Tootfinder

RadarGaussianDet3D: An Efficient and Effective Gaussian-based 3D Detector with 4D Automotive Radars
Weiyi Xiong, Bing Zhu, Tao Huang, Zewei Zheng
https://arxiv.org/abs/2509.16119

RadarGaussianDet3D: An Efficient and Effective Gaussian-based 3D Detector with 4D Automotive Radars
4D automotive radars have gained increasing attention for autonomous driving due to their low cost, robustness, and inherent velocity measurement capability. However, existing 4D radar-based 3D detectors rely heavily on pillar encoders for BEV feature extraction, where each point contributes to only a single BEV grid, resulting in sparse feature maps and degraded representation quality. In addition, they also optimize bounding box attributes independently, leading to sub-optimal detection accur…

Two-stream network-driven vision-based tactile sensor for object feature extraction and fusion perception
Muxing Huang, Zibin Chen, Weiliang Xu, Zilan Li, Yuanzhi Zhou, Guoyuan Zhou, Wenjing Chen, Xinming Li
https://arxiv.org/abs/2510.12528

Two-stream network-driven vision-based tactile sensor for object feature extraction and fusion perception
Tactile perception is crucial for embodied intelligent robots to recognize objects. Vision-based tactile sensors extract object physical attributes multidimensionally using high spatial resolution; however, this process generates abundant redundant information. Furthermore, single-dimensional extraction, lacking effective fusion, fails to fully characterize object attributes. These challenges hinder the improvement of recognition accuracy. To address this issue, this study introduces a two-stre…

A Multi-Scale Feature Extraction and Fusion UNet for Pathloss Prediction in UAV-Assisted mmWave Radio Networks
Sajjad Hussain
https://arxiv.org/abs/2509.09606 https://

A Multi-Scale Feature Extraction and Fusion UNet for Pathloss Prediction in UAV-Assisted mmWave Radio Networks
Accurate pathloss prediction is essential for the design and optimization of UAV-assisted millimeter-wave (mmWave) networks. While deep learning approaches have shown strong potential, their generalization across diverse environments, robustness to noisy inputs, and sensitivity to UAV altitude remain underexplored. To address these challenges, we propose a UNet-based deep learning architecture that combines multi-scale feature extraction, convolution-based feature fusion, and an atrous spatial …

Feature Space Analysis by Guided Diffusion Model
Kimiaki Shirahama, Miki Yanobu, Kaduki Yamashita, Miho Ohsaki
https://arxiv.org/abs/2509.07936 https://arx…

Feature Space Analysis by Guided Diffusion Model
One of the key issues in Deep Neural Networks (DNNs) is the black-box nature of their internal feature extraction process. Targeting vision-related domains, this paper focuses on analysing the feature space of a DNN by proposing a decoder that can generate images whose features are guaranteed to closely match a user-specified feature. Owing to this guarantee that is missed in past studies, our decoder allows us to evidence which of various attributes in an image are encoded into a feature by th…

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Sai Puneeth Reddy Gottam, Haoming Zhang, Eivydas Keras
https://arxiv.org/abs/2509.08333

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Visual-based localization has made significant progress, yet its performance often drops in large-scale, outdoor, and long-term settings due to factors like lighting changes, dynamic scenes, and low-texture areas. These challenges degrade feature extraction and tracking, which are critical for accurate motion estimation. While learning-based methods such as SuperPoint and SuperGlue show improved feature coverage and robustness, they still face generalization issues with out-of-distribution data…

EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
Kwanhyung Lee, Sungsoo Hong, Joonhyung Park, Jeonghyeop Lim, Juhwan Choi, Donghwee Yoon, Eunho Yang
https://arxiv.org/abs/2510.00549

EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
Machine learning models for clinical prediction rely on structured data extracted from Electronic Medical Records (EMRs), yet this process remains dominated by hardcoded, database-specific pipelines for cohort definition, feature selection, and code mapping. These manual efforts limit scalability, reproducibility, and cross-institutional generalization. To address this, we introduce EMR-AGENT (Automated Generalized Extraction and Navigation Tool), an agent-based framework that replaces manual r…

Normalisation of SWIFT Message Counterparties with Feature Extraction and Clustering
Thanasis Schoinas, Benjamin Guinard, Diba Esbati, Richard Chalk
https://arxiv.org/abs/2508.21081

Normalisation of SWIFT Message Counterparties with Feature Extraction and Clustering
Short text clustering is a known use case in the text analytics community. When the structure and content falls in the natural language domain e.g. Twitter posts or instant messages, then natural language techniques can be used, provided texts are of sufficient length to allow for use of (pre)trained models to extract meaningful information, such as part-of-speech or topic annotations. However, natural language models are not suitable for clustering transaction counterparties, as they are found…

