02 / OUR TECHNOLOGY
Advancing the methods behind reliable multimodal understanding, efficient 3D perception, and predictive modeling of real-world systems.
Improving visual reasoning, multilingual OCR understanding, and hallucination reduction through grounding-aware training and evaluation.
Exploring cross-plane volumetric representations that capture three-dimensional information while reducing the computational demands of conventional 3D processing.
Integrating heterogeneous measurements with spatial and temporal modeling to estimate and predict physical properties in real-world environments.
Vision-language models can generate plausible descriptions that are not supported by visual evidence. We investigate grounding-aware supervised fine-tuning and preference optimization to improve factual reliability and reduce hallucinations. Our research focuses on aligning model responses with observable image content, supported by curated training data, evaluation benchmarks, and experimental results.
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Reading text in real-world images is challenging due to blur, occlusion, perspective distortion, and multilingual content. We develop OCR-aware multimodal post-training methods to improve text extraction and grounded visual reasoning. In our experiments, information completeness improved from 71.3 to 84.6, while hallucination decreased from 18.3% to 5.5%, using the same frozen visual encoder.
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Processing volumetric images with conventional 3D neural networks can be computationally expensive. We investigate Distributive Volumetric Networks (DVNet), which integrate features across orthogonal spatial planes to capture three-dimensional information efficiently. An initial implementation demonstrates cross-plane feature mapping, providing a foundation for future 3D X-ray classification and object detection.
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Physical-world measurements are often sparse, noisy, and distributed across multiple sensors, locations, and time periods. We investigate sensor fusion, spatial statistical modeling, and temporal data analysis to reconstruct and predict physical properties. Prior applications include intelligent road construction, where heterogeneous measurements support spatial prediction and data-driven understanding of material and infrastructure conditions.
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