← Back to all news
arXiv Computer Vision By Pengfei Yang

LUMIN: Lightweight Universal Manufacturing Inspection Network for Anomaly Detection

Read the original on arXiv Computer Vision →

The paper introduces LUMIN, a lightweight network for manufacturing anomaly detection, and PSP, a four‑stage adaptive memory bank sampling pipeline that eliminates backbone forward passes. PSP achieves near‑random construction speed, 341× faster than FPS, while parallel similarity computation and stratified pixel sampling cut inference time by 20× with minimal accuracy loss. Experiments on five benchmarks show that LUMIN and PSP reach state‑of‑the‑art sampling accuracy with dramatically reduced latency and memory usage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Jul 23

SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection

arXiv:2604. 26633v2 Announce Type: replace-cross Abstract: Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly.

By Paul Julius K\"uhn, Mika Pommeranz, Arjan Kuijper, Saptarshi Neil Sinha
diffusionfine-tuningmultimodal
More like this →
arXiv Machine Learning
Aug 11

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection

arXiv:2608. 07770v1 Announce Type: new Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial conditions is less clear.

By Mike Szklarzewski, CJ George, Gavin Smithson, Christopher Stokes, Dakota Fulp, William M. Jones, Benjamin Wynn, Alexander Ur, Agit Yesiloz, Clint Kallenbach, Mark Swartz, Nathan DeBardeleben, Sharmistha Chakrabarti
benchmarksdata-engineering
More like this →
arXiv Computer Vision
Oct 22

DetM-Tracker: Deterministic and Memory-Efficient VIS for Autonomous Industrial Inspection

DetM-Tracker is a lightweight Video Instance Segmentation framework designed for autonomous industrial inspection. It reduces memory and computation by condensing instance features into four-scalar descriptors, dynamically allocating query capacity, and using entropy-gated skip connections to focus processing on uncertain regions. The method achieves roughly 20× lower inference latency and 6× less VRAM usage than dense-feature baselines.

By You-Rui Lin, Jen-Chieh Wu, Tzu-Ti Wei, Jen-Jee Chen, Yu-Chee Tseng
agentscomputer-visionefficiencybenchmarks
More like this →
arXiv Computer Vision
Sep 14

Joint Architecture-Token-Bitwidth Multi-Axis Optimization of Vision Transformers for Semiconductor IC Packaging

The paper introduces a holistic framework that jointly optimizes architecture, token, and bit-width for Vision Transformers (ViTs). It uses Neural Architecture Search (AutoFormer) to find compact backbones, token merging (ToMe) to reduce information processing, and fp16 mixed‑precision inference to accelerate operations. Experiments on ImageNet‑1K and a real‑world 3D X‑ray semiconductor defect dataset show over 10× improvements in throughput and reductions in parameters, FLOPs, and energy while preserving accuracy.

By Phat Nguyen, Xue Geng, Kaixin Xu, Wang Zhe, Xulei Yang, Ngai-Man Cheung
llmsefficiency
More like this →