pwc-archive/papers-with-abstracts huggingface dataset:
High-Precision Fabric Defect Detection via Adaptive Shape Convolutions and Large Kernel Spatial Modeling
Authors: Shuai Wang, Yang Xu, Hui Zheng, Baotian Li
Date: 2025-01-24 00:00:00
Tasks: Defect Detection

Detecting fabric defects in the textile industry remains a challenging task due to the diverse and complex nature of defect patterns. Traditional methods often suffer from slow inference speeds, limited accuracy, and inadequate recognition rates, particularly in scenarios involving intricate or subtle defects. To overcome these limitations, we introduce Fab-ASLKS, an advanced fabric defect detection framework built upon the YOLOv8s architecture. Fab-ASLKS incorporates two key modules: (1) the Adaptive Shape Convolution Module (ASCM), which leverages adaptive shape convolution within the Neck to enhance feature fusion and improve efficiency by extending the capabilities of the standard C2f structure, and (2) the Large Kernel Shift Convolution Module (LKSCM), designed to emulate large kernel effects within the Backbone, enabling superior spatial information extraction. These modules collaboratively optimize feature extraction and information integration across the network. Extensive experiments conducted on the Tianchi fabric defect detection dataset demonstrate that Fab-ASLKS achieves a 5% improvement in mAP@50 over the baseline, showcasing its capability to deliver high precision and efficiency.
Post-hoc Spurious Correlation Neutralization with Single-Weight Fictitious Class Unlearning
Authors: Shahin Hakemi, Naveed Akhtar, Ghulam Mubashar Hassan, Ajmal Mian
Date: 2025-01-24 00:00:00

Neural network training tends to exploit the simplest features as shortcuts to greedily minimize training loss. However, some of these features might be spuriously correlated with the target labels, leading to incorrect predictions by the model. Several methods have been proposed to address this issue. Focusing on suppressing the spurious correlations with model training, they not only incur additional training cost, but also have limited practical utility as the model misbehavior due to spurious relations is usually discovered after its deployment. It is also often overlooked that spuriousness is a subjective notion. Hence, the precise questions that must be investigated are; to what degree a feature is spurious, and how we can proportionally distract the model's attention from it for reliable prediction. To this end, we propose a method that enables post-hoc neutralization of spurious feature impact, controllable to an arbitrary degree. We conceptualize spurious features as fictitious sub-classes within the original classes, which can be eliminated by a class removal scheme. We then propose a unique precise class removal technique that employs a single-weight modification, which entails negligible performance compromise for the remaining classes. We perform extensive experiments, demonstrating that by editing just a single weight in a post-hoc manner, our method achieves highly competitive, or better performance against the state-of-the-art methods.
Fully Guided Neural Schrödinger bridge for Brain MR image synthesis
Authors: Hanyeol Yang, Sunggyu Kim, Yongseon Yoo, Jong-Min Lee
Date: 2025-01-24 00:00:00
Tasks: Image Generation

Multi-modal brain MRI provides essential complementary information for clinical diagnosis. However, acquiring all modalities is often challenging due to time and cost constraints. To address this, various methods have been proposed to generate missing modalities from available ones. Traditional approaches can be broadly categorized into two main types: paired and unpaired methods. While paired methods offer superior performance, obtaining large-scale paired datasets is challenging in real-world scenarios. Conversely, unpaired methods facilitate large-scale data collection but struggle to preserve critical image features, such as tumors. In this paper, we propose Fully Guided Schr\"odinger Bridges (FGSB), a novel framework based on Neural Schr\"odinger Bridges, to overcome these limitations. FGSB achieves stable, high-quality generation of missing modalities using minimal paired data. Furthermore, when provided with ground truth or a segmentation network for specific regions, FGSB can generate missing modalities while preserving these critical areas with reduced data requirements. Our proposed model consists of two consecutive phases. 1) Generation Phase: Fuses a generated image, a paired reference image, and Gaussian noise, employing iterative refinement to mitigate issues such as mode collapse and improve generation quality 2) Training Phase: Learns the mapping from the generated image to the target modality. Experiments demonstrate that FGSB achieves comparable generation performance to methods trained on large datasets, while using data from only two subjects. Moreover, the utilization of lesion information with FGSB significantly enhances its ability to preserve crucial lesion features.
Effective Defect Detection Using Instance Segmentation for NDI
Authors: Ashiqur Rahman, Venkata Devesh Reddy Seethi, Austin Yunker, Zachary Kral, Rajkumar Kettimuthu, Hamed Alhoori
Date: 2025-01-24 00:00:00
Tasks: Defect Detection, Instance Segmentation, Semantic Segmentation

