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Object-Based Video Tampering Localization via Trace Consistency Analysis

Object-Based Video Tampering Localization via Trace Consiste...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Pengfei Pei Yun Cao Jinchuan Li Zeyu Zhang Yuqi Pang Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of object-based video inpainting and splicing tampering techniques, the dissemination of malicious videos on the internet poses significant risks. Existing localization methods, however, exhibit limitations such as restriction to specific datasets, limited performance in detecting unknown forgeries, and lack of robustness when dealing with reprocessed videos. In this paper, we propose an effective video tampering localization network that comprehensively considers the common characteristics of inpainting and splicing and extracts more generalized features of forgery traces, significantly enhancing localization performance. Specifically, we design four modules to independently extract inherent difference features, including edge artifacts, pixel distribution, texture features, and frequency information. For feature fusion learning, we employ a two-stage approach: first, a Convolutional Neural Network (CNN)-based module is used to extract local features; then, a vision Transformer (ViT)-based module is utilized to extract global correlation features. Experimental results demonstrate that our method significantly outperforms existing state-of-theart methods. Ablation studies verify the necessity of each feature component, and the two-stage feature fusion approach shows better performance compared to directly embedding a CNN into a ViT hybrid architecture.

关键词： Location awareness Splicing signal processing Feature extraction Transformers Forgery Robustness Convolutional neural networks Data mining Speech processing

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SX-Stitch: An Efficient VMS-UNet Based Framework for Intraoperative Scoliosis X-Ray Image Stitching

SX-Stitch: An Efficient VMS-UNet Based Framework for Intraop...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yi Li Heting Gao Mingde He Jinqian Liang Jason Gu Wei Liu Department of Mechanical and Energy Engineering Southern University of Science and Technology Peking Union Medical College Hospital Electrical and Computer Engineering Dalhousie University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In scoliosis surgery, the limited field of view of the C-arm Xray machine restricts the surgeons’ holistic analysis of spinal structures. This paper presents an end-to-end efficient and robust intraoperative X-ray image stitching method for scoliosis surgery, named SX-Stitch. The method is divided into two stages: segmentation and stitching. In the segmentation stage, we propose a medical image segmentation model named vision Mamba of Spine-UNet (VMS-UNet), which utilizes the state space Mamba to capture long-distance contextual information while maintaining linear computational complexity. Meanwhile, the proposed model incorporates the SimAM attention mechanism, significantly improving the segmentation performance. In the stitching stage, we simplify the alignment process between images to minimize a registration energy function. The total energy function is then optimized to order unordered images and a hybrid energy function is introduced to optimize the best seam, effectively eliminating parallax artifacts. On the clinical dataset, Sx-Stitch demonstrates superiority over SOTA schemes both qualitatively and quantitatively.

关键词： Image segmentation Computational modeling Scoliosis Surgery signal processing Speech processing Computational complexity X-ray imaging Image stitching Context modeling

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Conformal Prediction for Manifold-based Source Localization with Gaussian Processes

Conformal Prediction for Manifold-based Source Localization ...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Vadim Rozenfeld Bracha Laufer Goldshtein Department of Electrical Engineering Tel-Aviv University Tel-Aviv Israel

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We address the problem of uncertainty quantification (UQ) in the localization of a sound source within adverse acoustic environments. Estimating the position of the source is influenced by various factors, such as noise and reverberation, leading to significant uncertainty. Quantifying this uncertainty is essential, particularly when localization outcomes impact critical decision-making processes, such as in robot audition, where the accuracy of location estimates directly influences subsequent actions. Despite this, common localization methods offer point estimates without quantifying the estimation uncertainty. To address this, we employ conformal prediction (CP)—a framework that delivers statistically valid prediction intervals (PIs) with finite-sample guarantees, independent of the data distribution. However, commonly used Inductive CP (ICP) methods require a large amount of labeled data, which can be difficult to obtain in the localization setting. To mitigate this limitation, we incorporate a semi-supervised manifold-based localization method using Gaussian process regression (GPR), with an efficient Transductive CP (TCP) technique, specifically designed for GPR. We demonstrate that our method generates statistically valid PIs across different acoustic conditions, while producing smaller intervals compared to baselines.

