ALBERT vs SLICK: MARSAIL’s New AI Fashion for Real-Time Car Insurance and Garages

Teerapong Panboonyuen
2025 computer-vision, deep-learning, automotive-ai, marsail
MARSAIL’s ALBERT and SLICK models demonstrate the perfect balance of accuracy and speed, redefining car insurance AI.
You can explore our GitHub project page 📦 here.

ALBERT vs SLICK: The New AI Fashion at MARSAIL

In Thailand’s rapidly evolving automotive insurance sector, the integration of transformer-based segmentation models like MARSAIL-ALBERT and MARSAIL-SLICK marks a pivotal shift toward scalable, AI-driven damage assessment—bringing unprecedented accuracy, efficiency, and trust to claim processing pipelines.

In addressing the complex challenge of fine-grained automotive damage segmentation, we present MARSAIL-ALBERT (Figure 1), a high-capacity teacher model architected on the principles of bidirectional transformer encoding and spatially-aware representation learning. Leveraging the power of attention mechanisms within a multi-scale encoder-decoder framework, MARSAIL-ALBERT excels at capturing subtle visual cues—scratches, dents, fractures—amid high-variance automotive imagery. It is explicitly optimized to model long-range dependencies across both local textures and global structural semantics, enabling precise localization of damage under varying viewpoints, illumination conditions, and occlusion levels.

As demonstrated in Figure 2, the model consistently produces highly detailed segmentation maps, revealing strong robustness to environmental perturbations such as specular highlights, cast shadows, and surface complexity. Functioning as the supervisory core of our framework, MARSAIL-ALBERT serves not only as a performant segmentation engine but also as a teacher network for structured knowledge distillation.

ALBERT Architecture

Figure 1: The architecture of the proposed MARSAIL-ALBERT model, a teacher network that leverages advanced localization and bidirectional encoder representations from transformers for high-precision car damage segmentation. This design facilitates robust spatial reasoning and context-aware feature extraction critical for complex automotive insurance scenarios.

ALBERT Results

Figure 2: Visualization of segmentation outcomes produced by MARSAIL-ALBERT, highlighting the model’s ability to localize fine-grained vehicle damage under varying lighting and occlusion conditions, showcasing its generalization capability across diverse automotive imagery.

To extend this capability into real-time and resource-constrained settings—typical of large-scale deployment in automotive insurance operations—we introduce MARSAIL-SLICK (Figure 3), a compact yet powerful student model distilled directly from MARSAIL-ALBERT. It incorporates a novel Selective Localization mechanism that prioritizes critical damage regions, coupled with an Instance Calibration module that aligns feature representation across inter-instance variability. This combination allows MARSAIL-SLICK to retain the semantic fidelity of its teacher while drastically reducing parameter count and inference time.

As evidenced in Figure 4, the student model maintains competitive segmentation performance, particularly in high-throughput scenarios such as claim triage or automated fleet inspection. Together, the ALBERT–SLICK teacher-student architecture offers a robust, scalable solution for real-world visual understanding tasks in the automotive insurance pipeline, aligning state-of-the-art deep learning with industry-grade reliability and speed.

SLICK Architecture

Figure 3: The architecture of MARSAIL-SLICK, a lightweight student model that incorporates selective localization and instance calibration mechanisms for knowledge-enhanced car damage segmentation. This model efficiently distills knowledge from the teacher network to enable real-time deployment while preserving semantic precision.

SLICK Results

Figure 4: Output predictions from the MARSAIL-SLICK model, demonstrating its capability to maintain high segmentation fidelity despite its reduced computational footprint. The results affirm the effectiveness of our teacher-student framework in knowledge transfer for robust performance in resource-constrained settings.

In the fast-moving world of automotive insurance, where accuracy and turnaround time can make or break the customer experience, MARSAIL (Motor AI Recognition Solution Artificial Intelligence Laboratory) stands at the forefront of transformation. Based in Thailand and led by the visionary Dr. Teerapong Panboonyuen — affectionately known as Dr. Kao — MARSAIL is redefining how artificial intelligence is used in car insurance and garage ecosystems. The lab’s mission is clear: to blend deep research with real-world impact. Earlier this year, Dr. Kao shared on Twitter the debut of MARS, an innovative architecture built on Attention Refinement with Sequential Quadtree Nodes. With its combination of scientific rigor and practical relevance, MARS isn’t just another academic model — it’s a bold step forward in computer vision and deep learning, designed to solve tangible problems in automotive analysis.

