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Enhancing Robustness of Implicit Neural Representations Against Weight Perturbations

Enhancing Robustness of Implicit Neural Representations Agai...

引用

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

作者： Wenyong Zhou Yuxin Cheng Zhengwu Liu Taiqiang Wu Chen Zhang Ngai Wong Department of EEE The University of Hong Kong Hong Kong SAR

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Implicit Neural Representations (INRs) encode discrete signals in a continuous manner using neural networks, demonstrating significant value across various multimedia applications. However, the vulnerability of INRs presents a critical challenge for their real-world deployments, as the network weights might be subjected to unavoidable perturbations. In this work, we investigate the robustness of INRs for the first time and find that even minor perturbations can lead to substantial performance degradation in the quality of signal reconstruction. To mitigate this issue, we formulate the robustness problem in INRs by minimizing the difference between loss with and without weight perturbations. Furthermore, we derive a novel robust loss function to regulate the gradient of the reconstruction loss with respect to weights, thereby enhancing the robustness. Extensive experiments on reconstruction tasks across multiple modalities demonstrate that our method achieves up to a 7.5 dB improvement in peak signal-to-noise ratio (PSNR) values compared to original INRs under noisy conditions.

关键词： PSNR Perturbation methods Noise Neural networks Robustness signal reconstruction Noise measurement Speech processing image reconstruction Gain

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Evidential Deep Learning with Reweighted Margin Adjustment for Uncertainty-Driven Cervical OCT image Diagnosis

Evidential Deep Learning with Reweighted Margin Adjustment f...

引用

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

作者： Hanfeng Zhu Yi Qu Yutao Ma Yuchen Pei School of Computer Science Central China Normal University Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Cervical optical coherence tomography (OCT) is crucial in diagnosing cervical diseases due to its high-resolution imaging and non-invasive characteristics. However, recent improvements in classifying cervical OCT images have mainly focused on accuracy, often neglecting the importance of uncertainty and robustness in disease predictions. Uncertainty estimation faces challenges in collecting evidence for the minority classes, leading to fake confidence in evidential deep learning. There are similar issues with cervical OCT image classification. This study proposes a novel evidential learning process for cervical disease screening using OCT. It provides a measure of decision margin for each class with two novel mechanisms to address the hinge posted by class imbalance. Specifically, we introduce a reweighted margin adjustment strategy to learn less biased evidence among classes and make reliable uncertainty estimations. Extensive experiments on internal and external cervical OCT datasets demonstrate the proposed method outperforms competitive baseline methods, providing more reliable and robust predictions, particularly in out-of-distributions scenarios. By improving uncertainty estimation for class imbalance, our method offers valuable insights for practical clinical applications, highlighting the importance of measuring uncertainty in medical AI-aided diagnosis systems.

关键词： Deep learning Uncertainty Accuracy Smoothing methods Estimation signal processing Robustness Speech processing Medical diagnostic imaging Diseases

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Cross-Channel Unlabeled Sensing over a Union of signal Subspaces

Cross-Channel Unlabeled Sensing over a Union of Signal Subsp...

引用

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

作者： Taulant Koka Manolis C. Tsakiris Benjamín Béjar Haro Michael Muma Robust Data Science Group Technische Universität Darmstadt Darmstadt Germany Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Swiss Data Science Center Paul Scherrer Institute Villigen Switzerland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Cross-channel unlabeled sensing addresses the problem of recovering a multi-channel signal from measurements that were shuffled across channels. This work expands the cross-channel unlabeled sensing framework to signals that lie in a union of subspaces. The extension allows for handling more complex signal structures and broadens the framework to tasks like compressed sensing. These mismatches between samples and channels often arise in applications such as whole-brain calcium imaging of freely moving organisms or multi-target tracking. We improve over previous models by deriving tighter bounds on the required number of samples for unique reconstruction, while supporting more general signal types. The approach is validated through an application in whole-brain calcium imaging, where organism movements disrupt sample-to-neuron mappings. This demonstrates the utility of our framework in real-world settings with imprecise sample-channel associations, achieving accurate signal reconstruction.

关键词： Neuroscience Tracking Calcium Imaging Organisms signal reconstruction Sensors Speech processing image reconstruction Compressed sensing

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A Convolutional Recurrent Mixer Network For Radar Meteorological image Super-Resolution

A Convolutional Recurrent Mixer Network For Radar Meteorolog...

