoration tasks, in particular, image deblurring, and show that distance minimization techniques out-perform standard LS for the various models under consideration.

关键词： Inverse problems distance minimization proximal algorithms method of alternative proximations image restoration

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EXPLORING SALIENCY BIAS IN MANIPULATION DETECTION 31

EXPLORING SALIENCY BIAS IN MANIPULATION DETECTION

引用

2024 International conference on image processing

作者： Krinsky, Joshua Bettis, Alan Tang, Qiuyu Moreira, Daniel Bharati, Aparna Lehigh Univ Dept Comp Sci & Engn Bethlehem PA 18015 USA Loyola Univ Dept Comp Sci Chicago IL USA

ISBN: (纸本)9798350349405;9798350349399

The social media-fuelled explosion of fake news and misinformation supported by tampered images has led to growth in the development of models and datasets for image manipulation detection. However, existing detection methods mostly treat media objects in isolation, without considering the impact of specific manipulations on viewer perception. Forensic datasets are usually analyzed based on the manipulation operations and corresponding pixel-based masks, but not on the semantics of the manipulation, i.e., type of scene, objects, and viewers' attention to scene content. The semantics of the manipulation play an important role in spreading misinformation through manipulated images. In an attempt to encourage further development of semantic-aware forensic approaches to understand visual misinformation, we propose a framework to analyze the trends of visual and semantic saliency in popular image manipulation datasets and their impact on detection. https://***/CV-Lehigh/Bias_IMD

关键词： Media Forensics image Manipulation Dataset Analysis image Saliency Semantic Understanding

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SALIENT GUIDED TEXT DETECTION IN E-COMMERCE imageS 31

SALIENT GUIDED TEXT DETECTION IN E-COMMERCE IMAGES

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2024 International conference on image processing

作者： Yin, Boon Yin Japar, Nurul Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia

ISBN: (纸本)9798350349405;9798350349399

Text detection is a fundamental task in computer vision that involves identifying and locating text within images or videos. It has been the subject of extensive research, with numerous approaches primarily tailored for open-scene text, but there are limited studies dedicated to practical industries such as e-commerce. E-commerce images are designed to capture human attention, and effective text detection can amplify this marketing strategy. Yet, identifying text in ecommerce images poses particular challenges due to their distinct visual attributes, which set them apart from open-scene images. Therefore, this paper aims to address this gap by exploring how human attention can aid text detection on e-commerce images. The proposed model merges high-level text features with low-level and saliency features and exploits both local and semantic characteristics of image regions. Leveraging visual cues, low-level and saliency features aid in predicting the saliency map, which is then employed to aid text detection. The proposed method achieves better localization of text, outperforming current state-of-the-art models on the benchmark e-commerce SalECI dataset. The code for this study is available at https: //***/bebbieyin/SalientTextDet.

关键词： text-detection visual saliency e-commerce image-processing

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