Tricking Open-World Object Recognition Models: Uncertainty in Out-of-Distribution Detection

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Research investigated the performance of three Open-World object recognition models—YOLO-World, Grounding Dino, and GPT-4o—when confronted with "impossible prompts" instructing them to detect non-existing objects. Tested on LVIS, Open Images, and JUS datasets, the experiment aimed to assess model uncertainty in real-life scenarios where object existence is unknown. GPT-4o exhibited the poorest performance in both object recognition and uncertainty estimation, demonstrating high overconfidence. In contrast, YOLO-World and Grounding Dino were slightly underconfident but showed superior uncertainty calibration compared to GPT-4o. Despite this, all three models occasionally assigned high-confidence predictions to non-existing objects, highlighting a need for improved uncertainty estimation in out-of-distribution detection.

Key takeaway

For Machine Learning Engineers developing open-world object recognition systems, you must rigorously test model uncertainty when objects are not present. Prioritize models like YOLO-World or Grounding Dino that demonstrate better uncertainty calibration over highly overconfident alternatives like GPT-4o. Your evaluation should include "impossible prompts" to identify and mitigate false high-confidence predictions on non-existing entities, enhancing real-world reliability.

Key insights

Open-World object recognition models struggle with uncertainty when prompted for non-existing objects, particularly GPT-4o's overconfidence.

Principles

• Models often fail to assess non-existent objects.
• Overconfidence is a critical issue in OOD detection.
• Uncertainty calibration varies significantly across models.

Method

Models were tested on LVIS, Open Images, and JUS datasets using "impossible prompts" to detect non-existing objects, observing their uncertainty performance.

In practice

• Evaluate models using impossible prompts.
• Prioritize uncertainty calibration in OOD tasks.
• Compare model over/underconfidence.

Topics

• Open-World Object Recognition
• Uncertainty Estimation
• Out-of-Distribution Detection
• YOLO-World
• Grounding Dino
• GPT-4o

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.