Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Enhanced Vision-Language Models for Diverse Sensor Understanding: Cost-Efficient Optimization and Benchmarking.

Sangyun Chung, Youngjoon Yu, Seyeon Kim

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 9, 2026
    PubMed
    Summary

    Vision-Language Models (VLMs) struggle with non-RGB sensor data. Our new method, Sensor-Aware Attributes Fine-Tuning (SAFT), enhances VLM understanding of diverse sensor images efficiently, without architecture changes.

    Related Concept Videos

    Vision01:24

    Vision

    Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
    Depth Perception and Spatial Vision01:15

    Depth Perception and Spatial Vision

    Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    AIM: An Advanced Hybrid Inference Model Combining Clinical Rules and Lifelog-Based Learning for Health Risk Prediction.

    Life (Basel, Switzerland)·2026
    Same author

    Country-level determinants of healthcare access for refugee populations in a host country: a comparative case study.

    International journal for equity in health·2026
    Same author

    Targeted social and behavior change communication (SBCC) in a high adolescent pregnancy setting of Bangladesh: a cluster-randomized study protocol engaging key family members.

    Implementation science : IS·2026
    Same author

    A Causal Lens on Non-RGB Vision Sensor Understanding in Vision-Language Models.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same author

    NetCrafter: ontology-derived gene network modeling and functional interpretation.

    Briefings in bioinformatics·2026
    Same author

    Prognostic factors for spinal cord cavernous hemangioma resection and optimal surgical timing for symptomatic patients.

    Journal of neurosurgery. Spine·2026

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Large-scale Vision-Language Models (VLMs) excel at aligning text and RGB images.
    • VLMs exhibit limited understanding of physical properties in non-RGB sensor images.
    • Existing VLMs possess inherent RGB-centric biases.

    Purpose of the Study:

    • To address the limitations of VLMs in understanding non-RGB sensor image data.
    • To introduce a cost-efficient paradigm for advancing sensor image understanding.
    • To overcome RGB-centric biases in current VLM architectures.

    Main Methods:

    • Proposed Sensor-Aware Attributes Fine-Tuning (SAFT) method.
    • Introduced Diverse Negative Attributes (DNA) optimization for robust learning.

    Related Experiment Videos

  • Developed VS-BENCH, a comprehensive benchmark for evaluating sensor-specific VLM understanding.

    • Main Results:

    • SAFT enables robust learning of non-RGB characteristics using minimal sensor-specific data.
    • The method achieves superior performance and generalization across diverse sensor modalities.
    • Demonstrated effectiveness in resource-constrained and architecture-invariant settings.

    Conclusions:

    • SAFT offers a practical advancement for VLM deployment in sensor-diverse environments.
    • The approach overcomes limitations without extensive training data or VLM modifications.
    • Validated through extensive experiments on various VLMs and sensor modalities.