PoolingVQ: A VQVAE Variant for Reducing Audio Redundancy and Boosting Multi-Modal Fusion in Music Emotion Analysis
Dinghao Zou, Yicheng Gong, Xiaokang Li, Xin Cao, Sunbowen Lee
https://arxiv.org/abs/2509.11976

PoolingVQ: A VQVAE Variant for Reducing Audio Redundancy and Boosting Multi-Modal Fusion in Music Emotion Analysis
Multimodal music emotion analysis leverages audio and MIDI modalities to enhance performance. While mainstream approaches focus on complex feature extraction networks, we posit that shortening the length of audio sequence features to mitigate redundancy, especially in contrast to MIDI's compact representation, may effectively boost task performance. To achieve this, we developed PoolingVQ by combining Vector Quantized Variational Autoencoder (VQVAE) with spatial pooling, which directly compress…

Software Defect Prediction using Autoencoder Transformer Model
Seshu Barma, Mohanakrishnan Hariharan, Satish Arvapalli
https://arxiv.org/abs/2510.10840 https://

Software Defect Prediction using Autoencoder Transformer Model
An AI-ML-powered quality engineering approach uses AI-ML to enhance software quality assessments by predicting defects. Existing ML models struggle with noisy data types, imbalances, pattern recognition, feature extraction, and generalization. To address these challenges, we develop a new model, Adaptive Differential Evolution (ADE) based Quantum Variational Autoencoder-Transformer (QVAET) Model (ADE-QVAET). ADE combines with QVAET to obtain high-dimensional latent features and maintain sequent…

Quenched Quantum Feature Maps
Anton Simen, Carlos Flores-Garrigos, Murilo Henrique De Oliveira, Gabriel Dario Alvarado Barrios, Juan F. R. Hern\'andez, Qi Zhang, Alejandro Gomez Cadavid, Yolanda Vives-Gilabert, Jos\'e D. Mart\'in-Guerrero, Enrique Solano, Narendra N. Hegade, Archismita Dalal
https://arxiv.org/abs/2508.20975

Quenched Quantum Feature Maps
We propose a quantum feature mapping technique that leverages the quench dynamics of a quantum spin glass to extract complex data patterns at the quantum-advantage level for academic and industrial applications. We demonstrate that encoding a dataset information into disordered quantum many-body spin-glass problems, followed by a nonadiabatic evolution and feature extraction via measurements of expectation values, significantly enhances machine learning (ML) models. By analyzing the performance…

Evaluating the Representation of Vowels in Wav2Vec Feature Extractor: A Layer-Wise Analysis Using MFCCs
Domenico De Cristofaro, Vincenzo Norman Vitale, Alessandro Vietti
https://arxiv.org/abs/2508.17914

Evaluating the Representation of Vowels in Wav2Vec Feature Extractor: A Layer-Wise Analysis Using MFCCs
Automatic Speech Recognition has advanced with self-supervised learning, enabling feature extraction directly from raw audio. In Wav2Vec, a CNN first transforms audio into feature vectors before the transformer processes them. This study examines CNN-extracted information for monophthong vowels using the TIMIT corpus. We compare MFCCs, MFCCs with formants, and CNN activations by training SVM classifiers for front-back vowel identification, assessing their classification accuracy to evaluate pho…

Hierarchical Spatial Algorithms for High-Resolution Image Quantization and Feature Extraction
Noor Islam S. Mohammad
https://arxiv.org/abs/2510.08449 https://

Hierarchical Spatial Algorithms for High-Resolution Image Quantization and Feature Extraction
This study introduces a modular framework for spatial image processing, integrating grayscale quantization, color and brightness enhancement, image sharpening, bidirectional transformation pipelines, and geometric feature extraction. A stepwise intensity transformation quantizes grayscale images into eight discrete levels, producing a posterization effect that simplifies representation while preserving structural detail. Color enhancement is achieved via histogram equalization in both RGB and Y…

Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
Partha Konar, Vishal S. Ngairangbam, Michael Spannowsky, Deepanshu Srivastava
https://arxiv.org/abs/2509.22059

Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
Deep learning has achieved remarkable success in jet classification tasks, yet a key challenge remains: understanding what these models learn and how their features relate to known QCD observables. Improving interpretability is essential for building robust and trustworthy machine learning tools in collider physics. To address this challenge, we investigate graph neural networks for quark-gluon discrimination, systematically incorporating physics-motivated inductive biases. In particular, we de…

