pwc-archive/papers-with-abstracts huggingface dataset:
Potential Contrast: Properties, Equivalences, and Generalization to Multiple Classes
Authors: Wallace Peaslee, Anna Breger, Carola-Bibiane Schönlieb
Date: 2025-05-02 00:00:00

Potential contrast is typically used as an image quality measure and quantifies the maximal possible contrast between samples from two classes of pixels in an image after an arbitrary grayscale transformation. It has been valuable in cultural heritage applications, identifying and visualizing relevant information in multispectral images while requiring a small number of pixels to be manually sampled. In this work, we introduce a normalized version of potential contrast that removes dependence on image format and also prove equalities that enable generalization to more than two classes and to continuous settings. Finally, we exemplify the utility of multi-class normalized potential contrast through an application to a medieval music manuscript with visible bleedthrough from the back page. We share our implementations, based on both original algorithms and our new equalities, including generalization to multiple classes, at https://github.com/wallacepeaslee/Multiple-Class-Normalized-Potential-Contrast.
Contactless pulse rate assessment: Results and insights for application in driving simulator
Authors: Đorđe D. Nešković, Kristina Stojmenova Pečečnik, Jaka Sodnik, Nadica Miljković
Date: 2025-05-02 00:00:00
Tasks: Photoplethysmography (PPG)

Camera-based monitoring of Pulse Rate (PR) enables continuous and unobtrusive assessment of driver's state, allowing estimation of fatigue or stress that could impact traffic safety. Commonly used wearable Photoplethysmography (PPG) sensors, while effective, suffer from motion artifacts and user discomfort. This study explores the feasibility of non-contact PR assessment using facial video recordings captured by a Red, Green, and Blue (RGB) camera in a driving simulation environment. The proposed approach detects subtle skin color variations due to blood flow and compares extracted PR values against reference measurements from a wearable wristband Empatica E4. We evaluate the impact of Eulerian Video Magnification (EVM) on signal quality and assess statistical differences in PR between age groups. Data obtained from 80 recordings from 64 healthy subjects covering a PR range of 45-160 bpm are analyzed, and signal extraction accuracy is quantified using metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results show that EVM slightly improves PR estimation accuracy, reducing MAE from 6.48 bpm to 5.04 bpm and RMSE from 7.84 bpm to 6.38 bpm. A statistically significant difference is found between older and younger groups with both video-based and ground truth evaluation procedures. Additionally, we discuss Empatica E4 bias and its potential impact on the overall assessment of contact measurements. Altogether the findings demonstrate the feasibility of camera-based PR monitoring in dynamic environments and its potential integration into driving simulators for real-time physiological assessment.
Optimal Vector Compressed Sensing Using James Stein Shrinkage
Authors: Apratim Dey, David Donoho
Date: 2025-05-01 00:00:00
Tasks: compressed sensing

The trend in modern science and technology is to take vector measurements rather than scalars, ruthlessly scaling to ever higher dimensional vectors. For about two decades now, traditional scalar Compressed Sensing has been synonymous with a Convex Optimization based procedure called Basis Pursuit. In the vector recovery case, the natural tendency is to return to a straightforward vector extension of Basis Pursuit, also based on Convex Optimization. However, Convex Optimization is provably suboptimal, particularly when $B$ is large. In this paper, we propose SteinSense, a lightweight iterative algorithm, which is provably optimal when $B$ is large. It does not have any tuning parameter, does not need any training data, requires zero knowledge of sparsity, is embarrassingly simple to implement, and all of this makes it easily scalable to high vector dimensions. We conduct a massive volume of both real and synthetic experiments that confirm the efficacy of SteinSense, and also provide theoretical justification based on ideas from Approximate Message Passing. Fascinatingly, we discover that SteinSense is quite robust, delivering the same quality of performance on real data, and even under substantial departures from conditions under which existing theory holds.
Field-scale soil moisture estimated from Sentinel-1 SAR data using a knowledge-guided deep learning approach
Authors: Yi Yu, Patrick Filippi, Thomas F. A. Bishop
Date: 2025-05-01 00:00:00

