Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requires instance-level feedback that answers where a defect occurs, what type it is, why it is defective, and its importance to overall image quality.
This paper presents the IEEE International Conference on Multimedia and Expo (ICME) 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing. The challenge is motivated by two key limitations of existing industrial defect inspection systems: (1) current deep learning-based methods often suffer significant performance degradation when deployed in unseen production scenarios, and (2) most benchmarks neglect severity-aware assessment, which is critical for risk control and yield optimization.
arXiv:2610.00994v1 Announce Type: new Abstract: Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce V...
arXiv:2606. 07953v1 Announce Type: new Abstract: Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding.
SalArt-VQA is a diagnostic benchmark that tests whether vision‑language models can understand salient artifacts in AI‑generated images. It includes 950 images and 3,681 multiple‑choice questions that assess artifact presence, semantic localization, spatial grounding, and evidence‑grounded defect identification. The benchmark reveals that high image‑level detection accuracy can mask failures in grounded understanding, showing a trade‑off between sensitivity and calibration.
arXiv:2603. 14342v2 Announce Type: replace-cross Abstract: Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery.
arXiv:2609.37576v1 Announce Type: new Abstract: With the rapid advancement of text-to-image (T2I) generation, robust evaluation becomes critical yet challenging, as traditional metrics fail to captur...
The paper investigates whether in‑domain training alone can enable multimodal large language models (MLLMs) to perform fine‑grained industrial anomaly understanding (MM‑IAU). It shows that even specialized MLLMs fail to match human experts on defect localization and detection, and that combining different MLLMs does not eliminate this shortfall. To address this, the authors propose SiGMA, a spatially grounded multi‑agent framework that splits tasks among heterogeneous MLLM agents and a dedicated visual defect expert, achieving 85.2% accuracy on the MMAD benchmark—close to human performance and surpassing the best trained specialist.
The paper introduces the CO-AID dataset, which captures systematic defects in state‑of‑the‑art text‑to‑image models when prompts involve complex composition such as multiple entities and attributes. Researchers manually curated 651 reference images across people, hand, object, and scene categories, edited ChatGPT‑generated prompts to emphasize compositional factors, and generated AI images with three T2I models. A subjective study with 29 participants produced multi‑label defect annotations, enabling training of a deep model that predicts defects and improves image generation.
arXiv:2606. 01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task.
The paper introduces Anomaly‑LR, a defect‑grounded latent reasoning framework for industrial anomaly detection that builds a global understanding of an image and then refines anomaly‑relevant representations directly in visual latent space. It also presents IAD‑LR‑22K, a new instruction dataset with 22,228 image‑question pairs and detailed annotations. Experiments demonstrate that Anomaly‑LR outperforms comparable‑scale methods on multiple IAD benchmarks without needing external references or tools.
AgroGround is a large‑scale dataset that links plant disease and agricultural target labels to image regions, enabling models to both recognize and localize these targets. It was created by automatically converting eight agricultural VQA datasets into 794,850 grounded instruction examples, including healthy images for negative supervision. Fine‑tuning a shared vision‑language model on these instructions improves recognition, joint accuracy, and healthy‑image abstention, outperforming its annotation teacher on both the AgroGround benchmark and the external PlantSeg test set.