Ultrasonic testing is a common Non-Destructive Inspection (NDI) method used in aerospace manufacturing. However, the complexity and size of the ultrasonic scans make it challenging to identify defects through visual inspection or machine learning models. Using computer vision techniques to identify defects from ultrasonic scans is an evolving research area. In this study, we used instance segmentation to identify the presence of defects in the ultrasonic scan images of composite panels that are representative of real components manufactured in aerospace. We used two models based on Mask-RCNN (Detectron 2) and YOLO 11 respectively. Additionally, we implemented a simple statistical pre-processing technique that reduces the burden of requiring custom-tailored pre-processing techniques. Our study demonstrates the feasibility and effectiveness of using instance segmentation in the NDI pipeline by significantly reducing data pre-processing time, inspection time, and overall costs.
SelfPrompt: Confidence-Aware Semi-Supervised Tuning for Robust Vision-Language Model Adaptation
Authors: Shuvendu Roy, Ali Etemad
Date: 2025-01-24 00:00:00
Tasks: Language Modeling, Language Modelling, Pseudo Label, Weakly-supervised Learning

We present SelfPrompt, a novel prompt-tuning approach for vision-language models (VLMs) in a semi-supervised learning setup. Existing methods for tuning VLMs in semi-supervised setups struggle with the negative impact of the miscalibrated VLMs on pseudo-labelling, and the accumulation of noisy pseudo-labels. SelfPrompt addresses these challenges by introducing a cluster-guided pseudo-labelling method that improves pseudo-label accuracy, and a confidence-aware semi-supervised learning module that maximizes the utilization of unlabelled data by combining supervised learning and weakly-supervised learning. Additionally, we investigate our method in an active semi-supervised learning setup, where the labelled set is strategically selected to ensure the best utilization of a limited labelling budget. To this end, we propose a weakly-supervised sampling technique that selects a diverse and representative labelled set, which can be seamlessly integrated into existing methods to enhance their performance. We conduct extensive evaluations across 13 datasets, significantly surpassing state-of-the-art performances with average improvements of 6.23% in standard semi-supervised learning, 6.25% in active semi-supervised learning, and 4.9% in base-to-novel generalization, using a 2-shot setup. Furthermore, SelfPrompt shows excellent generalization in single-shot settings, achieving an average improvement of 11.78%.
StreamingRAG: Real-time Contextual Retrieval and Generation Framework
Authors: Murugan Sankaradas, Ravi K. Rajendran, Srimat T. Chakradhar
Date: 2025-01-23 00:00:00
Tasks: Knowledge Graphs, RAG, Retrieval, Retrieval-augmented Generation

Extracting real-time insights from multi-modal data streams from various domains such as healthcare, intelligent transportation, and satellite remote sensing remains a challenge. High computational demands and limited knowledge scope restrict the applicability of Multi-Modal Large Language Models (MM-LLMs) on these data streams. Traditional Retrieval-Augmented Generation (RAG) systems address knowledge limitations of these models, but suffer from slow preprocessing, making them unsuitable for real-time analysis. We propose StreamingRAG, a novel RAG framework designed for streaming data. StreamingRAG constructs evolving knowledge graphs capturing scene-object-entity relationships in real-time. The knowledge graph achieves temporal-aware scene representations using MM-LLMs and enables timely responses for specific events or user queries. StreamingRAG addresses limitations in existing methods, achieving significant improvements in real-time analysis (5-6x faster throughput), contextual accuracy (through a temporal knowledge graph), and reduced resource consumption (using lightweight models by 2-3x).
Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost
Authors: Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee, Xinya Wang, Ronald M. Summers, Zhiyong Lu
Date: 2025-01-23 00:00:00
Tasks: All, Prediction

