Tootfinder

Benchmarking foundation models for hyperspectral image classification: Application to cereal crop type mapping
Walid Elbarz, Mohamed Bourriz, Hicham Hajji, Hamd Ait Abdelali, Fran\c{c}ois Bourzeix
https://arxiv.org/abs/2510.11576

Benchmarking foundation models for hyperspectral image classification: Application to cereal crop type mapping
Foundation models are transforming Earth observation, but their potential for hyperspectral crop mapping remains underexplored. This study benchmarks three foundation models for cereal crop mapping using hyperspectral imagery: HyperSigma, DOFA, and Vision Transformers pre-trained on the SpectralEarth dataset (a large multitemporal hyperspectral archive). Models were fine-tuned on manually labeled data from a training region and evaluated on an independent test region. Performance was measured w…

Hybrid Vision Transformer and Quantum Convolutional Neural Network for Image Classification
Mingzhu Wang, Yun Shang
https://arxiv.org/abs/2510.12291 https://

Hybrid Vision Transformer and Quantum Convolutional Neural Network for Image Classification
Quantum machine learning (QML) holds promise for computational advantage, yet progress on real-world tasks is hindered by classical preprocessing and noisy devices. We introduce ViT-QCNN-FT, a hybrid framework that integrates a fine-tuned Vision Transformer with a quantum convolutional neural network (QCNN) to compress high-dimensional images into features suited for noisy intermediate-scale quantum (NISQ) devices. By systematically probing entanglement, we show that ansatzes with uniformly dis…

Prototypical Contrastive Learning For Improved Few-Shot Audio Classification
Christos Sgouropoulos, Christos Nikou, Stefanos Vlachos, Vasileios Theiou, Christos Foukanelis, Theodoros Giannakopoulos
https://arxiv.org/abs/2509.10074

Prototypical Contrastive Learning For Improved Few-Shot Audio Classification
Few-shot learning has emerged as a powerful paradigm for training models with limited labeled data, addressing challenges in scenarios where large-scale annotation is impractical. While extensive research has been conducted in the image domain, few-shot learning in audio classification remains relatively underexplored. In this work, we investigate the effect of integrating supervised contrastive loss into prototypical few shot training for audio classification. In detail, we demonstrate that an…

Zero-shot image privacy classification with Vision-Language Models
Alina Elena Baia, Alessio Xompero, Andrea Cavallaro
https://arxiv.org/abs/2510.09253 https://

Zero-shot image privacy classification with Vision-Language Models
While specialized learning-based models have historically dominated image privacy prediction, the current literature increasingly favours adopting large Vision-Language Models (VLMs) designed for generic tasks. This trend risks overlooking the performance ceiling set by purpose-built models due to a lack of systematic evaluation. To address this problem, we establish a zero-shot benchmark for image privacy classification, enabling a fair comparison. We evaluate the top-3 open-source VLMs, accor…

Measuring dissimilarity between convex cones by means of max-min angles
Welington de Oliveira, Valentina Sessa, David Sossa
https://arxiv.org/abs/2511.10483 https://arxiv.org/pdf/2511.10483 https://arxiv.org/html/2511.10483
arXiv:2511.10483v1 Announce Type: new
Abstract: This work introduces a novel dissimilarity measure between two convex cones, based on the max-min angle between them. We demonstrate that this measure is closely related to the Pompeiu-Hausdorff distance, a well-established metric for comparing compact sets. Furthermore, we examine cone configurations where the measure admits simplified or analytic forms. For the specific case of polyhedral cones, a nonconvex cutting-plane method is deployed to compute, at least approximately, the measure between them. Our approach builds on a tailored version of Kelley's cutting-plane algorithm, which involves solving a challenging master program per iteration. When this master program is solved locally, our method yields an angle that satisfies certain necessary optimality conditions of the underlying nonconvex optimization problem yielding the dissimilarity measure between the cones. As an application of the proposed mathematical and algorithmic framework, we address the image-set classification task under limited data conditions, a task that falls within the scope of the \emph{Few-Shot Learning} paradigm. In this context, image sets belonging to the same class are modeled as polyhedral cones, and our dissimilarity measure proves useful for understanding whether two image sets belong to the same class.
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Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
Zilin Kang, Chonghua Liao, Tingqiang Xu, Huazhe Xu
https://arxiv.org/abs/2510.08549

Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
We propose ERA, a new paradigm that constrains the sampling entropy above given thresholds by applying specially designed activations to the outputs of models. Our approach demonstrates broad effectiveness across different domains: 1) for large language models(LLMs), boosting the AIME 2025 score for Qwen2.5-Math-7B by 37.4%; 2) for continuous control reinforcement learning agents, improving performance by more than 30% over strong baselines such as SAC on the challenging HumanoidBench; 3) for i…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/8]:
- Label Semantics for Robust Hyperspectral Image Classification
Hassan, Roshni, Bari, Islam, Mohammed, Farazi, Rahman

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…

S$^3$F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Network
Md. Saiful Bari Siddiqui, Mohammed Imamul Hassan Bhuiyan
https://arxiv.org/abs/2509.23442

S$^3$F-Net: A Multi-Modal Approach to Medical Image Classification via Spatial-Spectral Summarizer Fusion Network
Convolutional Neural Networks have become a cornerstone of medical image analysis due to their proficiency in learning hierarchical spatial features. However, this focus on a single domain is inefficient at capturing global, holistic patterns and fails to explicitly model an image's frequency-domain characteristics. To address these challenges, we propose the Spatial-Spectral Summarizer Fusion Network (S$^3$F-Net), a dual-branch framework that learns from both spatial and spectral representatio…

Accelerating Dynamic Image Graph Construction on FPGA for Vision GNNs
Anvitha Ramachandran, Dhruv Parikh, Viktor Prasanna
https://arxiv.org/abs/2509.25121 https://

Accelerating Dynamic Image Graph Construction on FPGA for Vision GNNs
Vision Graph Neural Networks (Vision GNNs, or ViGs) represent images as unstructured graphs, achieving state of the art performance in computer vision tasks such as image classification, object detection, and instance segmentation. Dynamic Image Graph Construction (DIGC) builds image graphs by connecting patches (nodes) based on feature similarity, and is dynamically repeated in each ViG layer following GNN based patch (node) feature updates. However, DIGC constitutes over 50% of end to end ViG…

Textual interpretation of transient image classifications from large language models
Fiorenzo Stoppa, Turan Bulmus, Steven Bloemen, Stephen J. Smartt, Paul J. Groot, Paul Vreeswijk, Ken W. Smith
https://arxiv.org/abs/2510.06931

Textual interpretation of transient image classifications from large language models
Modern astronomical surveys deliver immense volumes of transient detections, yet distinguishing real astrophysical signals (for example, explosive events) from bogus imaging artefacts remains a challenge. Convolutional neural networks are effectively used for real versus bogus classification; however, their reliance on opaque latent representations hinders interpretability. Here we show that large language models (LLMs) can approach the performance level of a convolutional neural network on thr…

NeuroSketch: An Effective Framework for Neural Decoding via Systematic Architectural Optimization
Gaorui Zhang, Zhizhang Yuan, Jialan Yang, Junru Chen, Li Meng, Yang Yang
https://arxiv.org/abs/2512.09524 https://arxiv.org/pdf/2512.09524 https://arxiv.org/html/2512.09524
arXiv:2512.09524v1 Announce Type: new
Abstract: Neural decoding, a critical component of Brain-Computer Interface (BCI), has recently attracted increasing research interest. Previous research has focused on leveraging signal processing and deep learning methods to enhance neural decoding performance. However, the in-depth exploration of model architectures remains underexplored, despite its proven effectiveness in other tasks such as energy forecasting and image classification. In this study, we propose NeuroSketch, an effective framework for neural decoding via systematic architecture optimization. Starting with the basic architecture study, we find that CNN-2D outperforms other architectures in neural decoding tasks and explore its effectiveness from temporal and spatial perspectives. Building on this, we optimize the architecture from macro- to micro-level, achieving improvements in performance at each step. The exploration process and model validations take over 5,000 experiments spanning three distinct modalities (visual, auditory, and speech), three types of brain signals (EEG, SEEG, and ECoG), and eight diverse decoding tasks. Experimental results indicate that NeuroSketch achieves state-of-the-art (SOTA) performance across all evaluated datasets, positioning it as a powerful tool for neural decoding. Our code and scripts are available at https://github.com/Galaxy-Dawn/NeuroSketch.
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A Comparison of Selected Image Transformation Techniques for Malware Classification
Rishit Agrawal, Kunal Bhatnagar, Andrew Do, Ronnit Rana, Mark Stamp
https://arxiv.org/abs/2509.10838

