IEEE Signal Processing Letters最新文献_第10页

Knowledge Distillation in Fourier Frequency Domain for Dense Prediction 面向密集预测的傅立叶频域知识精馏

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-11 DOI: 10.1109/LSP.2024.3515795

Min Shi;Chengkun Zheng;Qingming Yi;Jian Weng;Aiwen Luo

引用次数: 0

Noise-Robust Hearing Aid Voice Control 抗噪音助听器语音控制

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-09 DOI: 10.1109/LSP.2024.3512377

Iván López-Espejo;Eros Roselló;Amin Edraki;Naomi Harte;Jesper Jensen

引用次数: 0

BiASAM: Bidirectional-Attention Guided Segment Anything Model for Very Few-Shot Medical Image Segmentation BiASAM：用于少镜头医学图像分割的双向注意引导分割模型

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-09 DOI: 10.1109/LSP.2024.3513240

Wei Zhou;Guilin Guan;Wei Cui;Yugen Yi

{"title":"BiASAM: Bidirectional-Attention Guided Segment Anything Model for Very Few-Shot Medical Image Segmentation","authors":"Wei Zhou;Guilin Guan;Wei Cui;Yugen Yi","doi":"10.1109/LSP.2024.3513240","DOIUrl":"https://doi.org/10.1109/LSP.2024.3513240","url":null,"abstract":"The Segment Anything Model (SAM) excels in general segmentation but encounters difficulties in medical imaging due to few-shot learning challenges, particularly with extremely limited annotated data. Existing approaches often suffer from insufficient feature extraction and inadequate loss function balancing, resulting in decreased accuracy and poor generalization. To address these issues, we propose BiASAM, which uniquely incorporates two bidirectional attention mechanisms into SAM for medical image segmentation. Firstly, BiASAM integrates a spatial-frequency attention module to improve feature extraction, enhancing the model's ability to capture both fine and coarse details. Secondly, we employ an attention-based gradient update mechanism that dynamically adjusts loss weights, boosting the model's learning efficiency and adaptability in data-scarce scenarios. Additionally, BiASAM utilizes the point and box fusion prompt to enhance segmentation precision at both global and local levels. Experiments across various medical datasets show BiASAM achieves performance comparable to fully supervised methods with just two labeled samples.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"246-250"},"PeriodicalIF":3.2,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DiffHSR: Unleashing Diffusion Priors in Hyperspectral Image Super-Resolution DiffHSR：在高光谱图像超分辨率中释放扩散先验

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-09 DOI: 10.1109/LSP.2024.3512371

Yizhen Jia;Yumeng Xie;Ping An;Zhen Tian;Xia Hua

{"title":"DiffHSR: Unleashing Diffusion Priors in Hyperspectral Image Super-Resolution","authors":"Yizhen Jia;Yumeng Xie;Ping An;Zhen Tian;Xia Hua","doi":"10.1109/LSP.2024.3512371","DOIUrl":"https://doi.org/10.1109/LSP.2024.3512371","url":null,"abstract":"Hyperspectral images provide rich spectral information and have been widely applied in numerous computer vision tasks. However, their low spatial resolution often limits their use in applications such as image segmentation and recognition. In previous works, generating high-resolution hyperspectral (HR-HS) images required the use of low-resolution hyperspectral (LR-HS) images and high-resolution RGB (HR-RGB) images as priors, which increases the cost of data collection and may lead to measurement and calibration errors in practical applications. Although the currently popular CNN-based single hyperspectral image super-resolution (single HS-SR) methods have improved performance, they are not flexible enough to process images with different degradation. From a visual perspective, the generated super-resolution images exhibit a significant smudging effect due to the loss of information. Leveraging multi-modal techniques and generative prior, we propose DiffHSR that marks a significant leap in LR-HS images super-restoration without HR-RGB. Additionally, we have established a connection between hyperspectral images and the RGB image-based generative model tasks using low-cost data and fine-tuning approaches, which creates a novel paradigm. Comprehensive experiments have demonstrated that our proposed method achieves strong visual performance and competitive results in term of quantitative metrics and perceptive quality.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"236-240"},"PeriodicalIF":3.2,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Theoretical Guarantees for Sparse Graph Signal Recovery 稀疏图信号恢复的理论保证

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-09 DOI: 10.1109/LSP.2024.3514800

Gal Morgenstern;Tirza Routtenberg

引用次数: 0

High-Performance Optimization Framework for Reversible Data Hiding Predictor 可逆数据隐藏预测器的高性能优化框架

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-05 DOI: 10.1109/LSP.2024.3512357

Bin Ma;Hongtao Duan;Ruihe Ma;Yongjin Xian;Xiaolong Li

引用次数: 0

Text-Based Audio Retrieval by Learning From Similarities Between Audio Captions 通过学习音频字幕之间的相似性实现基于文本的音频检索

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-04 DOI: 10.1109/LSP.2024.3511414