Purpose: To automate contrast phase classification in CT using organ-specific features extracted from a widely used segmentation tool with a lightweight decision tree classifier. Materials and Methods: This retrospective study utilized three public CT datasets from separate institutions. The phase prediction model was trained on the WAW-TACE (median age: 66 [60,73]; 185 males) dataset, and externally validated on the VinDr-Multiphase (146 males; 63 females; 56 unk) and C4KC-KiTS (median age: 61 [50.68; 123 males) datasets. Contrast phase classification was performed using organ-specific features extracted by TotalSegmentator, followed by prediction using a gradient-boosted decision tree classifier. Results: On the VinDr-Multiphase dataset, the phase prediction model achieved the highest or comparable AUCs across all phases (>0.937), with superior F1-scores in the non-contrast (0.994), arterial (0.937), and delayed (0.718) phases. Statistical testing indicated significant performance differences only in the arterial and delayed phases (p<0.05). On the C4KC-KiTS dataset, the phase prediction model achieved the highest AUCs across all phases (>0.991), with superior F1-scores in arterial/venous (0.968) and delayed (0.935) phases. Statistical testing confirmed significant improvements over all baseline models in these two phases (p<0.05). Performance in the non-contrast class, however, was comparable across all models, with no statistically significant differences observed (p>0.05). Conclusion: The lightweight model demonstrated strong performance relative to all baseline models, and exhibited robust generalizability across datasets from different institutions.
Prior Knowledge Injection into Deep Learning Models Predicting Gene Expression from Whole Slide Images
Authors: Max Hallemeesch, Marija Pizurica, Paloma Rabaey, Olivier Gevaert, Thomas Demeester, Kathleen Marchal
Date: 2025-01-23 00:00:00
Tasks: Prognosis, whole slide images

Cancer diagnosis and prognosis primarily depend on clinical parameters such as age and tumor grade, and are increasingly complemented by molecular data, such as gene expression, from tumor sequencing. However, sequencing is costly and delays oncology workflows. Recent advances in Deep Learning allow to predict molecular information from morphological features within Whole Slide Images (WSIs), offering a cost-effective proxy of the molecular markers. While promising, current methods lack the robustness to fully replace direct sequencing. Here we aim to improve existing methods by introducing a model-agnostic framework that allows to inject prior knowledge on gene-gene interactions into Deep Learning architectures, thereby increasing accuracy and robustness. We design the framework to be generic and flexibly adaptable to a wide range of architectures. In a case study on breast cancer, our strategy leads to an average increase of 983 significant genes (out of 25,761) across all 18 experiments, with 14 generalizing to an increase on an independent dataset. Our findings reveal a high potential for injection of prior knowledge to increase gene expression prediction performance from WSIs across a wide range of architectures.
LLM-guided Instance-level Image Manipulation with Diffusion U-Net Cross-Attention Maps
Authors: Andrey Palaev, Adil Khan, Syed M. Ahsan Kazmi
Date: 2025-01-23 00:00:00
Tasks: Image Generation, Image Manipulation

The advancement of text-to-image synthesis has introduced powerful generative models capable of creating realistic images from textual prompts. However, precise control over image attributes remains challenging, especially at the instance level. While existing methods offer some control through fine-tuning or auxiliary information, they often face limitations in flexibility and accuracy. To address these challenges, we propose a pipeline leveraging Large Language Models (LLMs), open-vocabulary detectors, cross-attention maps and intermediate activations of diffusion U-Net for instance-level image manipulation. Our method detects objects mentioned in the prompt and present in the generated image, enabling precise manipulation without extensive training or input masks. By incorporating cross-attention maps, our approach ensures coherence in manipulated images while controlling object positions. Our method enables precise manipulations at the instance level without fine-tuning or auxiliary information such as masks or bounding boxes. Code is available at https://github.com/Palandr123/DiffusionU-NetLLM
Implicit Neural Surface Deformation with Explicit Velocity Fields
Authors: Lu Sang, Zehranaz Canfes, Dongliang Cao, Florian Bernard, Daniel Cremers
Date: 2025-01-23 00:00:00