Spectrotemporal Feature Extraction in EHG Signals and Tocograms for Enhanced Preterm Birth Prediction
Senith Jayakody, Kalana Jayasooriya, Sashini Liyanage, Roshan Godaliyadda, Parakrama Ekanayake, Chathura Rathnayake
https://arxiv.org/abs/2509.07432

Spectrotemporal Feature Extraction in EHG Signals and Tocograms for Enhanced Preterm Birth Prediction
Preterm birth (PTB), defined as delivery before 37 weeks of gestation, is a leading cause of neonatal mortality and long term health complications. Early detection is essential for enabling timely medical interventions. Electrohysterography (EHG) and tocography (TOCO) are promising non invasive tools for PTB prediction, but prior studies often suffer from class imbalance, improper oversampling, and reliance on features with limited physiological relevance. This work presents a machine learning …

Automated Research Article Classification and Recommendation Using NLP and ML
Shadikur Rahman, Hasibul Karim Shanto, Umme Ayman Koana, Syed Muhammad Danish
https://arxiv.org/abs/2510.05495

Automated Research Article Classification and Recommendation Using NLP and ML
In the digital era, the exponential growth of scientific publications has made it increasingly difficult for researchers to efficiently identify and access relevant work. This paper presents an automated framework for research article classification and recommendation that leverages Natural Language Processing (NLP) techniques and machine learning. Using a large-scale arXiv.org dataset spanning more than three decades, we evaluate multiple feature extraction approaches (TF--IDF, Count Vectorize…

Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimation
Wenyao Zhang, Hongsi Liu, Bohan Li, Jiawei He, Zekun Qi, Yunnan Wang, Shengyang Zhao, Xinqiang Yu, Wenjun Zeng, Xin Jin
https://arxiv.org/abs/2510.09320

Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimation
Current self-supervised monocular depth estimation (MDE) approaches encounter performance limitations due to insufficient semantic-spatial knowledge extraction. To address this challenge, we propose Hybrid-depth, a novel framework that systematically integrates foundation models (e.g., CLIP and DINO) to extract visual priors and acquire sufficient contextual information for MDE. Our approach introduces a coarse-to-fine progressive learning framework: 1) Firstly, we aggregate multi-grained featu…

Universal syndrome-based recovery for noise-adapted quantum error correction
Debjyoti Biswas, Prabha Mandayam
https://arxiv.org/abs/2510.08719 https://arxi…

Universal syndrome-based recovery for noise-adapted quantum error correction
Quantum error correction (QEC) is an essential tool for quantum computing that enables reliable information processing in the presence of noise. Syndrome measurements play a central role in QEC, making it possible to unambiguously identify the location and type of errors. While syndrome extraction is natural for conventional QEC protocols, where the errors satisfy certain algebraic constraints \emph{perfectly}, this feature is largely missing in the framework of approximate or noise-adapted QEC…

Gaussian Ensemble Topology (GET): A New Explicit and Inherently Smooth Framework for Manufacture-Ready Topology Optimization
Xinyu Ma, Chengxin Wang, Meng Wang, Xu Guo, Liu Yang, Huajian Gao
https://arxiv.org/abs/2510.05572

Gaussian Ensemble Topology (GET): A New Explicit and Inherently Smooth Framework for Manufacture-Ready Topology Optimization
We introduce the Gaussian Ensemble Topology (GET) method, a new explicit and manufacture-ready framework for topology optimization in which design geometries are represented as superpositions of anisotropic Gaussian functions. By combining explicit Gaussian descriptions with a level-set-like Heaviside projection, GET inherently generates smooth, curvature-continuous designs without requiring post-processing steps such as mesh or corner smoothing and feature extraction. The method is validated o…

IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
Ahsan Farabi, Muhaiminul Rashid Shad, Israt Khandaker
https://arxiv.org/abs/2510.00572

IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
Intrusion Detection Systems (IDS) face persistent challenges due to evolving cyberattacks, high-dimensional traffic data, and severe class imbalance in benchmark datasets such as NSL-KDD. To address these issues, we propose IntrusionX, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling. The architecture is further optimized using the Squirrel Search Algorithm (SSA), e…

Feature Representation and Clustering of Airport Congestion with Hurst Exponent and High Order Statistics
Wei Sun, Zi-Feng Yi, Zhi-Qiang Feng, Ji Ma, Ruo-shi Yang
https://arxiv.org/abs/2509.08952

Feature Representation and Clustering of Airport Congestion with Hurst Exponent and High Order Statistics
Air traffic controllers benefit from referencing historical dates with similar complex air traffic conditions to identify potential management measures and their effects, which is critical for understanding air transportation system laws and optimizing decisions. This study conducted data mining using flight timetables. It first explored airport congestion mechanisms and quantified congestion as time series, then proposed a higher-order cumulants based time series feature extraction method. Thi…

