arXiv AI
Sep 1

Background-Free Objectness Learning for Class-Agnostic Detection

Background-Free Objectness Learning (B-FOR) is a dense, class‑agnostic detection framework that learns objectness without treating unlabeled regions as background. It predicts multi‑scale object‑center and scale fields, using spatially structured soft targets to supervise only reliable annotated areas and introduces displacement‑aware scale fields to model object extent. Experiments on PASCAL VOC, MS‑COCO, and Open Images show B‑FOR improves recall by over +10 AR points compared to prior class‑agnostic baselines, with ablation studies confirming the importance of localized supervision and displacement‑aware scaling.

By Dania Batool, Liliana Lo Presti, Marco La Cascia, Filippo Vella
arXiv Computer Vision
Sep 14

DERA: Detached Edge-Residual Adaptation for Prohibited item Detection

arXiv:2609.12411v1 Announce Type: new Abstract: Prohibited-item detection in X-ray imagery remains challenging due to object superposition, weak texture, and material clutter which obscure both seman...

By Yonathan Michael, Mohamad Alansari, Mohammed Bennamoun, Dwarikanath Mahapatra, Andreas Henschel, Naoufel Werghi
arXiv Machine Learning
Jul 17

XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation

arXiv:2607. 14287v1 Announce Type: cross Abstract: Defect segmentation in additive manufacturing (AM) X-ray computed tomography (XCT) images remains challenging due to severe class imbalance and large distribution shifts across scan conditions.

By Md Mahedi Hasan, Md Mushfiqur Rahaman, Alan Pachkovskiy, Imtiaz Ahmed, Jeremy Dawson, Srinjoy Das
arXiv Computer Vision
4d ago

OX-NeRF: 3D X-ray Tomography Reconstruction from Sparse Views Using Implicit Neural Representation

OX-NeRF is a new framework for 3D X‑ray tomography reconstruction from ultra‑sparse views, combining cross‑scene feature learning with scene‑specific optimisation. It uses a CNN to extract shared features, maintains multi‑resolution hash grids for each scene, and fuses these with an MLP that is jointly optimised end‑to‑end. Benchmarks on parallel‑beam and cone‑beam X‑ray datasets show that OX‑NeRF achieves significantly higher reconstruction accuracy than existing radiance field methods for scenes with ten or fewer views.

By Thomas Welsch, Min-Hsin Tu, David J. Chapman, Daniel E. Eakins