Tootfinder

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Improving Image Captioning Descriptiveness by Ranking and LLM-based Fusion
Luigi Celona, Simone Bianco, Marco Donzella, Paolo Napoletano

MAPS: Masked Attribution-based Probing of Strategies- A computational framework to align human and model explanations
Sabine Muzellec, Yousif Kashef Alghetaa, Simon Kornblith, Kohitij Kar
https://arxiv.org/abs/2510.12141

MAPS: Masked Attribution-based Probing of Strategies- A computational framework to align human and model explanations
Human core object recognition depends on the selective use of visual information, but the strategies guiding these choices are difficult to measure directly. We present MAPS (Masked Attribution-based Probing of Strategies), a behaviorally validated computational tool that tests whether explanations derived from artificial neural networks (ANNs) can also explain human vision. MAPS converts attribution maps into explanation-masked images (EMIs) and compares image-by-image human accuracies on thes…

Smartphone-based iris recognition through high-quality visible-spectrum iris image capture.V2
Naveenkumar G Venkataswamy, Yu Liu, Soumyabrata Dey, Stephanie Schuckers, Masudul H Imtiaz
https://arxiv.org/abs/2510.06170

Smartphone-based iris recognition through high-quality visible-spectrum iris image capture.V2
Smartphone-based iris recognition in the visible spectrum (VIS) remains difficult due to illumination variability, pigmentation differences, and the absence of standardized capture controls. This work presents a compact end-to-end pipeline that enforces ISO/IEC 29794-6 quality compliance at acquisition and demonstrates that accurate VIS iris recognition is feasible on commodity devices. Using a custom Android application performing real-time framing, sharpness evaluation, and feedback, we intro…

Combining Intracortical Visual Prosthesis (ICVP) stimulation with auditory image-recognition information improves person-finding performance https://www.abstracts…

$\gamma$-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition
Mishal Fatima, Shashank Agnihotri, Marius Bock, Kanchana Vaishnavi Gandikota, Kristof Van Laerhoven, Michael Moeller, Margret Keuper
https://arxiv.org/abs/2509.22448

$γ$-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition
Most pattern recognition models are developed on pre-proce\-ssed data. In computer vision, for instance, RGB images processed through image signal processing (ISP) pipelines designed to cater to human perception are the most frequent input to image analysis networks. However, many modern vision tasks operate without a human in the loop, raising the question of whether such pre-processing is optimal for automated analysis. Similarly, human activity recognition (HAR) on body-worn sensor data comm…

Why AI isn't replacing radiologists: models underperform in hospital settings, AI use faces legal hurdles, and the job is much more than image recognition (The Works in Progress Newsletter)
https://www.worksinprogress.news/p/why-ai-isnt-replacing-radiologists…

AI isn't replacing radiologists
Radiology combines digital images, clear benchmarks, and repeatable tasks. But demand for human radiologists is ay an all-time high.

💔 Are Foundation Models Ready for Industrial Defect Recognition? A Reality Check on Real-World Data
#computing

Are Foundation Models Ready for Industrial Defect Recognition? A Reality Check on Real-World Data
Foundation Models (FMs) have shown impressive performance on various text and image processing tasks. They can generalize across domains and datasets in a zero-shot setting. This could make them suitable for automated quality inspection during series manufacturing, where various types of images are being evaluated for many different products. Replacing tedious labeling tasks with a simple text prompt to describe anomalies and utilizing the same models across many products would save significant…

A Collaborative Framework for Quantum Optimisation and Quantum Neural Networks: Credit Feature Selection and Image Classification
JiaNing Long, Xuechen Liang
https://arxiv.org/abs/2509.11110

A Collaborative Framework for Quantum Optimisation and Quantum Neural Networks: Credit Feature Selection and Image Classification
This paper investigates the efficacy of quantum computing in two distinct machine learning tasks: feature selection for credit risk assessment and image classification for handwritten digit recognition. For the first task, we address the feature selection challenge of the German Credit Dataset by formulating it as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is solved using quantum annealing to identify the optimal feature subset. Experimental results show that the result…

Face2Feel: Emotion-Aware Adaptive User Interface
Ismail Alihan Hadimlioglu, Siddharth Linga
https://arxiv.org/abs/2510.00489 https://arxiv.org/pdf/2510.004…

Face2Feel: Emotion-Aware Adaptive User Interface
This paper presents Face2Feel, a novel user interface (UI) model that dynamically adapts to user emotions and preferences captured through computer vision. This adaptive UI framework addresses the limitations of traditional static interfaces by integrating digital image processing, face recognition, and emotion detection techniques. Face2Feel analyzes user expressions utilizing a webcam or pre-installed camera as the primary data source to personalize the UI in real-time. Although dynamically c…

