pwc-archive/papers-with-abstracts huggingface dataset:
Project-and-Fuse: Improving RGB-D Semantic Segmentation via Graph Convolution Networks
Authors: Xiaoyan Jiang, Bohan Wang, Xinlong Wan, Shanshan Chen, Hamido Fujita, Hanan Abd. Al Juaid
Date: 2025-01-31 00:00:00
Tasks: Semantic Segmentation

Most existing RGB-D semantic segmentation methods focus on the feature level fusion, including complex cross-modality and cross-scale fusion modules. However, these methods may cause misalignment problem in the feature fusion process and counter-intuitive patches in the segmentation results. Inspired by the popular pixel-node-pixel pipeline, we propose to 1) fuse features from two modalities in a late fusion style, during which the geometric feature injection is guided by texture feature prior; 2) employ Graph Neural Networks (GNNs) on the fused feature to alleviate the emergence of irregular patches by inferring patch relationship. At the 3D feature extraction stage, we argue that traditional CNNs are not efficient enough for depth maps. So, we encode depth map into normal map, after which CNNs can easily extract object surface tendencies.At projection matrix generation stage, we find the existence of Biased-Assignment and Ambiguous-Locality issues in the original pipeline. Therefore, we propose to 1) adopt the Kullback-Leibler Loss to ensure no missing important pixel features, which can be viewed as hard pixel mining process; 2) connect regions that are close to each other in the Euclidean space as well as in the semantic space with larger edge weights so that location informations can been considered. Extensive experiments on two public datasets, NYU-DepthV2 and SUN RGB-D, have shown that our approach can consistently boost the performance of RGB-D semantic segmentation task.
FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM
Authors: Xinlong Wan, Xiaoyan Jiang, GuangSheng Luo, Ferdous Sohel, JenqNeng Hwang
Date: 2025-01-31 00:00:00
Tasks: Computational Efficiency, Crack Segmentation, General Knowledge, Segmentation, Structural Health Monitoring, Zero-shot Generalization

Automatic crack segmentation is a cornerstone technology for intelligent visual perception modules in road safety maintenance and structural integrity systems. Existing deep learning models and ``pre-training + fine-tuning'' paradigms often face challenges of limited adaptability in resource-constrained environments and inadequate scalability across diverse data domains. To overcome these limitations, we propose FlexiCrackNet, a novel pipeline that seamlessly integrates traditional deep learning paradigms with the strengths of large-scale pre-trained models. At its core, FlexiCrackNet employs an encoder-decoder architecture to extract task-specific features. The lightweight EdgeSAM's CNN-based encoder is exclusively used as a generic feature extractor, decoupled from the fixed input size requirements of EdgeSAM. To harmonize general and domain-specific features, we introduce the information-Interaction gated attention mechanism (IGAM), which adaptively fuses multi-level features to enhance segmentation performance while mitigating irrelevant noise. This design enables the efficient transfer of general knowledge to crack segmentation tasks while ensuring adaptability to diverse input resolutions and resource-constrained environments. Experiments show that FlexiCrackNet outperforms state-of-the-art methods, excels in zero-shot generalization, computational efficiency, and segmentation robustness under challenging scenarios such as blurry inputs, complex backgrounds, and visually ambiguous artifacts. These advancements underscore the potential of FlexiCrackNet for real-world applications in automated crack detection and comprehensive structural health monitoring systems.
Test-time Loss Landscape Adaptation for Zero-Shot Generalization in Vision-Language Models
Authors: Aodi Li, Liansheng Zhuang, Xiao Long, Minghong Yao, Shafei Wang
Date: 2025-01-31 00:00:00
Tasks: Domain Generalization, Test-time Adaptation, Zero-shot Generalization

Test-time adaptation of pre-trained vision-language models has emerged as a technique for tackling distribution shifts during the test time. Although existing methods, especially those based on Test-time Prompt Tuning (TPT), have shown promising results, their high computational cost associated with parameter optimization presents challenges for scalability and practical application. This paper unveils the unnecessary nature of backpropagation in existing methods from a loss landscape perspective. Building on this insight, this paper proposes a simple yet effective framework called Test-time Loss Landscape Adaptation (TLLA). TLLA leverages the relative position between the training minimum and test loss landscapes to guide the adaptation process, avoiding the update of model parameters at test time. Specifically, it mainly consists of two main stages: In the prompt tuning stage, a Sharpness-Aware Prompt Tuning (SAPT) method is introduced to identify the training flat minimum, setting the foundation for the subsequent test-time adaptation; In the test stage, a Sharpness-based Test Sample Selection (STSS) approach is utilized to ensure the alignment of flat minima within the training loss landscape and each augmented test sample's loss landscape. Extensive experiments on both domain generalization and cross-dataset benchmarks demonstrate that TLLA achieves state-of-the-art performances while significantly reducing computational overhead. Notably, TLLA surpasses TPT by an average of 5.32\% and 6.98\% on four ImageNet variant datasets when employing ResNet50 and ViT-B/16 image encoders, respectively. The code will be available soon.
Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study
Authors: W. M. Yomal De Mel, Nisansa de Silva
Date: 2025-01-28 00:00:00
Tasks: Emotion Recognition, Information Retrieval, Music Emotion Recognition, Music Information Retrieval, Transliteration