引用

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

作者： Rafael Gonçalves Pires Daniel F. S Santos Roberto V. Calheiros João Paulo Papa Ik Hyun Lee Sambit Bakshi Khan Muhammad Department of Computing São Paulo State University Bauru Brasil Meteorological Research Institute São Paulo State University Bauru Brasil Dept. of Mechatronics Engineering Tech University of Korea IKLAB Inc. South Korea Dept. of CS&E National Institute of Technology Rourkela India School of Convergence Sungkyunkwan University Seoul South Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counter-parts, often affected by sensor limitations or environmental factors. Convolutional Neural Networks (CNNs) are state-of-the-art for SR tasks but computationally heavy. This paper introduces a novel CRMN (Convolutional Recurrent Mixer Network), a hybrid deep learning-based SR technique designed to address the complexity of CNNs, which is validated in the context of meteorological radar images. Experiments on public benchmark datasets (Berkley432 and T291) and our newly manually collected precipitation dataset from the Meteorological Research Institute (IPMET) show that our CRMN model provides competitive results compared to leading SR methods with significantly fewer parameters, making it a promising and practical solution for SR applications, particularly radar meteorology.

关键词： image quality Meteorological radar Convolution Computational modeling Superresolution Real-time systems Complexity theory Speech processing Mixers signal resolution

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Refiner: Fine-grained Cross-modal Concepts Refinement for Compositional Zero-Shot Learning

Refiner: Fine-grained Cross-modal Concepts Refinement for Co...

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

作者： xiao Zhang Haodong Jing Hui Chen Yongqiang Ma Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent Compositional Zero-Shot Learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token design from Transformer-based encoder inevitably captures contextual information from unrelated objects and background, thus hindering the modeling of fine-grained class-specific visual features. Suffering from cross-modal gap, prior methods also struggle to improve compositional recognition performance. To address these issues, we propose a fine-grained cross-modal concepts refinement framework, termed as Refiner, which comprises two pivotal components: (i) the fine-grained concepts refinement of image embeddings to capture state-object context within visual scenes, and (ii) the cross-modal information fusion to mitigate the modality gap. By leveraging learnable query vectors to capture region-specific semantic information pertinent to composition labels, our approach refines visual representations with fine-grained state-object context information. As for cross-modal information fusion, we construct a robust image-to-text mapping by aligning visual embeddings with states, objects, and compositions, respectively. Extensive experiments demonstrate that our Refiner achieves new state-of-the-art performance across all popular benchmarks in both closed- and open-world settings.

关键词： Visualization Zero shot learning Semantics Benchmark testing signal processing Transformers Vectors Acoustics Speech processing Context modeling

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NeRF-VLD: Efficient Visual Landmark Database Construction via Scene Constraints

NeRF-VLD: Efficient Visual Landmark Database Construction vi...

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

作者： Tao Chen Qun Niu Ning Liu Sun Yat-Sen University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Visual landmarks, with their unique textures, play a crucial role in vision-based environmental sensing applications (such as augmented reality-based advertising, indoor navigation). However, few studies have focused on the construction of visual landmark databases, which is usually time-consuming and laborious. We propose a novel method called NeRF-VLD, which first introduces Neural Radiance Fields (NeRF) to build visual landmark databases efficiently. NeRF-VLD generates images from a small number of sources, thus eliminating the need for extensive site surveys and consequently reducing deployment costs significantly. To improve the quality of generated images, we introduce two scene-constrained modules. In the first module, sparse depth maps provided by Structure-from-Motion are introduced as additional geometric constraints. In the second module, scene regularization is applied to image pixels where depth information is unavailable, constraining the volume density of sample points near the camera plane to alleviate ghostly artifacts problem. Experimental results demonstrate that NeRF-VLD can achieve comparable landmark identification and positioning accuracy to real-image databases while reducing the number of image acquisition by 97%. Compared with full manual survey, NeRF-VLD reduces survey and update costs and improves the applicability of vision-based service.