Riding on this momentum, MARSAIL has unveiled two game-changing models in 2025: ALBERT and SLICK. These systems are not just incremental updates — they represent a complete rethink of how damage detection and claim assessment can be automated with AI. ALBERT is optimized for real-time car damage classification with high precision, while SLICK focuses on smart localization and segmentation of damage areas, tailored specifically for insurance workflows. Together, they offer insurers and garages tools that are faster, smarter, and more reliable than ever before. Backed by advanced machine learning techniques and a commitment to open research, MARSAIL is helping Thailand — and the region — become a serious global player in automotive AI innovation. Whether you’re in the lab or on the road, MARSAIL is making sure AI drives the future.

MARSAIL Logo

Figure 5: MARSAIL — a leading research lab in Thailand focused on applying AI to car insurance and automotive service innovations.

These models represent more than just technological progress; they embody a new philosophy in AI-powered insurance: a seamless synergy between uncompromising accuracy and lightning-fast efficiency, inspired by the teacher-student paradigm.

At the core of this paradigm lies ALBERT, the “teacher” — a powerhouse model meticulously engineered for razor-sharp precision. ALBERT dives deep into images, discerning the finest scratches, dents, and cracks with near-human expertise. It’s a master of detail, leaving no nuance unseen, perfect for complex offline investigations and comprehensive damage evaluations where absolute accuracy is essential.

In today’s fast-paced insurance ecosystem, speed is just as crucial as accuracy, particularly when it comes to frontline claim processing and on-the-spot damage assessments. This is where SLICK, the “student” model, truly shines. Guided by the advanced expertise of ALBERT, SLICK is engineered for agility and lightning-fast performance, delivering precise damage detection results in real-time. Whether running on edge devices or mobile phones, its optimized architecture allows insurance agents and repair shops to streamline their operations, making decisions faster without ever sacrificing quality.

SLICK: Revolutionizing Car Damage Segmentation with Knowledge-Enhanced AI at MARSAIL

The recent Google AI video on Knowledge Distillation: A Good Teacher is Patient and Consistent offers valuable insights into how AI models can be trained efficiently by leveraging a “teacher-student” framework. The key takeaway from this approach is that a well-trained teacher model can pass down its knowledge to a student model, significantly improving performance and generalization. This technique has sparked new ideas for MARSAIL and our work on SLICK (Selective Localization and Instance Calibration for Knowledge-Enhanced Car Damage Segmentation).

In the same spirit of knowledge transfer, SLICK takes inspiration from the concept of “distilling” knowledge from large, complex models to create an AI system capable of precise, real-world car damage segmentation. Through components like Selective Part Segmentation, Localization-Aware Attention, and Knowledge Fusion, SLICK enhances the ability of AI models to focus on and accurately segment vehicle parts—even under challenging conditions like occlusions and deformations. Much like the patient and consistent teacher-student relationship in knowledge distillation, SLICK learns from vast datasets (including synthetic crash data and real-world insurance records) to ensure robustness and adaptability across a variety of damage scenarios.

At MARSAIL, inspired by Google AI’s knowledge distillation, we’re applying these principles to create an AI system that not only improves segmentation accuracy but also optimizes the entire automotive insurance and repair workflow. With SLICK, we are ready to bring this advanced AI to Thailand, enhancing efficiency, reducing fraud, and setting new standards for the industry.

Inspiration from Andrej Karpathy: Embracing the New Era of AI Innovation at MARSAIL

In the video “Andrej Karpathy: Software Is Changing (Again)”, Karpathy discusses how artificial intelligence and deep learning are driving a new wave of transformation across industries. At MARSAIL, we deeply resonate with his perspective that AI is not just evolving—it’s fundamentally reshaping how we approach problem-solving and automation. Inspired by Karpathy’s vision, we’re applying the latest in AI research to redefine the way car damage estimation, insurance claims, and repair workflows are handled. Just as Karpathy highlights the importance of AI in software development, MARSAIL is leveraging cutting-edge AI models like SLICK to bring accuracy, speed, and efficiency to the automotive sector, helping to transform the Thai automotive insurance ecosystem into a more intelligent and scalable system.

By aligning our research with the principles Karpathy discusses, MARSAIL is at the forefront of AI-driven innovation in the automotive space, bringing faster, more reliable, and trustworthy solutions to insurers, garages, and customers alike.

Together, ALBERT and SLICK form a powerful duo that bridges the traditional divide between accuracy and efficiency — offering the best of both worlds to revolutionize car insurance workflows across Thailand and beyond.

ALBERT: The Teacher Model — Precision at a Cost

ALBERT stands for Advanced Localization and Bidirectional Encoder Representations from Transformers. This model is designed to be highly accurate and detailed in detecting subtle damages such as small scratches, dents, and cracks on vehicles. It leverages a vision transformer architecture enhanced with localized deformable tokens and parameter sharing to precisely focus on critical damage regions.