Soil moisture (SM) estimation from active microwave data remains challenging due to the complex interactions between radar backscatter and surface characteristics. While the water cloud model (WCM) provides a semi-physical approach for understanding these interactions, its empirical component often limits performance across diverse agricultural landscapes. This research presents preliminary efforts for developing a knowledge-guided deep learning approach, which integrates WCM principles into a long short-term memory (LSTM) model, to estimate field SM using Sentinel-1 Synthetic Aperture Radar (SAR) data. Our proposed approach leverages LSTM's capacity to capture spatiotemporal dependencies while maintaining physical consistency through a modified dual-component loss function, including a WCM-based semi-physical component and a boundary condition regularisation. The proposed approach is built upon the soil backscatter coefficients isolated from the total backscatter, together with Landsat-resolution vegetation information and surface characteristics. A four-fold spatial cross-validation was performed against in-situ SM data to assess the model performance. Results showed the proposed approach reduced SM retrieval uncertainties by 0.02 m$^3$/m$^3$ and achieved correlation coefficients (R) of up to 0.64 in areas with varying vegetation cover and surface conditions, demonstrating the potential to address the over-simplification in WCM.
Efficient and robust 3D blind harmonization for large domain gaps
Authors: Hwihun Jeong, Hayeon Lee, Se Young Chun, Jongho Lee
Date: 2025-04-30 00:00:00
Tasks: Hallucination, Image Harmonization

Blind harmonization has emerged as a promising technique for MR image harmonization to achieve scale-invariant representations, requiring only target domain data (i.e., no source domain data necessary). However, existing methods face limitations such as inter-slice heterogeneity in 3D, moderate image quality, and limited performance for a large domain gap. To address these challenges, we introduce BlindHarmonyDiff, a novel blind 3D harmonization framework that leverages an edge-to-image model tailored specifically to harmonization. Our framework employs a 3D rectified flow trained on target domain images to reconstruct the original image from an edge map, then yielding a harmonized image from the edge of a source domain image. We propose multi-stride patch training for efficient 3D training and a refinement module for robust inference by suppressing hallucination. Extensive experiments demonstrate that BlindHarmonyDiff outperforms prior arts by harmonizing diverse source domain images to the target domain, achieving higher correspondence to the target domain characteristics. Downstream task-based quality assessments such as tissue segmentation and age prediction on diverse MR scanners further confirm the effectiveness of our approach and demonstrate the capability of our robust and generalizable blind harmonization.
Stereo X-ray tomography on deformed object tracking
Authors: Zhenduo Shang, Thomas Blumensath
Date: 2025-04-30 00:00:00
Tasks: Object, Object Tracking, Position

X-ray computed tomography is a powerful tool for volumetric imaging, but requires the collection of a large number of low-noise projection images, which is often too time consuming, limiting its applicability. In our previous work \cite{shang2023stereo}, we proposed a stereo X-ray tomography system to map the 3D position of fiducial markers using only two projections of a static volume. In dynamic imaging settings, where objects undergo deformations during imaging, this static method can be extended by utilizing additional temporal information. We thus extend the method to track the deformation of fiducial markers in 3D space, where we use knowledge of the initial object shape as prior information, improving the prediction of the evolution of its deformed state over time. In particular, knowledge of the initial object's stereo projections is shown to improve the method's robustness to noise when detecting fiducial marker locations in the projections of the deformed objects. Furthermore, after feature detection, by using the features' initial 3D position information in the undeformed object, we can also demonstrate improvements in the 3D mapping of the deformed features. Using a range of deformed 3D objects, this new approach is shown to be able to track fiducial markers in noisy stereo tomography images with subpixel accuracy.
Rootlets-based registration to the spinal cord PAM50 template
Authors: Sandrine Bédard, Jan Valošek, Valeria Oliva, Kenneth A. Weber II, Julien Cohen-Adad
Date: 2025-04-30 00:00:00

Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxelwise group analyses. Traditional template-based registration of the spinal cord uses intervertebral discs for alignment. However, substantial anatomical variability across individuals exists between vertebral and spinal levels. This study proposes a novel registration approach that leverages spinal nerve rootlets to improve alignment accuracy and reproducibility across individuals. We developed a registration method leveraging dorsal cervical rootlets segmentation and aligning them non-linearly with the PAM50 spinal cord template. Validation was performed on a multi-subject, multi-site dataset (n=267, 44 sites) and a multi-subject dataset with various neck positions (n=10, 3 sessions). We further validated the method on task-based functional MRI (n=23) to compare group-level activation maps using rootlet-based registration to traditional disc-based methods. Rootlet-based registration showed superior alignment across individuals compared to the traditional disc-based method. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional MRI using rootlet-based increased Z scores and activation cluster size compared to disc-based registration (number of active voxels from 3292 to 7978). Rootlet-based registration enhances both inter- and intra-subject anatomical alignment and yields better spatial normalization for group-level fMRI analyses. Our findings highlight the potential of rootlet-based registration to improve the precision and reliability of spinal cord neuroimaging group analysis.
LoC-LIC: Low Complexity Learned Image Coding Using Hierarchical Feature Transforms
Authors: Ayman A. Ameen, Thomas Richter, André Kaup
Date: 2025-04-30 00:00:00
Tasks: Image Compression

Current learned image compression models typically exhibit high complexity, which demands significant computational resources. To overcome these challenges, we propose an innovative approach that employs hierarchical feature extraction transforms to significantly reduce complexity while preserving bit rate reduction efficiency. Our novel architecture achieves this by using fewer channels for high spatial resolution inputs/feature maps. On the other hand, feature maps with a large number of channels have reduced spatial dimensions, thereby cutting down on computational load without sacrificing performance. This strategy effectively reduces the forward pass complexity from \(1256 \, \text{kMAC/Pixel}\) to just \(270 \, \text{kMAC/Pixel}\). As a result, the reduced complexity model can open the way for learned image compression models to operate efficiently across various devices and pave the way for the development of new architectures in image compression technology.
Assimilation of SWOT Altimetry Data for Riverine Flood Reanalysis: From Synthetic to Real Data
Authors: Quentin Bonassies, Thanh Huy Nguyen, Ludovic Cassan, Andrea Piacentini, Sophie Ricci, Charlotte Emery, Christophe Fatras, Santiago Peña Luque, Raquel Rodriguez Suquet
Date: 2025-04-30 00:00:00
Tasks: Earth Observation

Floods are one of the most common and devastating natural disasters worldwide. The contribution of remote sensing is important for reducing the impact of flooding both during the event itself and for improving hydrodynamic models by reducing their associated uncertainties. This article presents the innovative capabilities of the Surface Water and Ocean Topography (SWOT) mission, especially its river node products, to enhance the accuracy of riverine flood reanalysis, performed on a 50-km stretch of the Garonne River. The experiments incorporate various data assimilation strategies, based on the ensemble Kalman filter (EnKF), which allows for sequential updates of model parameters based on available observations. The experimental results show that while SWOT data alone offers some improvements, combining it with in-situ water level measurements provides the most accurate representation of flood dynamics, both at gauge stations and along the river. The study also investigates the impact of different SWOT revisit frequencies on the models performance, revealing that assimilating more frequent SWOT observations leads to more reliable flood reanalyses. In the real event, it was demonstrated that the assimilation of SWOT and in-situ data accurately reproduces the water level dynamics, offering promising prospects for future flood monitoring systems. Overall, this study emphasizes the complementary strengths of Earth Observation data in improving the representation of the flood dynamics in the riverbed and the floodplains.
Selective Variable Convolution Meets Dynamic Content Guided Attention for Infrared Small Target Detection
Authors: Yirui Chen, Yiming Zhu, Yuxin Jing, Tianpei Zhang, Yuchen Zheng
Date: 2025-04-30 00:00:00