This research investigates the area of Music Information Retrieval (MIR) and Music Emotion Recognition (MER) in relation to Sinhala songs, an underexplored field in music studies. The purpose of this study is to analyze the behavior of Sinhala comments on YouTube Sinhala song videos using social media comments as primary data sources. These included comments from 27 YouTube videos containing 20 different Sinhala songs, which were carefully selected so that strict linguistic reliability would be maintained and relevancy ensured. This process led to a total of 93,116 comments being gathered upon which the dataset was refined further by advanced filtering methods and transliteration mechanisms resulting into 63,471 Sinhala comments. Additionally, 964 stop-words specific for the Sinhala language were algorithmically derived out of which 182 matched exactly with English stop-words from NLTK corpus once translated. Also, comparisons were made between general domain corpora in Sinhala against the YouTube Comment Corpus in Sinhala confirming latter as good representation of general domain. The meticulously curated data set as well as the derived stop-words form important resources for future research in the fields of MIR and MER, since they could be used and demonstrate that there are possibilities with computational techniques to solve complex musical experiences across varied cultural traditions
Prompt-oriented Output of Culture-Specific Items in Translated African Poetry by Large Language Model: An Initial Multi-layered Tabular Review
Authors: Adeyola Opaluwah
Date: 2025-01-29 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Specificity, Translation

This paper examines the output of cultural items generated by Chat Generative PreTrained Transformer Pro in response to three structured prompts to translate three anthologies of African poetry. The first prompt was broad, the second focused on poetic structure, and the third prompt emphasized cultural specificity. To support this analysis, four comparative tables were created. The first table presents the results of the cultural items produced after the three prompts, the second categorizes these outputs based on Aixela framework of Proper nouns and Common expressions, the third table summarizes the cultural items generated by human translators, a custom translation engine, and a Large Language Model. The final table outlines the strategies employed by Chat Generative PreTrained Transformer Pro following the culture specific prompt. Compared to the outputs of cultural items from reference human translation and the custom translation engine in prior studies the findings indicate that the culture oriented prompts used with Chat Generative PreTrained Transformer Pro did not yield significant enhancements of cultural items during the translation of African poetry from English to French. Among the fifty four cultural items, the human translation produced thirty three cultural items in repetition, the custom translation engine generated Thirty eight cultural items in repetition while Chat Generative PreTrained Transformer Pro produced forty one cultural items in repetition. The untranslated cultural items revealed inconsistencies in Large language models approach to translating cultural items in African poetry from English to French.
Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation
Authors: Renhao Lu
Date: 2025-02-01 00:00:00
Tasks: Semantic Segmentation

Recent advancements in deep neural networks have significantly enhanced the performance of semantic segmentation. However, class imbalance and instance imbalance remain persistent challenges, where smaller instances and thin boundaries are often overshadowed by larger structures. To address the multiscale nature of segmented objects, various models have incorporated mechanisms such as spatial attention and feature pyramid networks. Despite these advancements, most loss functions are still primarily pixel-wise, while regional and boundary-focused loss functions often incur high computational costs or are restricted to small-scale regions. To address this limitation, we propose the complex wavelet mutual information (CWMI) loss, a novel loss function that leverages mutual information from subband images decomposed by a complex steerable pyramid. The complex steerable pyramid captures features across multiple orientations and preserves structural similarity across scales. Meanwhile, mutual information is well-suited to capturing high-dimensional directional features and offers greater noise robustness. Extensive experiments on diverse segmentation datasets demonstrate that CWMI loss achieves significant improvements in both pixel-wise accuracy and topological metrics compared to state-of-the-art methods, while introducing minimal computational overhead. Our code is available at https://github.com/lurenhaothu/CWMI
A Multi-Modal Unsupervised Machine Learning Approach for Biomedical Signal Processing in CPR
Authors: Saidul Islam, Jamal Bentahar, Robin Cohen, Gaith Rjoub
Date: 2024-11-03 00:00:00
Tasks: Decision Making, Denoising, Respiratory Failure