However, this precision comes with a computational cost. ALBERT requires powerful CUDA-enabled GPUs and is relatively slow, making it ideal for offline batch processing or scenarios where accuracy takes precedence over speed.

MARSAIL ALBERT Model Result

Figure 6: MARSAIL-ALBERT model showcasing detailed and precise damage segmentation results.

SLICK: The Student Model — Lightning Speed Meets Smart Knowledge

To address real-time insurance needs, MARSAIL developed SLICK — Selective Localization and Instance Calibration with Knowledge. This model distills knowledge from ALBERT and integrates domain-specific insurance metadata like bumper zones and vehicle model weak points.

SLICK boosts processing speed by over 700% compared to ALBERT, enabling instant damage assessments on edge devices or mobile apps without sacrificing much accuracy. Its adaptive attention mechanism dynamically calibrates segmentation proposals using contextual knowledge graphs, making it robust under varying light, weather, and occlusion conditions.

MARSAIL SLICK Model Result

Figure 7: MARSAIL-SLICK model delivering rapid, knowledge-enhanced damage segmentation optimized for real-time insurance workflows.

🚘 Teaching machines to see smarter and faster: the MARSAIL teacher-student model

In the race to deliver the best car damage detection for insurance claims, MARSAIL takes a cutting-edge approach inspired by how humans learn: through mentorship. Our teacher-student model architecture pairs a high-capacity “teacher” network with a lean, speedy “student” model, capturing the best of both worlds — precision and efficiency.

Teacher-Student Model Concept Architecture

Figure 8: Conceptual architecture of the MARSAIL teacher-student model (Image source: Daily Dose of Data Science).

What is the teacher-student model?

Think of the teacher as a seasoned expert with a deep understanding of vehicle damage nuances — it’s large, powerful, and painstakingly precise. The student, meanwhile, is like an apprentice: smaller, faster, and designed to perform well in real-world settings with limited resources.

The magic happens when the student learns to mimic the teacher’s insights without needing to replicate its full complexity. This process is known as knowledge distillation — where the teacher’s “soft” predictions guide the student’s training, helping it grasp subtle visual patterns that would be hard to learn from raw data alone.

Simple Teacher-Student Model Concept

Figure 9: Simplified overview of the teacher-student learning framework (Image source: Daily Dose of Data Science).

Measuring size and efficiency: teacher vs. student

To illustrate the trade-off, here’s a glimpse of the teacher and student model sizes trained on the CIFAR-10 dataset. The teacher is notably larger but more precise, while the student’s compact size enables rapid inference — crucial for insurance agents working on the go.

Teacher Size (left) and Student Size (right) on CIFAR-10 dataset

Figure 10: Visual comparison of teacher (left) and student (right) model sizes (Image source: Daily Dose of Data Science).

How does the student learn from the teacher?

The training process involves the student observing both the teacher’s output and the ground truth, gradually adjusting itself to replicate the teacher’s nuanced judgments. This dual supervision accelerates the student’s learning curve, enabling it to deliver near-teacher accuracy with significantly fewer parameters.

How to Learn from Teacher-Student

Figure 11: Diagram showing how the student model learns from the teacher model (Image source: Daily Dose of Data Science).

Results that speak volumes

On multiple datasets and architectures (including CNN and ResNet), MARSAIL’s teacher-student training methods consistently improved student model accuracy across the board — sometimes by over 3% compared to training without guidance.

Model Dataset No KD (%) BLKD (%) TAKD (%)
CNN CIFAR-10 70.16 72.57 73.51
CNN CIFAR-100 41.09 44.57 44.92
ResNet CIFAR-10 88.52 88.65 88.98
ResNet CIFAR-100 61.37 61.41 61.82
ResNet ImageNet 65.20 66.60 67.36
Result Comparison of Models on CIFAR datasets

Figure 12: Model accuracy comparison showing improvement using knowledge distillation techniques (Image source: Daily Dose of Data Science).

Final layer feature summaries

This image visualizes how different layers in each model contribute to the final representation, highlighting the efficiency gains from knowledge distillation that help the student model stay compact yet powerful.

Summary of Final Layers in Each Model

Figure 13: Summary of final layer features in teacher and student models (Image source: Daily Dose of Data Science).

Why does this matter for car insurance?

  • Speed without compromise: Insurance agents and garages need fast, reliable damage detection on smartphones or edge devices. The student model delivers rapid results, trained under the teacher’s expert supervision.

  • Resource efficiency: Smaller models reduce computational costs and power consumption, enabling scalable deployment across Thailand’s wide insurance ecosystem.

  • Robust accuracy: Guided by the teacher, the student avoids common pitfalls of lightweight models, maintaining high performance even in challenging real-world conditions.