In this work, we introduce the first unsupervised method that simultaneously predicts time-varying neural implicit surfaces and deformations between pairs of point clouds. We propose to model the point movement using an explicit velocity field and directly deform a time-varying implicit field using the modified level-set equation. This equation utilizes an iso-surface evolution with Eikonal constraints in a compact formulation, ensuring the integrity of the signed distance field. By applying a smooth, volume-preserving constraint to the velocity field, our method successfully recovers physically plausible intermediate shapes. Our method is able to handle both rigid and non-rigid deformations without any intermediate shape supervision. Our experimental results demonstrate that our method significantly outperforms existing works, delivering superior results in both quality and efficiency.
INDIGO+: A Unified INN-Guided Probabilistic Diffusion Algorithm for Blind and Non-Blind Image Restoration
Authors: Di You, Pier Luigi Dragotti
Date: 2025-01-23 00:00:00
Tasks: Image Restoration

Generative diffusion models are becoming one of the most popular prior in image restoration (IR) tasks due to their remarkable ability to generate realistic natural images. Despite achieving satisfactory results, IR methods based on diffusion models present several limitations. First of all, most non-blind approaches require an analytical expression of the degradation model to guide the sampling process. Secondly, most existing blind approaches rely on families of pre-defined degradation models for training their deep networks. The above issues limit the flexibility of these approaches and so their ability to handle real-world degradation tasks. In this paper, we propose a novel INN-guided probabilistic diffusion algorithm for non-blind and blind image restoration, namely INDIGO and BlindINDIGO, which combines the merits of the perfect reconstruction property of invertible neural networks (INN) with the strong generative capabilities of pre-trained diffusion models. Specifically, we train the forward process of the INN to simulate an arbitrary degradation process and use the inverse to obtain an intermediate image that we use to guide the reverse diffusion sampling process through a gradient step. We also introduce an initialization strategy, to further improve the performance and inference speed of our algorithm. Experiments demonstrate that our algorithm obtains competitive results compared with recently leading methods both quantitatively and visually on synthetic and real-world low-quality images.
Integrating Persian Lip Reading in Surena-V Humanoid Robot for Human-Robot Interaction
Authors: Ali Farshian Abbasi, Aghil Yousefi-Koma, Soheil Dehghani Firouzabadi, Parisa Rashidi, Alireza Naeini
Date: 2025-01-23 00:00:00
Tasks: Landmark Tracking, Lip Reading, speech-recognition, Speech Recognition

Lip reading is vital for robots in social settings, improving their ability to understand human communication. This skill allows them to communicate more easily in crowded environments, especially in caregiving and customer service roles. Generating a Persian Lip-reading dataset, this study integrates Persian lip-reading technology into the Surena-V humanoid robot to improve its speech recognition capabilities. Two complementary methods are explored, an indirect method using facial landmark tracking and a direct method leveraging convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The indirect method focuses on tracking key facial landmarks, especially around the lips, to infer movements, while the direct method processes raw video data for action and speech recognition. The best-performing model, LSTM, achieved 89\% accuracy and has been successfully implemented into the Surena-V robot for real-time human-robot interaction. The study highlights the effectiveness of these methods, particularly in environments where verbal communication is limited.
GS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian Splatting
Authors: Junzhe Jiang, Chun Gu, Yurui Chen, Li Zhang
Date: 2025-01-22 00:00:00
Tasks: Autonomous Driving, NeRF, Novel View Synthesis

LiDAR novel view synthesis (NVS) has emerged as a novel task within LiDAR simulation, offering valuable simulated point cloud data from novel viewpoints to aid in autonomous driving systems. However, existing LiDAR NVS methods typically rely on neural radiance fields (NeRF) as their 3D representation, which incurs significant computational costs in both training and rendering. Moreover, NeRF and its variants are designed for symmetrical scenes, making them ill-suited for driving scenarios. To address these challenges, we propose GS-LiDAR, a novel framework for generating realistic LiDAR point clouds with panoramic Gaussian splatting. Our approach employs 2D Gaussian primitives with periodic vibration properties, allowing for precise geometric reconstruction of both static and dynamic elements in driving scenarios. We further introduce a novel panoramic rendering technique with explicit ray-splat intersection, guided by panoramic LiDAR supervision. By incorporating intensity and ray-drop spherical harmonic (SH) coefficients into the Gaussian primitives, we enhance the realism of the rendered point clouds. Extensive experiments on KITTI-360 and nuScenes demonstrate the superiority of our method in terms of quantitative metrics, visual quality, as well as training and rendering efficiency.
Patch-Based and Non-Patch-Based inputs Comparison into Deep Neural Models: Application for the Segmentation of Retinal Diseases on Optical Coherence Tomography Volumes
Authors: Khaled Al-Saih, Fares Al-Shargie, Mohammed Isam Al-hiyali, Reham Alhejaili
Date: 2025-01-22 00:00:00