关键词： Location awareness Uncertainty Noise Gaussian processes Position measurement Reverberation Noise measurement Speech processing Kernel robots

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Learning Explicit Modulation Vectors for Disentangled Transformer Attention-Based RGB-D Visual Tracking 27th

Learning Explicit Modulation Vectors for Disentangled Trans...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Pan, Yifan Xu, Tianyang Zhu, Xue-Feng Luo, Xiaoqing Wu, Xiao-Jun Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9783031784439

Effective fusion of RGB-D multi-modal features is crucial for RGB-D object tracking. Existing fusion methods mainly guide the interaction of RGB and depth by dense attention, but such formulation relies only on independent spatial token attributes, without considering the correspondences among channel slices. To address this limitation, we propose a spatial and channel disentangled attention mechanism, providing dual guidance on spatial and channel relevance for RGB-D fusion. Simultaneously, to deal with potential erroneous attention, we exploit the explicit modulation vectors to weaken less relevant spatial and channel features. Drawing on this, we design an adaptive architecture by weakening the significance of less confident intra-modal features and amplifying the supportive cross-modal features. The experimental results on four standard RGB-D benchmarking datasets, i.e., ARKitTrack, DepthTrack, RGBD1K, and CDTB, confirm the merit of our approach in adaptive fusion, outperforming existing state-of-the-art solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multicarrier modulation

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Shared vision Transformer Helps Scene Text Retrieval 4th

Shared Vision Transformer Helps Scene Text Retrieval

引用

4th international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Luo, Hailong Ibrayim, Mayire Hamdulla, Askar Deng, Qilin School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China

ISBN: (纸本)9789819787043

The scene text retrieval system can search all the images containing the query text in the gallery based on the input query text and locate the position of the query text at the same time. The current state-of-the-art approach is to match query images and text instances based on visual information, which somewhat mitigates the heterogeneous differences between the two modalities and can support the zero-shot training approach. But there is no consideration of two branches of features interacting on information as a way to enhance the features. In this paper, we improve this method. First, we design a structure that enhances features using a shared-visual embedding transformer (S-VET). Text instance features and query input features are brought closer visually by a visual transformer with shared parameters and then spliced and fused with the original features to enrich the visual information of the two branch features. We then design a Multi-Scale Residual Channel Attention (MRCA) module to obtain features at different scales by using convolution kernels at multiple scales and then use channel attention to mine beneficial information from features at different scales. Facilitated by the above scheme, our method achieved a more advanced performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Attention-Based Patch Matching and Motion-Driven Point Association for Accurate Point Tracking 27th

Attention-Based Patch Matching and Motion-Driven Point Asso...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Zang, Han Xu, Tianyang Zhu, Xue-Feng Song, Xiaoning Wu, Xiao-Jun Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9783031784439

Point tracking can be regarded as a transfer and extension of keypoints representation and matching. In contrast to matching the salient points like corners or spots, which are easily detected and described by detector-based approaches, point tracking tasks are capable of handling arbitrary points on physical surfaces, including nonrigid or weakly-textured surfaces. Additionally, keypoint matching lacks a direct mechanism to handle occlusion in tracking tasks. Therefore, we propose to use a detector-free local feature-matching model based on the transformer structure to perform patch matching, incorporating occlusion prediction and introducing an uncertainty estimate for extended robustness. Besides, using a coarse-to-fine strategy, we generate coarse predictions at the patch level and refine them to obtain accurate coordinates at the sub-pixel level by motion-driven point association. After fine-tuning the model on the training set of the Perception Test benchmark, our model APM-MPAPT outperforms the competitors in the benchmark on the corresponding validation set, with promising performance improvement against the baseline. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmarking

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Color Imaging in Medical Video Systems: Problems and Features

Color Imaging in Medical Video Systems: Problems and Feature...

引用

Digital signal processing and its Applications (DSPA), international conference on

作者： Natalia A. Obukhova Alexandr A. Motyko Alexandr A. Pozdeev Konstantin A. Smirnov SPbSU ETU “LETI” St. Petersburg Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

This paper explores the issue of accurate colour reproduction in medical imaging. It considers key problems and lists standards related to the process of forming and displaying medical images. Particular attention is paid to the fact that the existing standards do not take into account the individual characteristics of colour perception in different doctors. In the conditions of using modern displays with wide colour coverage this can lead to errors in colour rendering for a particular doctor, which, in turn, reduces the accuracy of disease diagnostics. The application of optimal LUT (Look-Up Table) with variable resolution to recalculate the colour space of the display is proposed, which allows to personalise the image for individual features of the doctor's vision and improve the accuracy of diagnosis. Experimental results demonstrating the advantages of the proposed approach over classical colour transform methods are presented. The results confirm that the use of variable resolution LUT can reduce colour rendering errors and improve the quality of medical diagnostics.