关键词： Surveys Visualization Costs Accuracy Manuals Neural radiance field Cameras Sensors Visual databases Speech processing

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Cross-Component Residual Prediction for Geometry-Based Point Cloud Compression

Cross-Component Residual Prediction for Geometry-Based Point...

引用

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

作者： Bharath Vishwanath Yingzhan xu Kai Zhang Li Zhang Bytedance Inc. San Diego CA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Point cloud compression is pivotal for the success of immersive multimedia applications. For attribute compression in geometry-based point cloud compression (G-PCC), Region Adaptive Hierarchical Transform (RAHT) is the preferred coding method. Inspired by the significant impact of cross-component prediction in traditional image and video coding, we investigate and present our pioneering work on cross-component residual prediction for RAHT in G-PCC. The method builds on the core observation that cross-component correlations are observed locally in some regions in some sequences. Accordingly, the prediction is employed for last few layers of RAHT which capture local characteristics. We employ a simple linear model, that predicts chroma residues from reconstructed luma residue. The prediction coefficients are learnt on the fly from reconstructed residues of the neighbors. The method gives 1% luma coding gain and around 2-3% chroma coding gain with negligible increase in complexity. The method is adopted to the Geometric Solid Test Model (GeS-TM v7.0), a dedicated codec being developed for solid point clouds.

关键词： Point cloud compression Video coding Correlation Transforms Streaming media Solids Encoding Complexity theory Speech processing image reconstruction

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Leveraging Multimodal Diffusion Models to Accelerate Imaging with Side Information

Leveraging Multimodal Diffusion Models to Accelerate Imaging...

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

作者： Timofey Efimov Harry Dong Megna Shah Jeff Simmons Sean Donegan Yuejie Chi Electrical and Computer Engineering Carnegie Mellon University Materials and Manufacturing Directorate Air Force Research Laboratory

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Diffusion models have found phenomenal success as expressive priors for solving inverse problems, but their extension beyond natural images to more structured scientific domains remains limited. Motivated by applications in materials science, we aim to reduce the number of measurements required from an expensive imaging modality of interest, by leveraging side information from an auxiliary modality that is much cheaper to obtain. To deal with the non-differentiable and black-box nature of the forward model, we propose a framework to train a multimodal diffusion model over the joint modalities, turning inverse problems with black-box forward models into simple linear inpainting problems. Numerically, we demonstrate the feasibility of training diffusion models over materials imagery data, and show that our approach achieves superior image reconstruction by leveraging the available side information, requiring significantly less amount of data from the expensive microscopy modality.

关键词： Training Three-dimensional displays Inverse problems Microscopy Closed box Diffusion models Turning Data models Numerical models image reconstruction

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CabiNet: A Deep Learning Framework for Multiclass Medical image Segmentation from Multiple Single Class Datasets

CabiNet: A Deep Learning Framework for Multiclass Medical Im...

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

作者： Aman Soni Ishita Maiti Nirmalya Ghosh Electrical Engineering IIT Kharagpur Kharagpur India School of Medical Science and Technology IIT Kharagpur Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The development of deep learning based image segmentation algorithms is often hindered by the lack of adequately annotated datasets, and this issue becomes a severe bottleneck in multi-class segmentation. The models can learn binary image segmentation since the datasets are often labeled for a single class. However, the task becomes more challenging for multi-class annotations and segmentation of regions of interest, e.g., multi-organ segmentation that plays a vital role in computer-assisted diagnosis, surgery planning, and other related applications. This study proposes a framework, CabiNet, that attempts to solve data bottleneck by learning multi-class healthy organ segmentation from partially annotated abdominal MRI data, i.e., multiple single-class datasets. Multiple organ specific expert networks and a Jack of All (JoA) network are trained to generate posterior probability maps of individual organs simultaneously which are accumulated to get the final posterior probability distributions over different organs for each pixel. On benchmark Combined Healthy Abdominal Organ Segmentation (CHAOS) MRI dataset CabiNet yielded very promising dice scores: liver (90.39%), right kidney (87.41%), left kidney (81.09%), spleen (90.78%) and overall average dice score as 87.4%.

关键词： Deep learning image segmentation Annotations Magnetic resonance imaging Posterior probability Surgery Benchmark testing Medical diagnostic imaging Kidney Tuning

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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

作者： Yujie Ding Shenghua Teng Zuoyong Li xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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