LUMIN: Lightweight Universal Manufacturing Inspection Network for Anomaly Detection

• URL: http://arxiv.org/abs/2609.04775v1
• Date: Fri, 04 Sep 2026 06:12:10 GMT
• Title: LUMIN: Lightweight Universal Manufacturing Inspection Network for Anomaly Detection
• Abstract summary: Industrial anomaly detection faces two engineering bottlenecks: memory bank construction latency and inference efficiency.<n>This paper focuses on sampling efficiency and inference optimization for industrial deployment with two core contributions.
• Score: 1.7396047556778942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Industrial anomaly detection faces two engineering bottlenecks: memory bank construction latency and inference efficiency. Traditional sampling algorithms (Farthest Point Sampling, K-Means, etc.) rely on numerous backbone forward passes and iterative distance computations, with construction times ranging from minutes to hours; heavy computation components such as multi-scale feature extraction struggle to meet the millisecond-level real-time requirements of production lines. This paper focuses on sampling efficiency and inference optimization for industrial deployment with two core contributions: (1) PSP (Plugin Sampler Pipeline)---a four-stage adaptive memory bank sampling pipeline based on 18-dimensional pixel metadata and five complementary visual plugins. PSP completes all sampling with zero backbone forward passes; coarse filtering is sub-second numerical sorting, and metadata extraction is a one-time offline cost. PSP supports progressive deployment and incremental updates. (2) Two engineering optimization strategies---parallel memory bank similarity computation (reducing inference memory and latency by over 95\%) and stratified pixel sampling for large-scale evaluation (reducing computation time by 20$\times$ while keeping metrics stable). As a vehicle for validation, we introduce LUMIN (Lightweight Universal Manufacturing Inspection Network) with extreme segmentation-head compression, systematically exploring the accuracy-efficiency frontier against strong baselines. Experiments on five benchmarks demonstrate that PSP matches state-of-the-art sampling accuracy at near-random construction cost (341$\times$ faster than FPS), while inference optimizations reduce evaluation time by 20$\times$ with negligible accuracy loss.

