pwc-archive/papers-with-abstracts huggingface dataset:
Noise Synthesis for Low-Light Image Denoising with Diffusion Models
Authors: Liying Lu, Raphaël Achddou, Sabine Süsstrunk
Date: 2025-03-14 00:00:00
Tasks: Denoising, Image Denoising

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep-learning methods perform well, they require large datasets of paired images that are impractical to acquire. As a remedy, synthesizing realistic low-light noise has gained significant attention. In this paper, we investigate the ability of diffusion models to capture the complex distribution of low-light noise. We show that a naive application of conventional diffusion models is inadequate for this task and propose three key adaptations that enable high-precision noise generation without calibration or post-processing: a two-branch architecture to better model signal-dependent and signal-independent noise, the incorporation of positional information to capture fixed-pattern noise, and a tailored diffusion noise schedule. Consequently, our model enables the generation of large datasets for training low-light denoising networks, leading to state-of-the-art performance. Through comprehensive analysis, including statistical evaluation and noise decomposition, we provide deeper insights into the characteristics of the generated data.
Deep Lossless Image Compression via Masked Sampling and Coarse-to-Fine Auto-Regression
Authors: Tiantian Li, Qunbing Xia, Yue Li, Ruixiao Guo, Gaobo Yang
Date: 2025-03-14 00:00:00
Tasks: Image Compression, regression

Learning-based lossless image compression employs pixel-based or subimage-based auto-regression for probability estimation, which achieves desirable performances. However, the existing works only consider context dependencies in one direction, namely, those symbols that appear before the current symbol in raster order. We believe that the dependencies between the current and future symbols should be further considered. In this work, we propose a deep lossless image compression via masked sampling and coarse-to-fine auto-regression. It combines lossy reconstruction and progressive residual compression, which fuses contexts from various directions and is more consistent with human perception. Specifically, the residuals are decomposed via $T$ iterative masked sampling, and each sampling consists of three steps: 1) probability estimation, 2) mask computation, and 3) arithmetic coding. The iterative process progressively refines our prediction and gradually presents a real image. Extensive experimental results show that compared with the existing traditional and learned lossless compression, our method achieves comparable compression performance on extensive datasets with competitive coding speed and more flexibility.
Simulating Dual-Pixel Images From Ray Tracing For Depth Estimation
Authors: Fengchen He, Dayang Zhao, Hao Xu, Tingwei Quan, Shaoqun Zeng
Date: 2025-03-14 00:00:00
Tasks: Deblurring, Depth Estimation

Many studies utilize dual-pixel (DP) sensor phase characteristics for various applications, such as depth estimation and deblurring. However, since the DP image features are entirely determined by the camera hardware, DP-depth paired datasets are very scarce, especially when performing depth estimation on customized cameras. To overcome this, studies simulate DP images using ideal optical system models. However, these simulations often violate real optical propagation laws,leading to poor generalization to real DP data. To address this, we investigate the domain gap between simulated and real DP data, and propose solutions using the Simulating DP images from ray tracing (Sdirt) scheme. The Sdirt generates realistic DP images via ray tracing and integrates them into the depth estimation training pipeline. Experimental results show that models trained with Sdirt-simulated images generalize better to real DP data.
SpaceSeg: A High-Precision Intelligent Perception Segmentation Method for Multi-Spacecraft On-Orbit Targets
Authors: Hao liu, Pengyu Guo, Siyuan Yang, Zeqing Jiang, Qinglei Hu, Dongyu Li
Date: 2025-03-14 00:00:00
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation

With the continuous advancement of human exploration into deep space, intelligent perception and high-precision segmentation technology for on-orbit multi-spacecraft targets have become critical factors for ensuring the success of modern space missions. However, the complex deep space environment, diverse imaging conditions, and high variability in spacecraft morphology pose significant challenges to traditional segmentation methods. This paper proposes SpaceSeg, an innovative vision foundation model-based segmentation framework with four core technical innovations: First, the Multi-Scale Hierarchical Attention Refinement Decoder (MSHARD) achieves high-precision feature decoding through cross-resolution feature fusion via hierarchical attention. Second, the Multi-spacecraft Connected Component Analysis (MS-CCA) effectively resolves topological structure confusion in dense targets. Third, the Spatial Domain Adaptation Transform framework (SDAT) eliminates cross-domain disparities and resist spatial sensor perturbations through composite enhancement strategies. Finally, a custom Multi-Spacecraft Segmentation Task Loss Function is created to significantly improve segmentation robustness in deep space scenarios. To support algorithm validation, we construct the first multi-scale on-orbit multi-spacecraft semantic segmentation dataset SpaceES, which covers four types of spatial backgrounds and 17 typical spacecraft targets. In testing, SpaceSeg achieves state-of-the-art performance with 89.87$\%$ mIoU and 99.98$\%$ mAcc, surpassing existing best methods by 5.71 percentage points. The dataset and code are open-sourced at https://github.com/Akibaru/SpaceSeg to provide critical technical support for next-generation space situational awareness systems.
Prototype-Guided Cross-Modal Knowledge Enhancement for Adaptive Survival Prediction
Authors: Fengchun Liu, Linghan Cai, Zhikang Wang, Zhiyuan Fan, Jin-Gang Yu, Hao Chen, Yongbing Zhang
Date: 2025-03-13 00:00:00
Tasks: Survival Prediction

Histo-genomic multimodal survival prediction has garnered growing attention for its remarkable model performance and potential contributions to precision medicine. However, a significant challenge in clinical practice arises when only unimodal data is available, limiting the usability of these advanced multimodal methods. To address this issue, this study proposes a prototype-guided cross-modal knowledge enhancement (ProSurv) framework, which eliminates the dependency on paired data and enables robust learning and adaptive survival prediction. Specifically, we first introduce an intra-modal updating mechanism to construct modality-specific prototype banks that encapsulate the statistics of the whole training set and preserve the modality-specific risk-relevant features/prototypes across intervals. Subsequently, the proposed cross-modal translation module utilizes the learned prototypes to enhance knowledge representation for multimodal inputs and generate features for missing modalities, ensuring robust and adaptive survival prediction across diverse scenarios. Extensive experiments on four public datasets demonstrate the superiority of ProSurv over state-of-the-art methods using either unimodal or multimodal input, and the ablation study underscores its feasibility for broad applicability. Overall, this study addresses a critical practical challenge in computational pathology, offering substantial significance and potential impact in the field.
Zero-Shot Subject-Centric Generation for Creative Application Using Entropy Fusion
Authors: Kaifeng Zou, Xiaoyi Feng, Peng Wang, Tao Huang, Zizhou Huang, Zhang Haihang, Yuntao Zou, Dagang Li
Date: 2025-03-12 00:00:00
Tasks: Descriptive, Image Generation

Generative models are widely used in visual content creation. However, current text-to-image models often face challenges in practical applications-such as textile pattern design and meme generation-due to the presence of unwanted elements that are difficult to separate with existing methods. Meanwhile, subject-reference generation has emerged as a key research trend, highlighting the need for techniques that can produce clean, high-quality subject images while effectively removing extraneous components. To address this challenge, we introduce a framework for reliable subject-centric image generation. In this work, we propose an entropy-based feature-weighted fusion method to merge the informative cross-attention features obtained from each sampling step of the pretrained text-to-image model FLUX, enabling a precise mask prediction and subject-centric generation. Additionally, we have developed an agent framework based on Large Language Models (LLMs) that translates users' casual inputs into more descriptive prompts, leading to highly detailed image generation. Simultaneously, the agents extract primary elements of prompts to guide the entropy-based feature fusion, ensuring focused primary element generation without extraneous components. Experimental results and user studies demonstrate our methods generates high-quality subject-centric images, outperform existing methods or other possible pipelines, highlighting the effectiveness of our approach.
Neighboring Autoregressive Modeling for Efficient Visual Generation
Authors: Yefei He, Yuanyu He, Shaoxuan He, Feng Chen, Hong Zhou, Kaipeng Zhang, Bohan Zhuang
Date: 2025-03-12 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation, Video Generation