Microscopic and collective signatures of feature learning in neural networks
Andrea Corti, Rosalba Pacelli, Pietro Rotondo, Marco Gherardi
https://arxiv.org/abs/2508.20989 https…

Microscopic and collective signatures of feature learning in neural networks
Feature extraction - the ability to identify relevant properties of data - is a key factor underlying the success of deep learning. Yet, it has proved difficult to elucidate its nature within existing predictive theories, to the extent that there is no consensus on the very definition of feature learning. A promising hint in this direction comes from previous phenomenological observations of quasi-universal aspects in the training dynamics of neural networks, displayed by simple properties of f…

Towards Robust and Realible Multimodal Fake News Detection with Incomplete Modality
Hengyang Zhou, Yiwei Wei, Jian Yang, Zhenyu Zhang
https://arxiv.org/abs/2510.05839 https://…

Towards Robust and Realible Multimodal Fake News Detection with Incomplete Modality
Multimodal fake news detection (MFND) has become an urgent task with the emergence of huge multimodal fake content on social media platforms. Previous studies mainly focus on complex feature extraction and fusion to learn discriminative information from multimodal content. However, in real-world applications, multimedia news may naturally lose some information during dissemination, resulting in modality incompleteness, which is detrimental to the generalization and robustness of existing models…

Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera
Mikihiro Ikura, Arren Glover, Masayoshi Mizuno, Chiara Bartolozzi
https://arxiv.org/abs/2510.06829

Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera
Line segment extraction is effective for capturing geometric features of human-made environments. Event-based cameras, which asynchronously respond to contrast changes along edges, enable efficient extraction by reducing redundant data. However, recent methods often rely on additional frame cameras or struggle with high event rates. This research addresses real-time line segment detection and tracking using only a modern, high-resolution (i.e., high event rate) event-based camera. Our lattice-a…

A Novel Preprocessing Unit for Effective Deep Learning based Classification and Grading of Diabetic Retinopathy
Pranoti Nage, Sanjay Shitole
https://arxiv.org/abs/2509.24497 htt…

A Novel Preprocessing Unit for Effective Deep Learning based Classification and Grading of Diabetic Retinopathy
Early detection of diabetic retinopathy (DR) is crucial as it allows for timely intervention, preventing vision loss and enabling effective management of diabetic complications. This research performs detection of DR and DME at an early stage through the proposed framework which includes three stages: preprocessing, segmentation, feature extraction, and classification. In the preprocessing stage, noise filtering is performed by fuzzy filtering, artefact removal is performed by non-linear diffus…

Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency
Riccardo Mazzieri, Eleonora Cicciarella, Jacopo Pegoraro, Federico Corradi, Michele Rossi
https://arxiv.org/abs/2509.23303

Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency
Radar-based Human Activity Recognition (HAR) offers privacy and robustness over camera-based methods, yet remains computationally demanding for edge deployment. We present the first use of Spiking Neural Networks (SNNs) for radar-based HAR on aircraft marshalling signal classification. Our novel hybrid architecture combines convolutional modules for spatial feature extraction with Leaky Integrate-and-Fire (LIF) neurons for temporal processing, inherently capturing gesture dynamics. The model re…

Hy-Facial: Hybrid Feature Extraction by Dimensionality Reduction Methods for Enhanced Facial Expression Classification
Xinjin Li, Yu Ma, Kaisen Ye, Jinghan Cao, Minghao Zhou, Yeyang Zhou
https://arxiv.org/abs/2509.26614

Hy-Facial: Hybrid Feature Extraction by Dimensionality Reduction Methods for Enhanced Facial Expression Classification
Facial expression classification remains a challenging task due to the high dimensionality and inherent complexity of facial image data. This paper presents Hy-Facial, a hybrid feature extraction framework that integrates both deep learning and traditional image processing techniques, complemented by a systematic investigation of dimensionality reduction strategies. The proposed method fuses deep features extracted from the Visual Geometry Group 19-layer network (VGG19) with handcrafted local d…

HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
Rajat Bhattacharjya, Woohyeok Park, Arnab Sarkar, Hyunwoo Oh, Mohsen Imani, Nikil Dutt
https://arxiv.org/abs/2510.10718

HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
Direction of Arrival (DoA) estimation techniques face a critical trade-off, as classical methods often lack accuracy in challenging, low signal-to-noise ratio (SNR) conditions, while modern deep learning approaches are too energy-intensive and opaque for resource-constrained, safety-critical systems. We introduce HYPERDOA, a novel estimator leveraging Hyperdimensional Computing (HDC). The framework introduces two distinct feature extraction strategies -- Mean Spatial-Lag Autocorrelation and Spa…