Related papers

• WarpMPC: Large-Batch MPC on GPU via ADMM with Unrolled $LDL^\top$ Factorization [19.701591585846014]
  This paper introduces numerical optimizations for maximizing throughput on GPU when solving large batches of sequential quadratic programming (SQP) iterations.<n>The optimizations are implemented in a toolbox WarpMPC for model-predictive control (MPC) in JAX and Warp.<n>We achieve throughputs of 8,000 to 250,000 SQP iterations per second on nonlinear cartpole, quadrotor, and humanoid robot benchmarks.
  arXiv  Detail & Related papers  (2026-07-13T14:27:47Z)
• HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction [9.951462226612865]
  HEPTv2 is an end-to-end point-transformer architecture that reconstructs tracks from detector hits in one trainable pipeline.<n>On TrackML, HEPTv2 achieves 98.6% double-majority tracking efficiency at a 0.8% fake rate.<n>These results show end-to-end transformers can deliver the accuracy and efficiency required for real-time particle reconstruction at the HL-LHC.
  arXiv  Detail & Related papers  (2026-06-18T16:18:10Z)
• EventShiftFlow: Towards Hardware-efficient FPGA-based Flow Estimation [43.77587889970362]
  Event-based vision sensors offer asynchronous, high-temporal-resolution measurements.<n>Many event-based motion estimation methods are computationally intensive and difficult to map to FPGA hardware.<n>We present a streaming velocity estimator that discretizes asynchronous events into fixed-duration time bins, constructs a 1-bit spatial occupancy grid, and evaluates multiple velocity hypotheses in parallel.
  arXiv  Detail & Related papers  (2026-05-27T11:13:58Z)
• Beyond GEMM-Centric NPUs: Enabling Efficient Diffusion LLM Sampling [14.471123653746275]
  Diffusion Large Language Models (dLLMs) introduce iterative denoising to enable parallel token generation.<n>Our design employs lightweight non-GEMM vector primitives, in-place memory reuse strategies, and a decoupled mixed-precision memory hierarchy.
  arXiv  Detail & Related papers  (2026-01-28T15:37:50Z)
• LUT-Compiled Kolmogorov-Arnold Networks for Lightweight DoS Detection on IoT Edge Devices [20.271194684947282]
  Kolmogorov-Arnold Networks (KANs) offer a compact alternative to Multi-Layer Perceptrons (MLPs)<n>B-spline evaluation introduces significant computational overhead unsuitable for latency-critical IoT applications.<n>We propose a lookup table (LUT) compilation pipeline that replaces expensive spline computations with precomputed quantized tables.
  arXiv  Detail & Related papers  (2026-01-12T22:32:18Z)
• Fast SAM2 with Text-Driven Token Pruning [52.8350457627401]
  Segment Anything Model 2 (SAM2), a vision computation model has significantly advanced in prompt-driven video object segmentation.<n>SAM2 pipelines propagate all visual tokens produced by the image encoder through downstream temporal reasoning modules, regardless of their relevance to the target object.<n>We introduce a text-guided token pruning framework that improves inference efficiency by selectively reducing token density prior to temporal propagation.
  arXiv  Detail & Related papers  (2025-12-24T18:59:05Z)
• Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning [71.30276778807068]
  We propose a unified framework that strategically coordinates sample pruning and token pruning.<n>Q-Tuning achieves a +38% average improvement over the full-data SFT baseline using only 12.5% of the original training data.
  arXiv  Detail & Related papers  (2025-09-28T13:27:38Z)
• Progressive Mixed-Precision Decoding for Efficient LLM Inference [49.05448842542558]
  We introduce Progressive Mixed-Precision Decoding (PMPD) to address the memory-boundedness of decoding.<n>PMPD achieves 1.4$-$12.2$times$ speedup in matrix-vector multiplications over fp16 models.<n>Our approach delivers a throughput gain of 3.8$-$8.0$times$ over fp16 models and up to 1.54$times$ over uniform quantization approaches.
  arXiv  Detail & Related papers  (2024-10-17T11:46:33Z)
• Megapixel Image Generation with Step-Unrolled Denoising Autoencoders [5.145313322824774]
  We propose a combination of techniques to push sample resolutions higher and reduce computational requirements for training and sampling. These include vector-quantized GAN (VQ-GAN), a vector-quantization (VQ) model capable of high levels of lossy - but perceptually insignificant - compression; hourglass transformers, a highly scaleable self-attention model; and step-unrolled denoising autoencoders (SUNDAE), a non-autoregressive (NAR) text generative model. Our proposed framework scales to high-resolutions ($1024 times 1024$) and trains quickly (
  arXiv  Detail & Related papers  (2022-06-24T15:47:42Z)
• Efficient Decoder-free Object Detection with Transformers [75.00499377197475]
  Vision transformers (ViTs) are changing the landscape of object detection approaches. We propose a decoder-free fully transformer-based (DFFT) object detector. DFFT_SMALL achieves high efficiency in both training and inference stages.
  arXiv  Detail & Related papers  (2022-06-14T13:22:19Z)
• Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining [58.10436813430554]
  Mini-batch training of graph neural networks (GNNs) requires a lot of computation and data movement. We argue in favor of performing mini-batch training with neighborhood sampling in a distributed multi-GPU environment. We present a sequence of improvements to mitigate these bottlenecks, including a performance-engineered neighborhood sampler. We also conduct an empirical analysis that supports the use of sampling for inference, showing that test accuracies are not materially compromised.
  arXiv  Detail & Related papers  (2021-10-16T02:41:35Z)
• SADet: Learning An Efficient and Accurate Pedestrian Detector [68.66857832440897]
  This paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector. It forms a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection. Though structurally simple, it presents state-of-the-art result and real-time speed of $20$ FPS for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-07-26T12:32:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.