Visual autoregressive models typically adhere to a raster-order ``next-token prediction" paradigm, which overlooks the spatial and temporal locality inherent in visual content. Specifically, visual tokens exhibit significantly stronger correlations with their spatially or temporally adjacent tokens compared to those that are distant. In this paper, we propose Neighboring Autoregressive Modeling (NAR), a novel paradigm that formulates autoregressive visual generation as a progressive outpainting procedure, following a near-to-far ``next-neighbor prediction" mechanism. Starting from an initial token, the remaining tokens are decoded in ascending order of their Manhattan distance from the initial token in the spatial-temporal space, progressively expanding the boundary of the decoded region. To enable parallel prediction of multiple adjacent tokens in the spatial-temporal space, we introduce a set of dimension-oriented decoding heads, each predicting the next token along a mutually orthogonal dimension. During inference, all tokens adjacent to the decoded tokens are processed in parallel, substantially reducing the model forward steps for generation. Experiments on ImageNet$256\times 256$ and UCF101 demonstrate that NAR achieves 2.4$\times$ and 8.6$\times$ higher throughput respectively, while obtaining superior FID/FVD scores for both image and video generation tasks compared to the PAR-4X approach. When evaluating on text-to-image generation benchmark GenEval, NAR with 0.8B parameters outperforms Chameleon-7B while using merely 0.4 of the training data. Code is available at https://github.com/ThisisBillhe/NAR.
Knowledge Consultation for Semi-Supervised Semantic Segmentation
Authors: Thuan Than, Nhat-Anh Nguyen-Dang, Dung Nguyen, Salwa K. Al Khatib, Ahmed Elhagry, Hai Phan, Yihui He, Zhiqiang Shen, Marios Savvides, Dang Huynh
Date: 2025-03-12 00:00:00
Tasks: Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

Semi-Supervised Semantic Segmentation reduces reliance on extensive annotations by using unlabeled data and state-of-the-art models to improve overall performance. Despite the success of deep co-training methods, their underlying mechanisms remain underexplored. This work revisits Cross Pseudo Supervision with dual heterogeneous backbones and introduces Knowledge Consultation (SegKC) to further enhance segmentation performance. The proposed SegKC achieves significant improvements on Pascal and Cityscapes benchmarks, with mIoU scores of 87.1%, 89.2%, and 89.8% on Pascal VOC with the 1/4, 1/2, and full split partition, respectively, while maintaining a compact model architecture.
MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation
Authors: Anzhe Cheng, Chenzhong Yin, Yu Chang, Heng Ping, Shixuan Li, Shahin Nazarian, Paul Bogdan
Date: 2025-03-11 00:00:00
Tasks: Image Segmentation, Panoptic Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