关键词： Image resolution Accuracy Three-dimensional displays Image color analysis Transforms Observers Rendering (computer graphics) Table lookup Standards signal resolution

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Hierarchical Relation Distillation for Efficient 3D Visual Grounding

Hierarchical Relation Distillation for Efficient 3D Visual G...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jun Liu School of Information Renmin University of China BeiJing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D vision-Language (3D-VL) Grounding seeks to localize 3D objects in point clouds using natural language descriptions. While existing methods focus on improving performance through intricate spatial understanding modules, the efficiency of 3D grounding models has been overlooked, despite its importance for real-time applications. To address this issue, we propose a novel hierarchical relation distillation framework that creates a small yet high-performing model through a two-stage distillation process. The first stage focuses on semantic feature distillation, eliminating noise and retaining valuable information for second stage training. The second stage utilizes half-layers of the teacher model for 3D relation feature distillation, enhancing computational efficiency without sacrificing accuracy. Experimental results on the challenging Nr3D and Sr3D datasets demonstrate that our distilled model achieves state-of-the-art performance while reducing computational costs by over 40%.

关键词： Training Visualization Solid modeling Three-dimensional displays Accuracy Grounding Computational modeling Semantics Computational efficiency Speech processing

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PDSeg: Patch-Wise Distillation and Controllable Image Generation for Weakly-Supervised Histopathology Tissue Segmentation

PDSeg: Patch-Wise Distillation and Controllable Image Genera...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wei-Hua Li Yu-Hsing Hsieh Huei-Fang Yang Chu-Song Chen National Taiwan University Taipei Taiwan National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Weakly-supervised semantic segmentation, which achieves pixel-wise segmentation using image-level labels, has emerged as an alternative to fully supervised methods by reducing the need for detailed annotations. Inspired by the recent success of the teacher-student strategy in various vision tasks, we present a transformer-based weakly supervised framework that distills knowledge from a CNN teacher. Specifically, we incorporate a sequence of patch-wise distillation tokens into the transformer student, with each token focused on learning a specific patch under the teacher’s guidance. This design enables the teacher to provide more reliable supervision to the student. On the other hand, in pathology images, it is often observed that certain tissue types are less represented than others. This class imbalance poses a significant challenge for many WSSS algorithms. To address this issue, we further introduce a data synthesis pipeline using a diffusion model conditioned on semantic label maps to mitigate the effects of class imbalance in histopathology images. Unlike previous methods that rely on full annotations to construct semantic label maps, our approach leverages the intrinsic characteristics of histopathology images. This leads to an approach that does not require full annotations and is well-suited for weakly-supervised scenarios. Through extensive experiments on the LUAD-HistoSeg and BCSS-WSSS datasets, we demonstrate that our approach outperforms state-of-the-art methods.

关键词： Histopathology Annotations Semantic segmentation Semantics Pipelines signal processing algorithms signal processing Transformers Diffusion models Speech processing

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Boosting Large Language Model for Speech Synthesis: An Empirical Study

Boosting Large Language Model for Speech Synthesis: An Empir...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Hongkun Hao Long Zhou Shujie Liu Jinyu Li Shujie Hu Rui Wang Furu Wei Shanghai Jiao Tong University Microsoft Corporation

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Large language models (LLMs) have made significant advancements in natural language processing and are concurrently extending the language ability to other modalities, such as speech and vision. Nevertheless, most of the previous work focuses on prompting LLMs with perception abilities like auditory comprehension, and the effective approach for augmenting LLMs with speech synthesis capabilities remains ambiguous. In this paper, we conduct a comprehensive empirical exploration of boosting LLMs with the ability to generate speech, by combining pre-trained LLM LLaMA/OPT and text-to-speech synthesis model VALL-E. We compare three integration methods between LLMs and speech synthesis models, including directly fine-tuned LLMs, superposed layers of LLMs and VALL-E, and coupled LLMs and VALL-E using LLMs as a powerful text encoder. Experimental results show that, using LoRA method to fine-tune LLMs directly to boost the speech synthesis capability does not work well, and superposed LLMs and VALL-E can improve the quality of generated speech both in speaker similarity and word error rate (WER). Among these three methods, coupled methods leveraging LLMs as the text encoder can achieve the best performance, making it outperform original speech synthesis models with a consistently better speaker similarity and a significant (10.9%) WER reduction.

关键词： Error analysis Large language models signal processing Boosting Natural language processing Acoustics Text to speech

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