Soft Disentanglement in Frequency Bands for Neural Audio Codecs
Benoit Ginies, Xiaoyu Bie, Olivier Fercoq, Ga\"el Richard
https://arxiv.org/abs/2510.03735 https://

Soft Disentanglement in Frequency Bands for Neural Audio Codecs
In neural-based audio feature extraction, ensuring that representations capture disentangled information is crucial for model interpretability. However, existing disentanglement methods often rely on assumptions that are highly dependent on data characteristics or specific tasks. In this work, we introduce a generalizable approach for learning disentangled features within a neural architecture. Our method applies spectral decomposition to time-domain signals, followed by a multi-branch audio co…

Towards fairer public transit: Real-time tensor-based multimodal fare evasion and fraud detection
Peter Wauyo, Dalia Bwiza, Alain Murara, Edwin Mugume, Eric Umuhoza
https://arxiv.org/abs/2510.02165

Towards fairer public transit: Real-time tensor-based multimodal fare evasion and fraud detection
This research introduces a multimodal system designed to detect fraud and fare evasion in public transportation by analyzing closed circuit television (CCTV) and audio data. The proposed solution uses the Vision Transformer for Video (ViViT) model for video feature extraction and the Audio Spectrogram Transformer (AST) for audio analysis. The system implements a Tensor Fusion Network (TFN) architecture that explicitly models unimodal and bimodal interactions through a 2-fold Cartesian product. …

Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges
Ryandhimas E. Zezario
https://arxiv.org/abs/2509.03017 https://

Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges
This paper provides an overview of recent progress in non-intrusive speech intelligibility prediction for hearing aids (HA). We summarize developments in robust acoustic feature extraction, hearing loss modeling, and the use of emerging architectures for long-sequence processing. Listener-specific adaptation strategies and domain generalization approaches that aim to improve robustness in unseen acoustic environments are also discussed. Remaining challenges, such as the need for large-scale, di…

Surformer v2: A Multimodal Classifier for Surface Understanding from Touch and Vision
Manish Kansana, Sindhuja Penchala, Shahram Rahimi, Noorbakhsh Amiri Golilarz
https://arxiv.org/abs/2509.04658

Surformer v2: A Multimodal Classifier for Surface Understanding from Touch and Vision
Multimodal surface material classification plays a critical role in advancing tactile perception for robotic manipulation and interaction. In this paper, we present Surformer v2, an enhanced multi-modal classification architecture designed to integrate visual and tactile sensory streams through a late(decision level) fusion mechanism. Building on our earlier Surformer v1 framework [1], which employed handcrafted feature extraction followed by mid-level fusion architecture with multi-head cross-…

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
Xin An, Ruijie Li, Qiao Ning, Hui Li, Qian Ma, Shikai Guo
https://arxiv.org/abs/2509.20240 https://

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
Non-coding RNAs (ncRNAs) play pivotal roles in gene expression regulation and the pathogenesis of various diseases. Accurate classification of ncRNAs is essential for functional annotation and disease diagnosis. To address existing limitations in feature extraction depth and multimodal fusion, we propose HGMamba-ncRNA, a HyperGraphMamba-based multichannel adaptive model, which integrates sequence, secondary structure, and optionally available expression features of ncRNAs to enhance classificat…

Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts
Dong Hyun Roh, Rajesh Kumar
https://arxiv.org/abs/2509.24807 https://

Active Authentication via Korean Keystrokes Under Varying LLM Assistance and Cognitive Contexts
Keystroke dynamics is a promising modality for active user authentication, but its effectiveness under varying LLM-assisted typing and cognitive conditions remains understudied. Using data from 50 users and cognitive labels from Bloom's Taxonomy, we evaluate keystroke-based authentication in Korean across three realistic typing scenarios: bona fide composition, LLM content paraphrasing, and transcription. Our pipeline incorporates continuity-aware segmentation, feature extraction, and classific…

Invited Paper: Feature-to-Classifier Co-Design for Mixed-Signal Smart Flexible Wearables for Healthcare at the Extreme Edge
Maha Shatta, Konstantinos Balaskas, Paula Carolina Lozano Duarte, Georgios Panagopoulos, Mehdi B. Tahoori, Georgios Zervakis
https://arxiv.org/abs/2508.19637

Invited Paper: Feature-to-Classifier Co-Design for Mixed-Signal Smart Flexible Wearables for Healthcare at the Extreme Edge
Flexible Electronics (FE) offer a promising alternative to rigid silicon-based hardware for wearable healthcare devices, enabling lightweight, conformable, and low-cost systems. However, their limited integration density and large feature sizes impose strict area and power constraints, making ML-based healthcare systems-integrating analog frontend, feature extraction and classifier-particularly challenging. Existing FE solutions often neglect potential system-wide solutions and focus on the cla…