Low-resolution image segmentation is crucial in real-world applications such as robotics, augmented reality, and large-scale scene understanding, where high-resolution data is often unavailable due to computational constraints. To address this challenge, we propose MaskAttn-UNet, a novel segmentation framework that enhances the traditional U-Net architecture via a mask attention mechanism. Our model selectively emphasizes important regions while suppressing irrelevant backgrounds, thereby improving segmentation accuracy in cluttered and complex scenes. Unlike conventional U-Net variants, MaskAttn-UNet effectively balances local feature extraction with broader contextual awareness, making it particularly well-suited for low-resolution inputs. We evaluate our approach on three benchmark datasets with input images rescaled to 128x128 and demonstrate competitive performance across semantic, instance, and panoptic segmentation tasks. Our results show that MaskAttn-UNet achieves accuracy comparable to state-of-the-art methods at significantly lower computational cost than transformer-based models, making it an efficient and scalable solution for low-resolution segmentation in resource-constrained scenarios.
VFM-UDA++: Improving Network Architectures and Data Strategies for Unsupervised Domain Adaptive Semantic Segmentation
Authors: Brunó B. Englert, Gijs Dubbelman
Date: 2025-03-11 00:00:00
Tasks: Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) has shown remarkably strong generalization from a labeled source domain to an unlabeled target domain while requiring relatively little data. At the same time, large-scale pretraining without labels of so-called Vision Foundation Models (VFMs), has also significantly improved downstream generalization. This motivates us to research how UDA can best utilize the benefits of VFMs. The earlier work of VFM-UDA showed that beyond state-of-the-art (SotA) results can be obtained by replacing non-VFM with VFM encoders in SotA UDA methods. In this work, we take it one step further and improve on the UDA architecture and data strategy themselves. We observe that VFM-UDA, the current SotA UDA method, does not use multi-scale inductive biases or feature distillation losses, while it is known that these can improve generalization. We address both limitations in VFM-UDA++ and obtain beyond SotA generalization on standard UDA benchmarks of up to +5.3 mIoU. Inspired by work on VFM fine-tuning, such as Rein, we also explore the benefits of adding more easy-to-generate synthetic source data with easy-to-obtain unlabeled target data and realize a +6.6 mIoU over the current SotA. The improvements of VFM-UDA++ are most significant for smaller models, however, we show that for larger models, the obtained generalization is only 2.8 mIoU from that of fully-supervised learning with all target labels. Based on these strong results, we provide essential insights to help researchers and practitioners advance UDA.
Goal-oriented Spectrum Sharing: Trading Edge Inference Power for Data Streaming Performance
Authors: Mattia Merluzzi, Miltiadis C. Filippou
Date: 2025-03-14 00:00:00
Tasks: Distributed Computing

We study the problem of spectrum sharing between goal-oriented (GO) and legacy data-oriented (DO) systems. For the former, data quality and representation is no longer optimized based on classical communication key performance indicators, but rather configured on the fly to achieve the goal of communication with the least resource overhead. This paradigm can be followed to flexibly adapt wireless and in-network artificial intelligence operations across different nodes (e.g., access points, users, sensors or actuators) to data traffic, channel conditions, energy availability and distributed computing capabilities. In this paper, we argue and demonstrate that computing and learning/inference operation performance strongly affect lower layers, calling for a real cross-layer optimization that encompasses physical and computation resource orchestration, up to the application level. Focusing on a communication channel shared among a GO and a DO user, we define a goal-effective achievable rate region (GEARR), to assess the maximum data rate attainable by the latter, subject to goal achievement guarantees for the former. Finally, we propose a cross-layer dynamic resource orchestration able to reach the boundaries of the GEARR, under different goal-effectiveness and compute resource consumption constraints.
Topological Dictionary Learning
Authors: Enrico Grimaldi, Claudio Battiloro, Paolo Di Lorenzo
Date: 2025-03-14 00:00:00
Tasks: Dictionary Learning

The aim of this paper is to introduce a novel dictionary learning algorithm for sparse representation of signals defined over combinatorial topological spaces, specifically, regular cell complexes. Leveraging Hodge theory, we embed topology into the dictionary structure via concatenated sub-dictionaries, each as a polynomial of Hodge Laplacians, yielding localized spectral topological filter frames. The learning problem is cast to jointly infer the underlying cell complex and optimize the dictionary coefficients and the sparse signal representation. We efficiently solve the problem via iterative alternating algorithms. Numerical results on both synthetic and real data show the effectiveness of the proposed procedure in jointly learning the sparse representations and the underlying relational structure of topological signals.
Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks
Authors: Ali Elkeshawy, Haifa Fares, Amor Nafkha
Date: 2025-03-14 00:00:00
Tasks: Action Detection, Activity Detection