Cardiopulmonary resuscitation (CPR) is a critical, life-saving intervention aimed at restoring blood circulation and breathing in individuals experiencing cardiac arrest or respiratory failure. Accurate and real-time analysis of biomedical signals during CPR is essential for monitoring and decision-making, from the pre-hospital stage to the intensive care unit (ICU). However, CPR signals are often corrupted by noise and artifacts, making precise interpretation challenging. Traditional denoising methods, such as filters, struggle to adapt to the varying and complex noise patterns present in CPR signals. Given the high-stakes nature of CPR, where rapid and accurate responses can determine survival, there is a pressing need for more robust and adaptive denoising techniques. In this context, an unsupervised machine learning (ML) methodology is particularly valuable, as it removes the dependence on labeled data, which can be scarce or impractical in emergency scenarios. This paper introduces a novel unsupervised ML approach for denoising CPR signals using a multi-modality framework, which leverages multiple signal sources to enhance the denoising process. The proposed approach not only improves noise reduction and signal fidelity but also preserves critical inter-signal correlations (0.9993) which is crucial for downstream tasks. Furthermore, it outperforms existing methods in an unsupervised context in terms of signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR), making it highly effective for real-time applications. The integration of multi-modality further enhances the system's adaptability to various biomedical signals beyond CPR, improving both automated CPR systems and clinical decision-making.
SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models
Authors: Chuc Man Duc, Hiromichi Fukui
Date: 2025-02-01 00:00:00
Tasks: Earth Observation, State Space Models

Foundation models refer to deep learning models pretrained on large unlabeled datasets through self-supervised algorithms. In the Earth science and remote sensing communities, there is growing interest in transforming the use of Earth observation data, including satellite and aerial imagery, through foundation models. Various foundation models have been developed for remote sensing, such as those for multispectral, high-resolution, and hyperspectral images, and have demonstrated superior performance on various downstream tasks compared to traditional supervised models. These models are evolving rapidly, with capabilities to handle multispectral, multitemporal, and multisensor data. Most studies use masked autoencoders in combination with Vision Transformers (ViTs) as the backbone for pretraining. While the models showed promising performance, ViTs face challenges, such as quadratic computational scaling with input length, which may limit performance on multiband and multitemporal data with long sequences. This research aims to address these challenges by proposing SatMamba, a new pretraining framework that combines masked autoencoders with State Space Model, offering linear computational scaling. Experiments on high-resolution imagery across various downstream tasks show promising results, paving the way for more efficient foundation models and unlocking the full potential of Earth observation data. The source code is available in https://github.com/mdchuc/HRSFM.
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective
Authors: Yujin Oh, Pengfei Jin, Sangjoon Park, Sekeun Kim, Siyeop Yoon, Kyungsang Kim, Jin Sung Kim, Xiang Li, Quanzheng Li
Date: 2025-02-02 00:00:00
Tasks: Fairness, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Mixture-of-Experts, Segmentation, Semantic Segmentation

Ensuring fairness in medical image segmentation is critical due to biases in imbalanced clinical data acquisition caused by demographic attributes (e.g., age, sex, race) and clinical factors (e.g., disease severity). To address these challenges, we introduce Distribution-aware Mixture of Experts (dMoE), inspired by optimal control theory. We provide a comprehensive analysis of its underlying mechanisms and clarify dMoE's role in adapting to heterogeneous distributions in medical image segmentation. Furthermore, we integrate dMoE into multiple network architectures, demonstrating its broad applicability across diverse medical image analysis tasks. By incorporating demographic and clinical factors, dMoE achieves state-of-the-art performance on two 2D benchmark datasets and a 3D in-house dataset. Our results highlight the effectiveness of dMoE in mitigating biases from imbalanced distributions, offering a promising approach to bridging control theory and medical image segmentation within fairness learning paradigms. The source code will be made available. The source code is available at https://github.com/tvseg/dMoE.
GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation
Authors: Linhao Luo, Zicheng Zhao, Gholamreza Haffari, Dinh Phung, Chen Gong, Shirui Pan
Date: 2025-02-03 00:00:00
Tasks: Graph Neural Network, Knowledge Graphs, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-augmented generation (RAG) has proven effective in integrating knowledge into large language models (LLMs). However, conventional RAGs struggle to capture complex relationships between pieces of knowledge, limiting their performance in intricate reasoning that requires integrating knowledge from multiple sources. Recently, graph-enhanced retrieval augmented generation (GraphRAG) builds graph structure to explicitly model these relationships, enabling more effective and efficient retrievers. Nevertheless, its performance is still hindered by the noise and incompleteness within the graph structure. To address this, we introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation. GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships. The GFM with 8M parameters undergoes a two-stage training process on large-scale datasets, comprising 60 knowledge graphs with over 14M triples and 700k documents. This results in impressive performance and generalizability for GFM-RAG, making it the first graph foundation model applicable to unseen datasets for retrieval without any fine-tuning required. Extensive experiments on three multi-hop QA datasets and seven domain-specific RAG datasets demonstrate that GFM-RAG achieves state-of-the-art performance while maintaining efficiency and alignment with neural scaling laws, highlighting its potential for further improvement.
Boosting Adversarial Robustness and Generalization with Structural Prior
Authors: Zhichao Hou, Weizhi Gao, Hamid Krim, Xiaorui Liu
Date: 2025-02-02 00:00:00
Tasks: Adversarial Robustness, Deep Learning, Dictionary Learning

