mlsanityData to 14 Oct 2026

Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling

arXiv 2506.13282 ↗First posted 16 Jun 2025cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Recycling steel scrap can reduce carbon dioxide (CO2) emissions from the steel industry. However, a significant challenge in steel scrap recycling is the inclusion of impurities other than steel. To address this issue, we propose vision-language-model-based anomaly detection where a model is finetuned in a supervised manner, enabling it to handle niche objects effectively. This model enables automated detection of anomalies at a fine-grained level within steel scrap. Specifically, we finetune the image encoder, equipped with multi-scale mechanism and text prompts aligned with both normal and anomaly images. The finetuning process trains these modules using a multiclass classification as the supervision.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 16 Jun 2025.

Builds on

20 of 31 references.

References · 20

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2023Segment Anythingcited by 8,234
  4. 2017Mask R-CNNcited by 7,277
  5. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  6. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  7. 2016Pyramid Scene Parsing Networkcited by 2,479
  8. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,245
  9. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  10. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  11. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  12. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 400
8 more
  1. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  2. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
  3. 2019Dice Loss for Data-imbalanced NLP Taskscited by 182
  4. 2024The Road Less Scheduledcited by 163
  5. 2023APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADcited by 130
  6. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 111
  7. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  8. 2024ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentationcited by 49

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026SteelDefectX: A Multi-Form Vision-Language Dataset and Benchmark for Steel Surface Defect Analysis
  2. 2024FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language Model
  3. 2026GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
  4. 2025From Lab to Factory: Pitfalls and Guidelines for Self-/Unsupervised Defect Detection on Low-Quality Industrial Images
  5. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
  6. 2025Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models
  7. 2025RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection
  8. 2026MMVIAD: Multi-view Multi-task Video Understanding for Industrial Anomaly Detection