Worldwide, sight loss is commonly occurred by retinal diseases, with age-related macular degeneration (AMD) being a notable facet that affects elderly patients. Approaching 170 million persons wide-ranging have been spotted with AMD, a figure anticipated to rise to 288 million by 2040. For visualizing retinal layers, optical coherence tomography (OCT) dispenses the most compelling non-invasive method. Frequent patient visits have increased the demand for automated analysis of retinal diseases, and deep learning networks have shown promising results in both image and pixel-level 2D scan classification. However, when relying solely on 2D data, accuracy may be impaired, especially when localizing fluid volume diseases. The goal of automatic techniques is to outperform humans in manually recognizing illnesses in medical data. In order to further understand the benefit of deep learning models, we studied the effects of the input size. The dice similarity coefficient (DSC) metric showed a human performance score of 0.71 for segmenting various retinal diseases. Yet, the deep models surpassed human performance to establish a new era of advancement of segmenting the diseases on medical images. However, to further improve the performance of the models, overlapping patches enhanced the performance of the deep models compared to feeding the full image. The highest score for a patch-based model in the DSC metric was 0.88 in comparison to the score of 0.71 for the same model in non-patch-based for SRF fluid segmentation. The objective of this article is to show a fair comparison between deep learning models in relation to the input (Patch-Based vs. NonPatch-Based).
InsTex: Indoor Scenes Stylized Texture Synthesis
Authors: Yunfan Zhang, Zhiwei Xiong, Zhiqi Shen, Guosheng Lin, Hao Wang, Nicolas Vun
Date: 2025-01-22 00:00:00
Tasks: Texture Synthesis

Generating high-quality textures for 3D scenes is crucial for applications in interior design, gaming, and augmented/virtual reality (AR/VR). Although recent advancements in 3D generative models have enhanced content creation, significant challenges remain in achieving broad generalization and maintaining style consistency across multiple viewpoints. Current methods, such as 2D diffusion models adapted for 3D texturing, suffer from lengthy processing times and visual artifacts, while approaches driven by 3D data often fail to generalize effectively. To overcome these challenges, we introduce InsTex, a two-stage architecture designed to generate high-quality, style-consistent textures for 3D indoor scenes. InsTex utilizes depth-to-image diffusion priors in a coarse-to-fine pipeline, first generating multi-view images with a pre-trained 2D diffusion model and subsequently refining the textures for consistency. Our method supports both textual and visual prompts, achieving state-of-the-art results in visual quality and quantitative metrics, and demonstrates its effectiveness across various 3D texturing applications.
Triplet Synthesis For Enhancing Composed Image Retrieval via Counterfactual Image Generation
Authors: Kenta Uesugi, Naoki Saito, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
Date: 2025-01-22 00:00:00
Tasks: counterfactual, Image Generation, Image Retrieval, Retrieval, Triplet

Composed Image Retrieval (CIR) provides an effective way to manage and access large-scale visual data. Construction of the CIR model utilizes triplets that consist of a reference image, modification text describing desired changes, and a target image that reflects these changes. For effectively training CIR models, extensive manual annotation to construct high-quality training datasets, which can be time-consuming and labor-intensive, is required. To deal with this problem, this paper proposes a novel triplet synthesis method by leveraging counterfactual image generation. By controlling visual feature modifications via counterfactual image generation, our approach automatically generates diverse training triplets without any manual intervention. This approach facilitates the creation of larger and more expressive datasets, leading to the improvement of CIR model's performance.