Palmistry-Informed Feature Extraction and Analysis using Machine Learning
Shweta Patil
https://arxiv.org/abs/2509.02248 https://arxiv.org/pdf/2509.02248

Palmistry-Informed Feature Extraction and Analysis using Machine Learning
This paper explores the automated analysis of palmar features using machine learning techniques. We present a computer vision pipeline that extracts key characteristics from palm images, such as principal line structures, texture, and shape metrics. These features are used to train predictive models on a novel dataset curated from annotated palm images. Our approach moves beyond traditional subjective interpretation by providing a data-driven, quantitative framework for studying the correlation…

D\'esentrelacement Fr\'equentiel Doux pour les Codecs Audio Neuronaux
Beno\^it Gini\`es, Xiaoyu Bie, Olivier Fercoq, Ga\"el Richard
https://arxiv.org/abs/2510.03741

Désentrelacement Fréquentiel Doux pour les Codecs Audio Neuronaux
While neural-based models have led to significant advancements in audio feature extraction, the interpretability of the learned representations remains a critical challenge. To address this, disentanglement techniques have been integrated into discrete neural audio codecs to impose structure on the extracted tokens. However, these approaches often exhibit strong dependencies on specific datasets or task formulations. In this work, we propose a disentangled neural audio codec that leverages spec…

NV3D: Leveraging Spatial Shape Through Normal Vector-based 3D Object Detection
Krittin Chaowakarn, Paramin Sangwongngam, Nang Htet Htet Aung, Chalie Charoenlarpnopparut
https://arxiv.org/abs/2510.11632

NV3D: Leveraging Spatial Shape Through Normal Vector-based 3D Object Detection
Recent studies in 3D object detection for autonomous vehicles aim to enrich features through the utilization of multi-modal setups or the extraction of local patterns within LiDAR point clouds. However, multi-modal methods face significant challenges in feature alignment, and gaining features locally can be oversimplified for complex 3D object detection tasks. In this paper, we propose a novel model, NV3D, which utilizes local features acquired from voxel neighbors, as normal vectors computed p…

PRNet: Original Information Is All You Have
PeiHuang Zheng, Yunlong Zhao, Zheng Cui, Yang Li
https://arxiv.org/abs/2510.09531 https://arxiv.org/pdf/2510.09…

PRNet: Original Information Is All You Have
Small object detection in aerial images suffers from severe information degradation during feature extraction due to limited pixel representations, where shallow spatial details fail to align effectively with semantic information, leading to frequent misses and false positives. Existing FPN-based methods attempt to mitigate these losses through post-processing enhancements, but the reconstructed details often deviate from the original image information, impeding their fusion with semantic conte…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/9]:
- DeeP-Mod: Deep Dynamic Programming based Environment Modelling using Feature Extraction
Chris Child, Lam Ngo

Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking
Zhuohao Li, Wenqing Chen, Jianxing Yu, Zhichao Lu
https://arxiv.org/abs/2508.19558

Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking
Embedding models have demonstrated strong performance in tasks like clustering, retrieval, and feature extraction while offering computational advantages over generative models and cross-encoders. Benchmarks such as MTEB have shown that text embeddings from large language models (LLMs) capture rich semantic information, but their ability to reflect code-level functional semantics remains unclear. Existing studies largely focus on code clone detection, which emphasizes syntactic similarity and o…

AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement
M. Sajid, Deepanshu Gupta, Yash Modi, Sanskriti Jain, Harshith Jai Surya Ganji, A. Rahaman, Harshvardhan Choudhary, Nasir Saleem, Amir Hussain, M. Tanveer
https://arxiv.org/abs/2510.05295

AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement
In this paper, we propose AUREXA-SE (Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement), a progressive bimodal framework tailored for audio-visual speech enhancement (AVSE). AUREXA-SE jointly leverages raw audio waveforms and visual cues by employing a U-Net-based 1D convolutional encoder for audio and a Swin Transformer V2 for efficient and expressive visual feature extraction. Central to the architecture is a novel bidirect…

An enhanced statistical feature fusion approach using an improved distance evaluation algorithm and weighted K-nearest neighbor for bearing fault diagnosis
Amir Eshaghi Chaleshtori, Abdollah Aghaie
https://arxiv.org/abs/2509.21219

An enhanced statistical feature fusion approach using an improved distance evaluation algorithm and weighted K-nearest neighbor for bearing fault diagnosis
Bearings are among the most failure-prone components in rotating machinery, and their condition directly impacts overall performance. Therefore, accurately diagnosing bearing faults is essential for ensuring system stability. However, detecting such malfunctions in noisy environments, where data is collected from multiple sensors, necessitates the extraction and selection of informative features. This paper proposes an improved distance evaluation algorithm combined with a weighted K-nearest ne…

