Tootfinder

Learning Object-Centric Representations in SAR Images with Multi-Level Feature Fusion
Oh-Tae Jang, Min-Gon Cho, Kyung-Tae Kim
https://arxiv.org/abs/2509.09298 https://

Learning Object-Centric Representations in SAR Images with Multi-Level Feature Fusion
Synthetic aperture radar (SAR) images contain not only targets of interest but also complex background clutter, including terrain reflections and speckle noise. In many cases, such clutter exhibits intensity and patterns that resemble targets, leading models to extract entangled or spurious features. Such behavior undermines the ability to form clear target representations, regardless of the classifier. To address this challenge, we propose a novel object-centric learning (OCL) framework, named…

Curriculum Learning with Synthetic Data for Enhanced Pulmonary Nodule Detection in Chest Radiographs
Pranav Sambhu, Om Guin, Madhav Sambhu, Jinho Cha
https://arxiv.org/abs/2510.07681

Curriculum Learning with Synthetic Data for Enhanced Pulmonary Nodule Detection in Chest Radiographs
This study evaluates whether integrating curriculum learning with diffusion-based synthetic augmentation can enhance the detection of difficult pulmonary nodules in chest radiographs, particularly those with low size, brightness, and contrast, which often challenge conventional AI models due to data imbalance and limited annotation. A Faster R-CNN with a Feature Pyramid Network (FPN) backbone was trained on a hybrid dataset comprising expert-labeled NODE21 (1,213 patients; 52.4 percent male; me…

Comparative Evaluation of Traditional and Deep Learning Feature Matching Algorithms using Chandrayaan-2 Lunar Data
R. Makharia, J. G. Singla, Amitabh, N. Dube, H. Sharma
https://arxiv.org/abs/2509.04775

Comparative Evaluation of Traditional and Deep Learning Feature Matching Algorithms using Chandrayaan-2 Lunar Data
Accurate image registration is critical for lunar exploration, enabling surface mapping, resource localization, and mission planning. Aligning data from diverse lunar sensors -- optical (e.g., Orbital High Resolution Camera, Narrow and Wide Angle Cameras), hyperspectral (Imaging Infrared Spectrometer), and radar (e.g., Dual-Frequency Synthetic Aperture Radar, Selene/Kaguya mission) -- is challenging due to differences in resolution, illumination, and sensor distortion. We evaluate five feature …

F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data
Ziyin Zhang, Zihan Liao, Hang Yu, Peng Di, Rui Wang
https://arxiv.org/abs/2510.02294 …

F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data
We introduce F2LLM - Foundation to Feature Large Language Models, a suite of state-of-the-art embedding models in three sizes: 0.6B, 1.7B, and 4B. Unlike previous top-ranking embedding models that require massive contrastive pretraining, sophisticated training pipelines, and costly synthetic training data, F2LLM is directly finetuned from foundation models on 6 million query-document-negative tuples curated from open-source, non-synthetic datasets, striking a strong balance between training cos…

Multi-level SSL Feature Gating for Audio Deepfake Detection
Hoan My Tran, Damien Lolive, Aghilas Sini, Arnaud Delhay, Pierre-Fran\c{c}ois Marteau, David Guennec
https://arxiv.org/abs/2509.03409

Multi-level SSL Feature Gating for Audio Deepfake Detection
Recent advancements in generative AI, particularly in speech synthesis, have enabled the generation of highly natural-sounding synthetic speech that closely mimics human voices. While these innovations hold promise for applications like assistive technologies, they also pose significant risks, including misuse for fraudulent activities, identity theft, and security threats. Current research on spoofing detection countermeasures remains limited by generalization to unseen deepfake attacks and la…

Optimization techniques for SQL ML queries: A performance analysis of real-time feature computation in OpenMLDB
Mashkhal A. Sidiq, Aras A. Salih, Samrand M. Hassan
https://arxiv.org/abs/2509.15529

Optimization techniques for SQL+ML queries: A performance analysis of real-time feature computation in OpenMLDB
In this study, we optimize SQL+ML queries on top of OpenMLDB, an open-source database that seamlessly integrates offline and online feature computations. The work used feature-rich synthetic dataset experiments in Docker, which acted like production environments that processed 100 to 500 records per batch and 6 to 12 requests per batch in parallel. Efforts have been concentrated in the areas of better query plans, cached execution plans, parallel processing, and resource management. The experim…

Towards multi-purpose locally differentially-private synthetic data release via spline wavelet plug-in estimation
Thibault Randrianarisoa, Lukas Steinberger, Botond Szab\'o
https://arxiv.org/abs/2508.13969

Towards multi-purpose locally differentially-private synthetic data release via spline wavelet plug-in estimation
We develop plug-in estimators for locally differentially private semi-parametric estimation via spline wavelets. The approach leads to optimal rates of convergence for a large class of estimation problems that are characterized by (differentiable) functionals $Λ(f)$ of the true data generating density $f$. The crucial feature of the locally private data $Z_1,\dots, Z_n$ we generate is that it does not depend on the particular functional $Λ$ (or the unknown density $f$) the analyst wants to es…

Fifty Years of SAR Automatic Target Recognition: The Road Forward
Jie Zhou, Yongxiang Liu, Li Liu, Weijie Li, Bowen Peng, Yafei Song, Gangyao Kuang, Xiang Li
https://arxiv.org/abs/2509.22159

Fifty Years of SAR Automatic Target Recognition: The Road Forward
This paper provides the first comprehensive review of fifty years of synthetic aperture radar automatic target recognition (SAR ATR) development, tracing its evolution from inception to the present day. Central to our analysis is the inheritance and refinement of traditional methods, such as statistical modeling, scattering center analysis, and feature engineering, within modern deep learning frameworks. The survey clearly distinguishes long-standing challenges that have been substantially miti…