Huang Xie;Khazar Khorrami;Okko Räsänen;Tuomas Virtanen

{"title":"Text-Based Audio Retrieval by Learning From Similarities Between Audio Captions","authors":"Huang Xie;Khazar Khorrami;Okko Räsänen;Tuomas Virtanen","doi":"10.1109/LSP.2024.3511414","DOIUrl":"https://doi.org/10.1109/LSP.2024.3511414","url":null,"abstract":"This letter proposes to use similarities of audio captions for estimating audio-caption relevances to be used for training text-based audio retrieval systems. Current audio-caption datasets (e.g., Clotho) contain audio samples paired with annotated captions, but lack relevance information about audio samples and captions beyond the annotated ones. Besides, mainstream approaches (e.g., CLAP) usually treat the annotated pairs as positives and consider all other audio-caption combinations as negatives, assuming a binary relevance between audio samples and captions. To infer the relevance between audio samples and arbitrary captions, we propose a method that computes non-binary audio-caption relevance scores based on the textual similarities of audio captions. We measure textual similarities of audio captions by calculating the cosine similarity of their Sentence-BERT embeddings and then transform these similarities into audio-caption relevance scores using a logistic function, thereby linking audio samples through their annotated captions to all other captions in the dataset. To integrate the computed relevances into training, we employ a listwise ranking objective, where relevance scores are converted into probabilities of ranking audio samples for a given textual query. We show the effectiveness of the proposed method by demonstrating improvements in text-based audio retrieval compared to methods that use binary audio-caption relevances for training.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"221-225"},"PeriodicalIF":3.2,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MLLM-TA: Leveraging Multimodal Large Language Models for Precise Temporal Video Grounding MLLM-TA：利用多模态大语言模型进行精确的时间视频接地

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-04 DOI: 10.1109/LSP.2024.3511426

Yi Liu;Haowen Hou;Fei Ma;Shiguang Ni;Fei Richard Yu

{"title":"MLLM-TA: Leveraging Multimodal Large Language Models for Precise Temporal Video Grounding","authors":"Yi Liu;Haowen Hou;Fei Ma;Shiguang Ni;Fei Richard Yu","doi":"10.1109/LSP.2024.3511426","DOIUrl":"https://doi.org/10.1109/LSP.2024.3511426","url":null,"abstract":"In untrimmed video tasks, identifying temporal boundaries in videos is crucial for temporal video grounding. With the emergence of multimodal large language models (MLLMs), recent studies have focused on endowing these models with the capability of temporal perception in untrimmed videos. To address the challenge, in this paper, we introduce a multimodal large language model named MLLM-TA with precise temporal perception to obtain temporal attention. Unlike the traditional MLLMs, answering temporal questions through one or two words related to temporal information, we leverage the text description proficiency of MLLMs to acquire video temporal attention with description. Specifically, we design a dual temporal-aware generative branches aimed at the visual space of the entire video and the textual space of global descriptions, simultaneously generating mutually supervised consistent temporal attention, thereby enhancing the video temporal perception capabilities of MLLMs. Finally, we evaluate our approach on both video grounding task and highlight detection task on three popular benchmarks, including Charades-STA, ActivityNet Captions and QVHighlights. The extensive results show that our MLLM-TA significantly outperforms previous approaches both on zero-shot and supervised setting, achieving state-of-the-art performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"281-285"},"PeriodicalIF":3.2,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond Diagonal RIS: Key to Next-Generation Integrated Sensing and Communications? 超越对角线RIS：下一代集成传感和通信的关键？

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-04 DOI: 10.1109/LSP.2024.3511395

Tara Esmaeilbeig;Kumar Vijay Mishra;Mojtaba Soltanalian

引用次数: 0

FAMSeC: A Few-Shot-Sample-Based General AI-Generated Image Detection Method FAMSeC：一种基于少量采样的通用ai生成图像检测方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2024-12-04 DOI: 10.1109/LSP.2024.3511421

Juncong Xu;Yang Yang;Han Fang;Honggu Liu;Weiming Zhang

{"title":"FAMSeC: A Few-Shot-Sample-Based General AI-Generated Image Detection Method","authors":"Juncong Xu;Yang Yang;Han Fang;Honggu Liu;Weiming Zhang","doi":"10.1109/LSP.2024.3511421","DOIUrl":"https://doi.org/10.1109/LSP.2024.3511421","url":null,"abstract":"The explosive growth of generative AI has saturated the internet with AI-generated images, raising security concerns and increasing the need for reliable detection methods. The primary requirement for such detection is generalizability, typically achieved by training on numerous fake images from various models. However, practical limitations, such as closed-source models and restricted access, often result in limited training samples. Therefore, training a general detector with few-shot samples is essential for modern detection mechanisms. To address this challenge, we propose FAMSeC, a general AI-generated image detection method based on LoRA-based \u0000<bold>F\u0000orgery \u0000<bold>A\u0000wareness \u0000<bold>M\u0000odule and \u0000<bold>Se\u0000mantic feature-guided \u0000<bold>C\u0000ontrastive learning strategy. To effectively learn from limited samples and prevent overfitting, we developed a forgery awareness module (FAM) based on LoRA, maintaining the generalization of pre-trained features. Additionally, to cooperate with FAM, we designed a semantic feature-guided contrastive learning strategy (SeC), making the FAM focus more on the differences between real/fake image than on the features of the samples themselves. Experiments show that FAMSeC outperforms state-of-the-art method, enhancing classification accuracy by 14.55% with just 0.56% of the training samples.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"226-230"},"PeriodicalIF":3.2,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0