Infrared Small Target Detection (IRSTD) system aims to identify small targets in complex backgrounds. Due to the convolution operation in Convolutional Neural Networks (CNNs), applying traditional CNNs to IRSTD presents challenges, since the feature extraction of small targets is often insufficient, resulting in the loss of critical features. To address these issues, we propose a dynamic content guided attention multiscale feature aggregation network (DCGANet), which adheres to the attention principle of 'coarse-to-fine' and achieves high detection accuracy. First, we propose a selective variable convolution (SVC) module that integrates the benefits of standard convolution, irregular deformable convolution, and multi-rate dilated convolution. This module is designed to expand the receptive field and enhance non-local features, thereby effectively improving the discrimination of targets from backgrounds. Second, the core component of DCGANet is a two-stage content guided attention module. This module employs two-stage attention mechanism to initially direct the network's focus to salient regions within the feature maps and subsequently determine whether these regions correspond to targets or background interference. By retaining the most significant responses, this mechanism effectively suppresses false alarms. Additionally, we propose adaptive dynamic feature fusion (ADFF) module to substitute for static feature cascading. This dynamic feature fusion strategy enables DCGANet to adaptively integrate contextual features, thereby enhancing its ability to discriminate true targets from false alarms. DCGANet has achieved new benchmarks across multiple datasets.
Random Features for Grassmannian Kernels
Authors: Rémi Delogne, Laurent Jacques
Date: 2025-04-30 00:00:00

The Grassmannian manifold G(k, n) serves as a fundamental tool in signal processing, computer vision, and machine learning, where problems often involve classifying, clustering, or comparing subspaces. In this work, we propose a sketching-based approach to approximate Grassmannian kernels using random projections. We introduce three variations of kernel approximation, including two that rely on binarised sketches, offering substantial memory gains. We establish theoretical properties of our method in the special case of G(1, n) and extend it to general G(k, n). Experimental validation demonstrates that our sketched kernels closely match the performance of standard Grassmannian kernels while avoiding the need to compute or store the full kernel matrix. Our approach enables scalable Grassmannian-based methods for large-scale applications in machine learning and pattern recognition.
Emerging Advances in Learned Video Compression: Models, Systems and Beyond
Authors: Chuanmin Jia, Feng Ye, Siwei Ma, Wen Gao, Huifang Sun, Leonardo Chiariglione
Date: 2025-04-30 00:00:00
Tasks: Video Compression

Video compression is a fundamental topic in the visual intelligence, bridging visual signal sensing/capturing and high-level visual analytics. The broad success of artificial intelligence (AI) technology has enriched the horizon of video compression into novel paradigms by leveraging end-to-end optimized neural models. In this survey, we first provide a comprehensive and systematic overview of recent literature on end-to-end optimized learned video coding, covering the spectrum of pioneering efforts in both uni-directional and bi-directional prediction based compression model designation. We further delve into the optimization techniques employed in learned video compression (LVC), emphasizing their technical innovations, advantages. Some standardization progress is also reported. Furthermore, we investigate the system design and hardware implementation challenges of the LVC inclusively. Finally, we present the extensive simulation results to demonstrate the superior compression performance of LVC models, addressing the question that why learned codecs and AI-based video technology would have with broad impact on future visual intelligence research.
SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution
Authors: Taiga Hayami, Kakeru Koizumi, Hiroshi Watanabe
Date: 2025-04-30 00:00:00
Tasks: Super-Resolution, Video Compression