Enhancing Self-Supervised Speaker Verification Using Similarity-Connected Graphs and GCN
Zhaorui Sun, Yihao Chen, Jialong Wang, Minqiang Xu, Lei Fang, Sian Fang, Lin Liu
https://arxiv.org/abs/2509.04147

Enhancing Self-Supervised Speaker Verification Using Similarity-Connected Graphs and GCN
With the continuous development of speech recognition technology, speaker verification (SV) has become an important method for identity authentication. Traditional SV methods rely on handcrafted feature extraction, while deep learning has significantly improved system performance. However, the scarcity of labeled data still limits the widespread application of deep learning in SV. Self-supervised learning, by mining latent information in large unlabeled datasets, enhances model generalization a…

Pothole Detection and Recognition based on Transfer Learning
Mang Hu, Qianqian Xia
https://arxiv.org/abs/2509.06750 https://arxiv.org/pdf/2509.06750…

Pothole Detection and Recognition based on Transfer Learning
With the rapid development of computer vision and machine learning, automated methods for pothole detection and recognition based on image and video data have received significant attention. It is of great significance for social development to conduct an in-depth analysis of road images through feature extraction, thereby achieving automatic identification of the pothole condition in new images. Consequently, this is the main issue addressed in this study. Based on preprocessing techniques suc…

Efficient Discriminative Joint Encoders for Large Scale Vision-Language Reranking
Mitchell Keren Taraday, Shahaf Wagner, Chaim Baskin
https://arxiv.org/abs/2510.06820 https://…

Efficient Discriminative Joint Encoders for Large Scale Vision-Language Reranking
Multimodal retrieval still leans on embedding-based models like CLIP for fast vector search over pre-computed image embeddings. Yet, unlike text retrieval, where joint-encoder rerankers are standard, comparable vision--language rerankers are largely absent. We find that seminal joint encoders such as BLIP are severely bottlenecked by an expensive visual feature-extraction stage, preventing practical deployment at scale. Motivated by this bottleneck, we introduce EDJE, an Efficient Discriminativ…

Scale-interaction transformer: a hybrid cnn-transformer model for facial beauty prediction
Djamel Eddine Boukhari
https://arxiv.org/abs/2509.05078 https://…

Scale-interaction transformer: a hybrid cnn-transformer model for facial beauty prediction
Automated Facial Beauty Prediction (FBP) is a challenging computer vision task due to the complex interplay of local and global facial features that influence human perception. While Convolutional Neural Networks (CNNs) excel at feature extraction, they often process information at a fixed scale, potentially overlooking the critical inter-dependencies between features at different levels of granularity. To address this limitation, we introduce the Scale-Interaction Transformer (SIT), a novel hy…

Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution
Haosong Liu, Xiancheng Zhu, Huanqiang Zeng, Jianqing Zhu, Jiuwen Cao, Junhui Hou
https://arxiv.org/abs/2509.04824

Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution
Recently, Mamba-based methods, with its advantage in long-range information modeling and linear complexity, have shown great potential in optimizing both computational cost and performance of light field image super-resolution (LFSR). However, current multi-directional scanning strategies lead to inefficient and redundant feature extraction when applied to complex LF data. To overcome this challenge, we propose a Subspace Simple Scanning (Sub-SS) strategy, based on which we design the Subspace …

Neuroplastic Modular Framework: Cross-Domain Image Classification of Garbage and Industrial Surfaces
Debojyoti Ghosh, Soumya K Ghosh, Adrijit Goswami
https://arxiv.org/abs/2510.05071

Neuroplastic Modular Framework: Cross-Domain Image Classification of Garbage and Industrial Surfaces
Efficient and accurate classification of waste and industrial surface defects is essential for ensuring sustainable waste management and maintaining high standards in quality control. This paper introduces the Neuroplastic Modular Classifier, a novel hybrid architecture designed for robust and adaptive image classification in dynamic environments. The model combines a ResNet-50 backbone for localized feature extraction with a Vision Transformer (ViT) to capture global semantic context. Addition…

Adversarially Robust MIMO Physical Layer Authentication for Non-Stationary Channels
Ali Khandan Boroujeni, Ghazal Bagheri, Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, Stefan K\"opsell, Rafael F. Schaefer
https://arxiv.org/abs/2509.21171