This work investigates a novel approach to boost adversarial robustness and generalization by incorporating structural prior into the design of deep learning models. Specifically, our study surprisingly reveals that existing dictionary learning-inspired convolutional neural networks (CNNs) provide a false sense of security against adversarial attacks. To address this, we propose Elastic Dictionary Learning Networks (EDLNets), a novel ResNet architecture that significantly enhances adversarial robustness and generalization. This novel and effective approach is supported by a theoretical robustness analysis using influence functions. Moreover, extensive and reliable experiments demonstrate consistent and significant performance improvement on open robustness leaderboards such as RobustBench, surpassing state-of-the-art baselines. To the best of our knowledge, this is the first work to discover and validate that structural prior can reliably enhance deep learning robustness under strong adaptive attacks, unveiling a promising direction for future research.
Self-Organizing Interaction Spaces: A Framework for Engineering Pervasive Applications in Mobile and Distributed Environments
Authors: Shubham Malhotra
Date: 2025-02-03 00:00:00
Tasks: Cloud Computing

The rapid adoption of pervasive and mobile computing has led to an unprecedented rate of data production and consumption by mobile applications at the network edge. These applications often require interactions such as data exchange, behavior coordination, and collaboration, which are typically mediated by cloud servers. While cloud computing has been effective for distributed systems, challenges like latency, cost, and intermittent connectivity persist. With the advent of 5G technology, features like location-awareness and device-to-device (D2D) communication enable a more distributed and adaptive architecture. This paper introduces Self-Organizing Interaction Spaces (SOIS), a novel framework for engineering pervasive applications. SOIS leverages the dynamic and heterogeneous nature of mobile nodes, allowing them to form adaptive organizational structures based on their individual and social contexts. The framework provides two key abstractions for modeling and programming pervasive applications using an organizational mindset and mechanisms for adapting dynamic organizational structures. Case examples and performance evaluations of a simulated mobile crowd-sensing application demonstrate the feasibility and benefits of SOIS. Results highlight its potential to enhance efficiency and reduce reliance on traditional cloud models, paving the way for innovative solutions in mobile and distributed environments.
Partial Channel Network: Compute Fewer, Perform Better
Authors: Haiduo Huang, Tian Xia, Wenzhe Zhao, Pengju Ren
Date: 2025-02-03 00:00:00

Designing a module or mechanism that enables a network to maintain low parameters and FLOPs without sacrificing accuracy and throughput remains a challenge. To address this challenge and exploit the redundancy within feature map channels, we propose a new solution: partial channel mechanism (PCM). Specifically, through the split operation, the feature map channels are divided into different parts, with each part corresponding to different operations, such as convolution, attention, pooling, and identity mapping. Based on this assumption, we introduce a novel partial attention convolution (PATConv) that can efficiently combine convolution with visual attention. Our exploration indicates that the PATConv can completely replace both the regular convolution and the regular visual attention while reducing model parameters and FLOPs. Moreover, PATConv can derive three new types of blocks: Partial Channel-Attention block (PAT_ch), Partial Spatial-Attention block (PAT_sp), and Partial Self-Attention block (PAT_sf). In addition, we propose a novel dynamic partial convolution (DPConv) that can adaptively learn the proportion of split channels in different layers to achieve better trade-offs. Building on PATConv and DPConv, we propose a new hybrid network family, named PartialNet, which achieves superior top-1 accuracy and inference speed compared to some SOTA models on ImageNet-1K classification and excels in both detection and segmentation on the COCO dataset. Our code is available at https://github.com/haiduo/PartialNet.