# Vision-Language Models for Infrared Industrial Sensing in Additive Manufacturing Scene Description

arXiv: https://arxiv.org/abs/2512.11098 · HTML: https://mlsanity.com/paper/2512.11098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://mlsanity.com/paper/2303.15389/) (2023)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [Segment Any Anomaly without Training via Hybrid Prompt Regularization](https://mlsanity.com/paper/2305.10724/) (2023)
- [ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation](https://mlsanity.com/paper/2401.12665/) (2024)
- [A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG](https://mlsanity.com/paper/2503.24307/) (2025)
- [Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models](https://mlsanity.com/paper/2505.15130/) (2025)
- [NMformer: A Transformer for Noisy Modulation Classification in Wireless Communication](https://mlsanity.com/paper/2411.02428/) (2024)
- [Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration](https://mlsanity.com/paper/2505.11191/) (2025)
- [ASSERTIFY: Utilizing Large Language Models to Generate Assertions for Production Code](https://mlsanity.com/paper/2411.16927/) (2024)
- [Simulating a Bias Mitigation Scenario in Large Language Models](https://mlsanity.com/paper/2509.14438/) (2025)
- [Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications](https://mlsanity.com/paper/2504.16972/) (2025)
- [ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment](https://mlsanity.com/paper/2506.22967/) (2025)
- [Automated Feature Tracking for Real-Time Kinematic Analysis and Shape Estimation of Carbon Nanotube Growth](https://mlsanity.com/paper/2508.19232/) (2025)

## Abstract (arXiv)

Many manufacturing environments operate in low-light conditions or within enclosed machines where conventional vision systems struggle. Infrared cameras provide complementary advantages in such environments. Simultaneously, supervised AI systems require large labeled datasets, which makes zero-shot learning frameworks more practical for applications including infrared cameras. Recent advances in vision-language foundation models (VLMs) offer a new path in zero-shot predictions from paired image-text representations. However, current VLMs cannot understand infrared camera data since they are trained on RGB data. This work introduces VLM-IRIS (Vision-Language Models for InfraRed Industrial Sensing), a zero-shot framework that adapts VLMs to infrared data by preprocessing infrared images captured by a FLIR Boson sensor into RGB-compatible inputs suitable for CLIP-based encoders. We demonstrate zero-shot workpiece presence detection on a 3D printer bed where temperature differences between the build plate and workpieces make the task well-suited for thermal imaging. VLM-IRIS converts the infrared images to magma representation and applies centroid prompt ensembling with a CLIP ViT-B/32 encoder to achieve high accuracy on infrared images without any model retraining. These findings demonstrate that the proposed improvements to VLMs can be effectively extended to thermal applications for label-free monitoring.