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Anastasiia Iasakova
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Founder's Pitch
"BREPS enhances robustness in AI segmentation models by generating realistic adversarial bounding boxes."
Commercial Viability Breakdown
Breakdown pending for this paper.
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Why It Matters
Without BREPS, AI segmentation models may suffer from poor performance in real-world settings due to unaccounted for variations in user-provided bounding boxes, leading to inaccuracies and inefficiencies in applications like medical imaging or robotics.
Product Angle
Create a SaaS platform that provides segmentation robustness evaluation as a service, allowing developers to upload models and against a library of realistic prompts generated by BREPS, returning comprehensive robustness reports.
Disruption
BREPS could replace or augment current segmentation model evaluation practices, which often do not consider realistic user input variability, thus improving the fidelity and reliability of AI deployments.
Product Opportunity
The increasing reliance on AI for critical systems like autonomous vehicles and medical diagnostics creates a demand for robust, fail-safe models. Organizations will pay for tools that ensure their models' reliability and safety.
Use Case Idea
Integrate BREPS into an AI auditing tool that evaluates and certifies the robustness of segmentation AI tools before deployment, particularly for critical applications like autonomous vehicles or medical diagnostics.
Science
The paper introduces a method to evaluate segmentation models' robustness by simulating real user behavior in bounding box creation. It uses BREPS, an adversarial approach that generates realistic bounding boxes to test model variations, highlighting vulnerabilities and variability in segmentation quality across different prompts.
Method & Eval
The method was tested using a large-scale user study with 2,500 participants. It evaluated segmentation models across 10 datasets, both general and medical, with BREPS-generated bounding boxes to assess robustness against realistic variation in user input.
Caveats
The approach may be limited by its dependency on existing datasets and user studies for realism. Additionally, the focus on bounding boxes means other prompt types like text or point prompts remain unaddressed.
Author Intelligence
Andrey Moskalenko
LEAD Lomonosov Moscow State University; FusionBrain Lab; IAI MSU
and.v.moskalenko@gmail.com
Danil Kuznetsov
NUST MISIS
kuznetsov.danil@proton.me
Irina Dudko
NUST MISIS
Anastasiia Iasakova
NUST MISIS
Nikita Boldyrev
FusionBrain Lab
Denis Shepelev
FusionBrain Lab; NUST MISIS
Andrei Spiridonov
FusionBrain Lab
Andrey Kuznetsov
FusionBrain Lab
Vlad Shakhuro
Lomonosov Moscow State University; FusionBrain Lab; NUST MISIS