Grant-free random access (GF-RA) is a promising access technique for massive machine-type communications (mMTC) in future wireless networks, particularly in the context of 5G and beyond (6G) systems. Within the context of GF-RA, this study investigates the efficiency of employing supervised machine learning techniques to tackle the challenges on the device activity detection (AD). GF-RA addresses scalability by employing non-orthogonal pilot sequences, which provides an efficient alternative comparing to conventional grant-based random access (GB-RA) technique that are constrained by the scarcity of orthogonal preamble resources. In this paper, we propose a novel lightweight data-driven algorithmic framework specifically designed for activity detection in GF-RA for mMTC in cell-free massive multiple-input multiple-output (CF-mMIMO) networks. We propose two distinct framework deployment strategies, centralized and decentralized, both tailored to streamline the proposed approach implementation across network infrastructures. Moreover, we introduce optimized post-detection methodologies complemented by a clustering stage to enhance overall detection performances. Our 3GPP-compliant simulations have validated that the proposed algorithm achieves state-of-the-art model-based activity detection accuracy while significantly reducing complexity. Achieving 99% accuracy, it demonstrates real-world viability and effectiveness.
Beamforming for Movable and Rotatable Antenna Enabled Multi-User Communications
Authors: Ruojing Zhao, Yifei Xu, Songjie Yang, Hua Chen, Chadi Assi
Date: 2025-03-14 00:00:00
Tasks: Diversity, Position

In the development of wireless communication technology, multiple-input multiple-output (MIMO) technology has emerged as a key enabler, significantly enhancing the capacity of communication systems. However, traditional MIMO systems, which rely on fixed-position antennas (FPAs) with spacing limitations, cannot fully exploit the channel variations in the continuous spatial domain, thus limiting the system's spatial multiplexing performance and diversity. To address these limitations, movable antennas (MAs) have been introduced, offering a breakthrough in signal processing and spatial multiplexing by overcoming the constraints of FPA-based systems. Furthermore, this paper extends the functionality of MAs by introducing movable rotatable antennas (MRAs), which enhance the system's ability to optimize performance in the spatial domain by adding rotational degrees of freedom. By incorporating a dynamic precoding framework based on both antenna position and rotation angle optimization, and employing the zero-forcing (ZF) precoding method, this paper proposes an efficient optimization approach aimed at improving signal quality, mitigating interference, and solving the non-linear, constrained optimization problem using the sequential quadratic programming (SQP) algorithm. This approach effectively enhances the communication system's performance.
Modeling and Optimization for Flexible Cylindrical Arrays-Enabled Wireless Communications
Authors: Songjie Yang, Jiahe Guo, Zilin He, Boyu Ning, Weidong Mei, Zhongpei Zhang, Chadi Assi, Chau Yuen
Date: 2025-03-14 00:00:00
Tasks: Compressive Sensing

Flexible-geometry arrays have garnered much attention in wireless communications, which dynamically adjust wireless channels to improve the system performance. In this paper, we propose a novel flexible-geometry array for a $360^\circ$ coverage, named flxible cylindrical array (FCLA), comprised of multiple flexible circular arrays (FCAs). The elements in each FCA can revolve around the circle track to change their horizontal positions, and the FCAs can move along the vertical axis to change the elements' heights. Considering that horizontal revolving can change the antenna orientation, we adopt both the omni-directional and the directional antenna patterns. Based on the regularized zero-forcing (RZF) precoding scheme, we formulate a particular compressive sensing (CS) problem incorporating joint precoding and antenna position optimization, and propose two effective methods, namely FCLA-J and FCLA-A, to solve it. Specifically, the first method involves jointly optimizing the element's revolving angle, height, and precoding coefficient within a single CS framework. The second method decouples the CS problem into two subproblems by utilizing an alternative sparse optimization approach for the revolving angle and height, thereby reducing time complexity. Simulation results reveal that, when utilizing directional radiation patterns, FCLA-J and FCLA-A achieve substantial performance improvements of 43.32\% and 25.42\%, respectively, compared to uniform cylindrical arrays (UCLAs) with RZF precoding.