A Comparison of Selected Image Transformation Techniques for Malware Classification
Recently, a considerable amount of malware research has focused on the use of powerful image-based machine learning techniques, which generally yield impressive results. However, before image-based techniques can be applied to malware, the samples must be converted to images, and there is no generally-accepted approach for doing so. The malware-to-image conversion strategies found in the literature often appear to be ad hoc, with little or no effort made to take into account properties of execu…

A Semantics-Aware Hierarchical Self-Supervised Approach to Classification of Remote Sensing Images
Giulio Weikmann, Gianmarco Perantoni, Lorenzo Bruzzone
https://arxiv.org/abs/2510.04916

A Semantics-Aware Hierarchical Self-Supervised Approach to Classification of Remote Sensing Images
Deep learning has become increasingly important in remote sensing image classification due to its ability to extract semantic information from complex data. Classification tasks often include predefined label hierarchies that represent the semantic relationships among classes. However, these hierarchies are frequently overlooked, and most approaches focus only on fine-grained classification schemes. In this paper, we present a novel Semantics-Aware Hierarchical Consensus (SAHC) method for learn…

Automatic Classification of Magnetic Chirality of Solar Filaments from H-Alpha Observations
Alexis Chalmers, Azim Ahmadzadeh
https://arxiv.org/abs/2509.18214 https://

Paperless-NGX has become the true successor to #DEVONthink 3 for me, which now focuses on AI and changed its payment model. The automatic document classification is impressively accurate. Container hosting makes self-hosting straightforward. If you want modern, intelligent document management that works cross-platform with full control,

Quantum Probabilistic Label Refining: Enhancing Label Quality for Robust Image Classification
Fang Qi, Lu Peng, Zhengming Ding
https://arxiv.org/abs/2510.00528 https://

Quantum Probabilistic Label Refining: Enhancing Label Quality for Robust Image Classification
Learning with softmax cross-entropy on one-hot labels often leads to overconfident predictions and poor robustness under noise or perturbations. Label smoothing mitigates this by redistributing some confidence uniformly, but treats all samples equally, ignoring intra-class variability. We propose a hybrid quantum-classical framework that leverages quantum non-determinism to refine data labels into probabilistic ones, offering more nuanced, human-like uncertainty representations than label smoot…

Bayesian Modelling of Multi-Year Crop Type Classification Using Deep Neural Networks and Hidden Markov Models
Gianmarco Perantoni, Giulio Weikmann, Lorenzo Bruzzone
https://arxiv.org/abs/2510.07008

Bayesian Modelling of Multi-Year Crop Type Classification Using Deep Neural Networks and Hidden Markov Models
The temporal consistency of yearly land-cover maps is of great importance to model the evolution and change of the land cover over the years. In this paper, we focus the attention on a novel approach to classification of yearly satellite image time series (SITS) that combines deep learning with Bayesian modelling, using Hidden Markov Models (HMMs) integrated with Transformer Encoder (TE) based DNNs. The proposed approach aims to capture both i) intricate temporal correlations in yearly SITS and…

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Hanna Kreutzer, Anne-Sophie Caselitz, Thomas Dratsch, Daniel Pinto dos Santos, Christiane Kuhl, Daniel Truhn, Sven Nebelung
https://arxiv.org/abs/2510.05664 …

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. Methods: This retrospective study included radiography series of the clavicle (n=1,170), elbow (n=3,755), and thumb (n=1,978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To …

microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification
Sathira Silva, Eman Ali, Chetan Arora, Muhammad Haris Khan
https://arxiv.org/abs/2510.02270

microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification
Unsupervised adaptation of CLIP-based vision-language models (VLMs) for fine-grained image classification requires sensitivity to microscopic local cues. While CLIP exhibits strong zero-shot transfer, its reliance on coarse global features restricts its performance on fine-grained classification tasks. Prior efforts inject fine-grained knowledge by aligning large language model (LLM) descriptions with the CLIP $\texttt{[CLS]}$ token; however, this approach overlooks spatial precision. We propos…

Crosslisted article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Error correction in multiclass image classification of facial emotion on unbalanced samples
Andrey A. Lebedev, Victor B. Kazantsev, Sergey V. Stasenko

FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification
Prajit Sengupta, Islem Rekik
https://arxiv.org/abs/2509.10510

FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification
Medical image classification requires not only high predictive performance but also interpretability to ensure clinical trust and adoption. Graph Neural Networks (GNNs) offer a powerful framework for modeling relational structures within datasets; however, standard GNNs often operate as black boxes, limiting transparency and usability, particularly in clinical settings. In this work, we present an interpretable graph-based learning framework named FireGNN that integrates trainable fuzzy rules i…

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…

UMind: A Unified Multitask Network for Zero-Shot M/EEG Visual Decoding
Chengjian Xu, Yonghao Song, Zelin Liao, Haochuan Zhang, Qiong Wang, Qingqing Zheng
https://arxiv.org/abs/2509.14772

UMind: A Unified Multitask Network for Zero-Shot M/EEG Visual Decoding
Decoding visual information from time-resolved brain recordings, such as EEG and MEG, plays a pivotal role in real-time brain-computer interfaces. However, existing approaches primarily focus on direct brain-image feature alignment and are limited to single-task frameworks or task-specific models. In this paper, we propose a Unified MultItask Network for zero-shot M/EEG visual Decoding (referred to UMind), including visual stimulus retrieval, classification, and reconstruction, where multiple t…

Combining Audio and Non-Audio Inputs in Evolved Neural Networks for Ovenbird
Sergio Poo Hernandez, Vadim Bulitko, Erin Bayne
https://arxiv.org/abs/2509.10566 https://

Combining Audio and Non-Audio Inputs in Evolved Neural Networks for Ovenbird
In the last several years the use of neural networks as tools to automate species classification from digital data has increased. This has been due in part to the high classification accuracy of image classification through Convolutional Neural Networks (CNN). In the case of audio data CNN based recognizers are used to automate the classification of species in audio recordings by using information from sound visualization (i.e., spectrograms). It is common for these recognizers to use the spect…

Object-Centric Case-Based Reasoning via Argumentation
Gabriel de Olim Gaul, Adam Gould, Avinash Kori, Francesca Toni
https://arxiv.org/abs/2510.00185 https://

Object-Centric Case-Based Reasoning via Argumentation
We introduce Slot Attention Argumentation for Case-Based Reasoning (SAA-CBR), a novel neuro-symbolic pipeline for image classification that integrates object-centric learning via a neural Slot Attention (SA) component with symbolic reasoning conducted by Abstract Argumentation for Case-Based Reasoning (AA-CBR). We explore novel integrations of AA-CBR with the neural component, including feature combination strategies, casebase reduction via representative samples, novel count-based partial orde…

Sparse Representations Improve Adversarial Robustness of Neural Network Classifiers
Killian Steunou, Sigurd Saue, Th\'eo Druilhe
https://arxiv.org/abs/2509.21130 https://

Sparse Representations Improve Adversarial Robustness of Neural Network Classifiers
Deep neural networks perform remarkably well on image classification tasks but remain vulnerable to carefully crafted adversarial perturbations. This work revisits linear dimensionality reduction as a simple, data-adapted defense. We empirically compare standard Principal Component Analysis (PCA) with its sparse variant (SPCA) as front-end feature extractors for downstream classifiers, and we complement these experiments with a theoretical analysis. On the theory side, we derive exact robustnes…

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…

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- Brain Tumor Classification on MRI in Light of Molecular Markers
Liu, Yuan, Zeng, Tang, Zhang, Lin, Xu, Huang, Wang

Imaging Modalities-Based Classification for Lung Cancer Detection
Sajim Ahmed, Muhammad Zain Chaudhary, Muhammad Zohaib Chaudhary, Mahmoud Abbass, Ahmed Sherif, Mohammad Mahbubur Rahman Khan Mamun
https://arxiv.org/abs/2509.16254

Imaging Modalities-Based Classification for Lung Cancer Detection
Lung cancer continues to be the predominant cause of cancer-related mortality globally. This review analyzes various approaches, including advanced image processing methods, focusing on their efficacy in interpreting CT scans, chest radiographs, and biological markers. Notably, we identify critical gaps in the previous surveys, including the need for robust models that can generalize across diverse populations and imaging modalities. This comprehensive synthesis aims to serve as a foundational …

WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification
Yiwen Jiang, Deval Mehta, Siyuan Yan, Yaling Shen, Zimu Wang, Zongyuan Ge
https://arxiv.org/abs/2509.17740

WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification
Multimodal Large Language Models (MLLMs) have shown promise in visual-textual reasoning, with Multimodal Chain-of-Thought (MCoT) prompting significantly enhancing interpretability. However, existing MCoT methods rely on rationale-rich datasets and largely focus on inter-object reasoning, overlooking the intra-object understanding crucial for image classification. To address this gap, we propose WISE, a Weak-supervision-guided Step-by-step Explanation method that augments any image classificatio…

Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification
Yucheng Lu, Hubert Dariusz Zaj\k{a}c, Veronika Cheplygina, Amelia Jim\'enez-S\'anchez
https://arxiv.org/abs/2510.00902

Intuitions of Machine Learning Researchers about Transfer Learning for Medical Image Classification
Transfer learning is crucial for medical imaging, yet the selection of source datasets - which can impact the generalizability of algorithms, and thus patient outcomes - often relies on researchers' intuition rather than systematic principles. This study investigates these decisions through a task-based survey with machine learning practitioners. Unlike prior work that benchmarks models and experimental setups, we take a human-centered HCI perspective on how practitioners select source datasets…

Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance
Luc Boudier, Loris Manganelli, Eleftherios Tsonis, Nicolas Dufour, Vicky Kalogeiton
https://arxiv.org/abs/2509.22635

Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance
Few-shot image classification remains challenging due to the limited availability of labeled examples. Recent approaches have explored generating synthetic training data using text-to-image diffusion models, but often require extensive model fine-tuning or external information sources. We present a novel training-free approach, called DIPSY, that leverages IP-Adapter for image-to-image translation to generate highly discriminative synthetic images using only the available few-shot examples. DIP…

DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification
Fazle Rafsani, Jay Shah, Catherine D. Chong, Todd J. Schwedt, Teresa Wu
https://arxiv.org/abs/2509.12512

DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification
Anomaly detection and classification in medical imaging are critical for early diagnosis but remain challenging due to limited annotated data, class imbalance, and the high cost of expert labeling. Emerging vision foundation models such as DINOv2, pretrained on extensive, unlabeled datasets, offer generalized representations that can potentially alleviate these limitations. In this study, we propose an attention-based global aggregation framework tailored specifically for 3D medical image anoma…

ERDE: Entropy-Regularized Distillation for Early-exit
Martial Guidez, Stefan Duffner, Yannick Alpou, Oscar R\"oth, Christophe Garcia
https://arxiv.org/abs/2510.04856 https:…

ERDE: Entropy-Regularized Distillation for Early-exit
Although deep neural networks and in particular Convolutional Neural Networks have demonstrated state-of-the-art performance in image classification with relatively high efficiency, they still exhibit high computational costs, often rendering them impractical for real-time and edge applications. Therefore, a multitude of compression techniques have been developed to reduce these costs while maintaining accuracy. In addition, dynamic architectures have been introduced to modulate the level of co…

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Zihan Wei, Tapabrata Chakraborti
https://arxiv.org/abs/2509.22712 https://

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Recent works have shown that deep learning based skin lesion image classification models trained on unbalanced dataset can exhibit bias toward protected demographic attributes such as race, age,and gender. Current bias mitigation methods usually either achieve high level of fairness with the degradation of accuracy, or only improve the model fairness on a single attribute. Additionally usually most bias mitigation strategies are either pre hoc through data processing or post hoc through fairnes…

Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability
Haifei Zhang, Patrick Barry, Eduardo Brandao
https://arxiv.org/abs/2510.00773 https://

Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability
In the context of image classification, Concept Bottleneck Models (CBMs) first embed images into a set of human-understandable concepts, followed by an intrinsically interpretable classifier that predicts labels based on these intermediate representations. While CBMs offer a semantically meaningful and interpretable classification pipeline, they often sacrifice predictive performance compared to end-to-end convolutional neural networks. Moreover, the propagation of uncertainty from concept pred…

Stratify or Die: Rethinking Data Splits in Image Segmentation
Naga Venkata Sai Jitin Jami, Thomas Altstidl, Jonas Mueller, Jindong Li, Dario Zanca, Bjoern Eskofier, Heike Leutheuser
https://arxiv.org/abs/2509.21056

Stratify or Die: Rethinking Data Splits in Image Segmentation
Random splitting of datasets in image segmentation often leads to unrepresentative test sets, resulting in biased evaluations and poor model generalization. While stratified sampling has proven effective for addressing label distribution imbalance in classification tasks, extending these ideas to segmentation remains challenging due to the multi-label structure and class imbalance typically present in such data. Building on existing stratification concepts, we introduce Iterative Pixel Stratifi…