Implicit Neural Representations (INRs) have garnered significant attention for their ability to model complex signals across a variety of domains. Recently, INR-based approaches have emerged as promising frameworks for neural video compression. While conventional methods primarily focus on embedding video content into compact neural networks for efficient representation, they often struggle to reconstruct high-frequency details under stringent model size constraints, which are critical in practical compression scenarios. To address this limitation, we propose an INR-based video representation method that integrates a general-purpose super-resolution (SR) network. Motivated by the observation that high-frequency components exhibit low temporal redundancy across frames, our method entrusts the reconstruction of fine details to the SR network. Experimental results demonstrate that the proposed method outperforms conventional INR-based baselines in terms of reconstruction quality, while maintaining comparable model sizes.
Zoomer: Adaptive Image Focus Optimization for Black-box MLLM
Authors: Jiaxu Qian, Chendong Wang, Yifan Yang, Chaoyun Zhang, Huiqiang Jiang, Xufang Luo, Yu Kang, QIngwei Lin, Anlan Zhang, Shiqi Jiang, Ting Cao, Tianjun Mao, Suman Banerjee, Guyue Liu, Saravan Rajmohan, Dongmei Zhang, Yuqing Yang, Qi Zhang, Lili Qiu
Date: 2025-04-30 00:00:00
Tasks: Image Captioning, Object Recognition, Question Answering, Visual Prompting

Recent advancements in multimodal large language models (MLLMs) have broadened the scope of vision-language tasks, excelling in applications like image captioning and interactive question-answering. However, these models struggle with accurately processing visual data, particularly in tasks requiring precise object recognition and fine visual details. Stringent token limits often result in the omission of critical information, hampering performance. To address these limitations, we introduce \SysName, a novel visual prompting mechanism designed to enhance MLLM performance while preserving essential visual details within token limits. \SysName features three key innovations: a prompt-aware strategy that dynamically highlights relevant image regions, a spatial-preserving orchestration schema that maintains object integrity, and a budget-aware prompting method that balances global context with crucial visual details. Comprehensive evaluations across multiple datasets demonstrate that \SysName consistently outperforms baseline methods, achieving up to a $26.9\%$ improvement in accuracy while significantly reducing token consumption.
MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection
Authors: Qiushi Yang, Yuan YAO, Miaomiao Cui, Liefeng Bo
Date: 2025-04-30 00:00:00
Tasks: Instance Segmentation, Interactive Segmentation, Object, Object Tracking, Segmentation, Semantic Segmentation, Video Instance Segmentation, Video Object Segmentation, Video Semantic Segmentation

The recent Segment Anything Model 2 (SAM2) has demonstrated exceptional capabilities in interactive object segmentation for both images and videos. However, as a foundational model on interactive segmentation, SAM2 performs segmentation directly based on mask memory from the past six frames, leading to two significant challenges. Firstly, during inference in videos, objects may disappear since SAM2 relies solely on memory without accounting for object motion information, which limits its long-range object tracking capabilities. Secondly, its memory is constructed from fixed past frames, making it susceptible to challenges associated with object disappearance or occlusion, due to potentially inaccurate segmentation results in memory. To address these problems, we present MoSAM, incorporating two key strategies to integrate object motion cues into the model and establish more reliable feature memory. Firstly, we propose Motion-Guided Prompting (MGP), which represents the object motion in both sparse and dense manners, then injects them into SAM2 through a set of motion-guided prompts. MGP enables the model to adjust its focus towards the direction of motion, thereby enhancing the object tracking capabilities. Furthermore, acknowledging that past segmentation results may be inaccurate, we devise a Spatial-Temporal Memory Selection (ST-MS) mechanism that dynamically identifies frames likely to contain accurate segmentation in both pixel- and frame-level. By eliminating potentially inaccurate mask predictions from memory, we can leverage more reliable memory features to exploit similar regions for improving segmentation results. Extensive experiments on various benchmarks of video object segmentation and video instance segmentation demonstrate that our MoSAM achieves state-of-the-art results compared to other competitors.