FROQ: Observing Face Recognition Models for Efficient Quality Assessment
\v{Z}iga Babnik, Deepak Kumar Jain, Peter Peer, Vitomir \v{S}truc
https://arxiv.org/abs/2509.17689 https…

FROQ: Observing Face Recognition Models for Efficient Quality Assessment
Face Recognition (FR) plays a crucial role in many critical (high-stakes) applications, where errors in the recognition process can lead to serious consequences. Face Image Quality Assessment (FIQA) techniques enhance FR systems by providing quality estimates of face samples, enabling the systems to discard samples that are unsuitable for reliable recognition or lead to low-confidence recognition decisions. Most state-of-the-art FIQA techniques rely on extensive supervised training to achieve a…

Emotion-Aware Speech Generation with Character-Specific Voices for Comics
Zhiwen Qian, Jinhua Liang, Huan Zhang
https://arxiv.org/abs/2509.15253 https://ar…

Emotion-Aware Speech Generation with Character-Specific Voices for Comics
This paper presents an end-to-end pipeline for generating character-specific, emotion-aware speech from comics. The proposed system takes full comic volumes as input and produces speech aligned with each character's dialogue and emotional state. An image processing module performs character detection, text recognition, and emotion intensity recognition. A large language model performs dialogue attribution and emotion analysis by integrating visual information with the evolving plot context. Spe…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/7]:
- STIV: Scalable Text and Image Conditioned Video Generation
Lin, Liu, Chen, Lu, Hu, Fu, Allardice, Lai, Song, Zhang, Chen, Fei, Li, Sun, Chang, Yang

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/3]:
- Filter-Guided Diffusion for Controllable Image Generation
Zeqi Gu, Ethan Yang, Abe Davis

A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss
MohammadAli Hamidi, Hadi Amirpour, Luigi Atzori, Christian Timmerer
https://arxiv.org/abs/2509.10114

A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss
Face image quality assessment (FIQA) plays a critical role in face recognition and verification systems, especially in uncontrolled, real-world environments. Although several methods have been proposed, general-purpose no-reference image quality assessment techniques often fail to capture face-specific degradations. Meanwhile, state-of-the-art FIQA models tend to be computationally intensive, limiting their practical applicability. We propose a lightweight and efficient method for FIQA, designe…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/7]:
- M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation
Xiaoqi Zhao, Hongpeng Jia, Youwei Pang, Long Lv, Feng Tian, Lihe Zhang, Weibing Sun, Huchuan Lu

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- ReLumix: Extending Image Relighting to Video via Video Diffusion Models
Lezhong Wang, Shutong Jin, Ruiqi Cui, Anders Bjorholm Dahl, Jeppe Revall Frisvad, Siavash Bigdeli

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- CNS-Bench: Benchmarking Image Classifier Robustness Under Continuous Nuisance Shifts
Olaf D\"unkel, Artur Jesslen, Jiahao Xie, Christian Theobalt, Christian Rupprecht, Adam Kortylewski

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- On the Role of Individual Differences in Current Approaches to Computational Image Aesthetics
Li-Wei Chen, Ombretta Strafforello, Anne-Sofie Maerten, Tinne Tuytelaars, Johan Wagemans

Dual-View Alignment Learning with Hierarchical-Prompt for Class-Imbalance Multi-Label Classification
Sheng Huang, Jiexuan Yan, Beiyan Liu, Bo Liu, Richang Hong
https://arxiv.org/abs/2509.17747

Dual-View Alignment Learning with Hierarchical-Prompt for Class-Imbalance Multi-Label Classification
Real-world datasets often exhibit class imbalance across multiple categories, manifesting as long-tailed distributions and few-shot scenarios. This is especially challenging in Class-Imbalanced Multi-Label Image Classification (CI-MLIC) tasks, where data imbalance and multi-object recognition present significant obstacles. To address these challenges, we propose a novel method termed Dual-View Alignment Learning with Hierarchical Prompt (HP-DVAL), which leverages multi-modal knowledge from visi…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- AMF-MedIT: An Efficient Align-Modulation-Fusion Framework for Medical Image-Tabular Data
Congjing Yu, Jing Ye, Yang Liu, Xiaodong Zhang, Zhiyong Zhang

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/3]:
- Geometry and Perception Guided Gaussians for Multiview-consistent 3D Generation from a Single Image
Pufan Li, Bi'an Du, Wei Hu

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- J-RAS: Enhancing Medical Image Segmentation via Retrieval-Augmented Joint Training
Salma J. Ahmed, Emad A. Mohammed, Azam Asilian Bidgoli