Crosslisted article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- HQCNN: A Hybrid Quantum-Classical Neural Network for Medical Image Classification
, Fahim, Paul, Hossain, Ahmed, Chakraborty

ThinkFake: Reasoning in Multimodal Large Language Models for AI-Generated Image Detection
Tai-Ming Huang, Wei-Tung Lin, Kai-Lung Hua, Wen-Huang Cheng, Junichi Yamagishi, Jun-Cheng Chen
https://arxiv.org/abs/2509.19841

ThinkFake: Reasoning in Multimodal Large Language Models for AI-Generated Image Detection
The increasing realism of AI-generated images has raised serious concerns about misinformation and privacy violations, highlighting the urgent need for accurate and interpretable detection methods. While existing approaches have made progress, most rely on binary classification without explanations or depend heavily on supervised fine-tuning, resulting in limited generalization. In this paper, we propose ThinkFake, a novel reasoning-based and generalizable framework for AI-generated image detec…

DF-LLaVA: Unlocking MLLM's potential for Synthetic Image Detection via Prompt-Guided Knowledge Injection
Zhuokang Shen, Kaisen Zhang, Bohan Jia, Yuan Fang, Zhou Yu, Shaohui Lin
https://arxiv.org/abs/2509.14957

DF-LLaVA: Unlocking MLLM's potential for Synthetic Image Detection via Prompt-Guided Knowledge Injection
With the increasing prevalence of synthetic images, evaluating image authenticity and locating forgeries accurately while maintaining human interpretability remains a challenging task. Existing detection models primarily focus on simple authenticity classification, ultimately providing only a forgery probability or binary judgment, which offers limited explanatory insights into image authenticity. Moreover, while MLLM-based detection methods can provide more interpretable results, they still la…

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…

ClustViT: Clustering-based Token Merging for Semantic Segmentation
Fabio Montello, Ronja G\"uldenring, Lazaros Nalpantidis
https://arxiv.org/abs/2510.01948 https://

ClustViT: Clustering-based Token Merging for Semantic Segmentation
Vision Transformers can achieve high accuracy and strong generalization across various contexts, but their practical applicability on real-world robotic systems is limited due to their quadratic attention complexity. Recent works have focused on dynamically merging tokens according to the image complexity. Token merging works well for classification but is less suited to dense prediction. We propose ClustViT, where we expand upon the Vision Transformer (ViT) backbone and address semantic segmen…

Leveraging Geometric Visual Illusions as Perceptual Inductive Biases for Vision Models
Haobo Yang, Minghao Guo, Dequan Yang, Wenyu Wang
https://arxiv.org/abs/2509.15156 https://…

Leveraging Geometric Visual Illusions as Perceptual Inductive Biases for Vision Models
Contemporary deep learning models have achieved impressive performance in image classification by primarily leveraging statistical regularities within large datasets, but they rarely incorporate structured insights drawn directly from perceptual psychology. To explore the potential of perceptually motivated inductive biases, we propose integrating classic geometric visual illusions well-studied phenomena from human perception into standard image-classification training pipelines. Specifically, …

UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction
Zhi Chen
https://arxiv.org/abs/2509.11108 https://

UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction
Ultrasound imaging is widely used in clinical practice due to its cost-effectiveness, mobility, and safety. However, current AI research often treats disease prediction and tissue segmentation as two separate tasks and their model requires substantial computational overhead. In such a situation, we introduce UltraUPConvNet, a computationally efficient universal framework designed for both ultrasound image classification and segmentation. Trained on a large-scale dataset containing more than 9,7…

Curriculum Multi-Task Self-Supervision Improves Lightweight Architectures for Onboard Satellite Hyperspectral Image Segmentation
Hugo Carlesso, Josiane Mothe, Radu Tudor Ionescu
https://arxiv.org/abs/2509.13229

Curriculum Multi-Task Self-Supervision Improves Lightweight Architectures for Onboard Satellite Hyperspectral Image Segmentation
Hyperspectral imaging (HSI) captures detailed spectral signatures across hundreds of contiguous bands per pixel, being indispensable for remote sensing applications such as land-cover classification, change detection, and environmental monitoring. Due to the high dimensionality of HSI data and the slow rate of data transfer in satellite-based systems, compact and efficient models are required to support onboard processing and minimize the transmission of redundant or low-value data, e.g. cloud-…