SynSpill: Improved Industrial Spill Detection With Synthetic Data
Aaditya Baranwal et al. (University of Central Florida)

On the Use of Time Series Foundation Models for Industrial Process Management
Seongbeom Park et al. (Ulsan National Institute of Science and Technology)

Towards Automated Assembly Quality Inspection with Synthetic Data and Domain Randomization
Xiaomeng Zhu et al. (KTH Royal Institute of Technology)

Self-supervised Learning for the Process Management of Continuous Multistage Manufacturing Processes
Hojin Cho et al. (Ulsan National Institute of Science and Technology)

RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentation
Mehrdad Moradi et al. (Georgia Institute of Technology)

iSafetyBench: A video-language benchmark for safety in industrial environment
Raiyaan Abdullah et al. (University of Central Florida)

A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection
Francesco Setti et al. (Università degli Studi di Verona)

InspectVLM: Unified in Theory, Unreliable in Practice
Jonathan Lwowski et al. (Zeitview Inc)

SupConWI-RL: wafer inspection with reinforcement learning enhanced by supervised contrastive learning
Aleksandr Dekhovich et al. (Delft University of Technology)

PCB-SAID: A Low-Cost Camera-Based Dataset for Few-Shot SMD Assembly Inspection
Raffaele Mineo et al. (Università degli Studi di Catania)

WS2: Weakly Supervised Segmentation using Before-After Supervision in Waste Sorting
Andrea Marelli et al. (Politecnico di Milano)

Robust Anomaly Detection in Industrial Environments via Meta-Learning
Francesco Setti et al. (Università degli Studi di Verona)

Development and Implementation of a Digital Twin for Power Plants: Enhancing Maintenance and Operational Efficiency
Guneet Bhatia et al. (Siemens Energy)

Counting Stacked Objects
Corentin Dumery et al. (École polytechnique fédérale de Lausanne, EPFL)

UniLEAD: A Unified and Lightweight model for Anomaly Detection
Shih-Chih Lin et al. (National Tsing Hua University)

GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection
Donghyeong Kim et al. (Yonsei University)

Deep Learning-based Rail Surface Condition Evaluation
Shilin Hu et al. (Stony Brook University)