Adversarially Robust MIMO Physical Layer Authentication for Non-Stationary Channels
We propose an adversarially robust physical layer authentication (AR-PLA) framework tailored for non-stationary multiple-input multiple-output (MIMO) wireless channels. The framework integrates sequential Bayesian decision-making, deep feature extraction via contrastive learning, and generative adversarial modeling to simulate adaptive spoofers. Unlike conventional methods that assume stationary channels or independent observations, our approach explicitly accounts for temporal and spatial corr…

Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation
Junying Meng, Gangxuan Zhou, Jun Liu, Weihong Guo
https://arxiv.org/abs/2509.04273

Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation
Despite signi cant progress in semi-supervised medical image segmentation, most existing segmentation networks overlook e ective methodological guidance for feature extraction and important prior information from datasets. In this paper, we develop a semi-supervised medical image segmentation framework that e ectively integrates spatial regularization methods and volume priors. Speci cally, our approach integrates a strong explicit volume prior at the image scale and Threshold Dynamics spatia…

Quality-Ensured In-Situ Process Monitoring with Deep Canonical Correlation Analysis
Xiaoyang Song (Judy), Wenbo Sun (Judy), Metin Kayitmazbatir (Judy), Jionghua (Judy), Jin
https://arxiv.org/abs/2509.19652

Quality-Ensured In-Situ Process Monitoring with Deep Canonical Correlation Analysis
This paper proposes a deep learning-based approach for in-situ process monitoring that captures nonlinear relationships between in-control high-dimensional process signature signals and offline product quality data. Specifically, we introduce a Deep Canonical Correlation Analysis (DCCA)-based framework that enables the joint feature extraction and correlation analysis of multi-modal data sources, such as optical emission spectra and CT scan images, which are collected in advanced manufacturing …

Millisecond-Response Tracking and Gazing System for UAVs: A Domestic Solution Based on "Phytium Cambricon"
Yuchen Zhu, Longxiang Yin, Kai Zhao
https://arxiv.org/abs/2509.04043

Millisecond-Response Tracking and Gazing System for UAVs: A Domestic Solution Based on "Phytium + Cambricon"
In the frontier research and application of current video surveillance technology, traditional camera systems exhibit significant limitations of response delay exceeding 200 ms in dynamic scenarios due to the insufficient deep feature extraction capability of automatic recognition algorithms and the efficiency bottleneck of computing architectures, failing to meet the real-time requirements in complex scenes. To address this issue, this study proposes a heterogeneous computing architecture base…

A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients
Jingyang Shan, Qishuai Yu, Jiacen Liu, Shaolin Zhang, Wen Shen, Yanxiao Zhao, Tianyi Wang, Xiaolin Qin, Yiheng Yin
https://arxiv.org/abs/2509.02256

A Multimodal Cross-View Model for Predicting Postoperative Neck Pain in Cervical Spondylosis Patients
Neck pain is the primary symptom of cervical spondylosis, yet its underlying mechanisms remain unclear, leading to uncertain treatment outcomes. To address the challenges of multimodal feature fusion caused by imaging differences and spatial mismatches, this paper proposes an Adaptive Bidirectional Pyramid Difference Convolution (ABPDC) module that facilitates multimodal integration by exploiting the advantages of difference convolution in texture extraction and grayscale invariance, and a Feat…

U-MAN: U-Net with Multi-scale Adaptive KAN Network for Medical Image Segmentation
Bohan Huang, Qianyun Bao, Haoyuan Ma
https://arxiv.org/abs/2509.22444 https://

U-MAN: U-Net with Multi-scale Adaptive KAN Network for Medical Image Segmentation
Medical image segmentation faces significant challenges in preserving fine-grained details and precise boundaries due to complex anatomical structures and pathological regions. These challenges primarily stem from two key limitations of conventional U-Net architectures: (1) their simple skip connections ignore the encoder-decoder semantic gap between various features, and (2) they lack the capability for multi-scale feature extraction in deep layers. To address these challenges, we propose the …

Robust RGB-T Tracking via Learnable Visual Fourier Prompt Fine-tuning and Modality Fusion Prompt Generation
Hongtao Yang, Bineng Zhong, Qihua Liang, Zhiruo Zhu, Yaozong Zheng, Ning Li
https://arxiv.org/abs/2509.19733

Robust RGB-T Tracking via Learnable Visual Fourier Prompt Fine-tuning and Modality Fusion Prompt Generation
Recently, visual prompt tuning is introduced to RGB-Thermal (RGB-T) tracking as a parameter-efficient finetuning (PEFT) method. However, these PEFT-based RGB-T tracking methods typically rely solely on spatial domain information as prompts for feature extraction. As a result, they often fail to achieve optimal performance by overlooking the crucial role of frequency-domain information in prompt learning. To address this issue, we propose an efficient Visual Fourier Prompt Tracking (named VFPTra…