  • Accuracy vs. Speed: ALBERT excels in detailed offline analysis, perfect for complex claim investigations. SLICK offers instant, reliable damage detection to accelerate frontline claim approvals and garage estimates.

  • Hardware Flexibility: ALBERT demands high-end GPUs; SLICK can run efficiently on more modest, real-world devices — a game changer for field agents and repair shops.

  • Knowledge Integration: SLICK’s use of insurance-specific metadata bridges the gap between raw image analysis and domain expertise, improving real-world applicability.

Mathematical Insight (Simplified)

At the heart of our system lies a sophisticated process that refines how the model understands and represents visual data at every step. Imagine ALBERT as a multi-layered brain that carefully adjusts its internal view of an image piece by piece. At each layer, it uses two powerful tools: one that lets it look broadly across different parts of the image to understand overall patterns (multi-scale self-attention), and another that processes these insights through focused, step-by-step reasoning (a multilayer perceptron).

$$ z^{(l+1)} = z^{(l)} + \text{MSA}(\text{LN}(z^{(l)})) + \text{MLP}(\text{LN}(z^{(l)})) $$

This dynamic combination helps ALBERT balance the big picture with fine details, ensuring that the model not only recognizes individual features but also how they relate to each other in context. To keep this learning smooth and stable, it applies a normalization step—similar to tuning an instrument—to make sure each layer’s output remains consistent and meaningful.

Parallel to this, SLICK operates like an intelligent curator, enhancing the model’s confidence in its predictions. It does this by merging two streams of knowledge: the direct visual cues from the image itself and additional information pulled from a structured knowledge graph—think of this as a database of domain-specific facts and relationships.

$$ s_{mask} = \sigma(W_q [f_{img} | f_{kg}]) + b $$

To blend these inputs effectively, SLICK employs a gating mechanism that acts like a smart filter or valve. This gate carefully weighs how much influence the visual data and the knowledge graph should each have in shaping the final mask quality scores. By doing so, the model doesn’t just rely on what it sees but also on what it knows about the world, leading to sharper, more reliable segmentation.

In essence, this combination of refined visual understanding and context-aware knowledge integration lets our system adapt its focus dynamically—prioritizing regions and details that matter most for accurate damage assessment and claim processing.

Published Research

Looking Ahead

MARSAIL continues to innovate by balancing AI model accuracy and deployment efficiency. ALBERT and SLICK represent the cutting edge of automotive AI, ready to transform insurance claim processes in Thailand and beyond — enabling smarter, faster, and fairer car insurance.

Citation

Panboonyuen, Teerapong. (Jul 2025). ALBERT vs SLICK: MARSAIL’s New AI Fashion for Real-Time Car Insurance and Garages. Blog post on Kao Panboonyuen. https://kaopanboonyuen.github.io/blog/2025-07-02-albert-vs-slick-marsail-new-ai-fashion/

Or

@article{panboonyuen2025albert_slick,
  title={ALBERT: Advanced Localization and Bidirectional Encoder Representations from Transformers for Automotive Damage Evaluation},
  author={Panboonyuen, Teerapong},
  journal={arXiv preprint arXiv:2506.10524},
  year={2025},
  url={https://arxiv.org/abs/2506.10524}
}

@article{panboonyuen2025slick,
  title={SLICK: Selective Localization and Instance Calibration for Knowledge-Enhanced Car Damage Segmentation in Automotive Insurance},
  author={Panboonyuen, Teerapong},
  journal={arXiv preprint arXiv:2506.10528},
  year={2025},
  url={https://arxiv.org/abs/2506.10528}
}
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References

  1. Panboonyuen, Teerapong. “ALBERT: Advanced Localization and Bidirectional Encoder Representations from Transformers for Automotive Damage Evaluation.” arXiv preprint arXiv:2506.10524 (2025). https://arxiv.org/abs/2506.10524

  2. Panboonyuen, Teerapong. “SLICK: Selective Localization and Instance Calibration for Knowledge-Enhanced Car Damage Segmentation in Automotive Insurance.” arXiv preprint arXiv:2506.10528 (2025). https://arxiv.org/abs/2506.10528

  3. Knowledge Distillation with Teacher Assistant for Model Compression: https://www.dailydoseofds.com/p/knowledge-distillation-with-teacher-assistant-for-model-compression/

automotive-ai computer-vision deep-learning marsail insurance-tech transformers
Teerapong Panboonyuen
Teerapong Panboonyuen

My research focuses on leveraging advanced machine intelligence techniques, specifically computer vision, to enhance semantic understanding, learning representations, visual recognition, and geospatial data interpretation.