[Paper] User Prompting Strategies and Prompt Enhancement Methods for Open-Set Object Detection in XR Environments

Source: arXiv - 2601.23281v1

Overview

Open‑set object detection (OSOD) aims to locate objects and recognize when something falls outside the set of known classes. While recent OSOD models achieve impressive numbers on benchmark datasets, we still know little about how they behave when users interact with them in mixed‑reality (XR) applications. This paper investigates exactly that: how different ways of phrasing a user’s visual prompt affect OSOD performance, and how simple “prompt‑enhancement” tricks can make the models far more reliable in real‑world XR scenarios.

Key Contributions

• Prompt taxonomy for XR – defines four realistic user‑prompt styles (standard, under‑detailed, over‑detailed, pragmatically ambiguous).
• Empirical evaluation on real XR imagery – tests two state‑of‑the‑art OSOD models, GroundingDINO and YOLO‑E, using vision‑language models to synthesize the diverse prompts.
• Prompt‑enhancement techniques – proposes two lightweight methods (semantic expansion & confidence‑based filtering) that can be applied at inference time without retraining the detector.
• Quantitative robustness analysis – shows that ambiguous prompts cause the biggest performance drop, while under‑detailed prompts are surprisingly benign.
• Actionable guidelines – delivers concrete prompting strategies and enhancement pipelines that XR developers can adopt today.

Methodology

1. Dataset collection – the authors captured a set of XR screenshots (mixed‑reality overlays, hand‑held device views) containing both known objects (e.g., chairs, laptops) and truly unknown items (novel gadgets, decorative props).
2. Prompt generation – using a large vision‑language model (e.g., GPT‑4V), they automatically rewrote each image’s ground‑truth description into the four prompt styles:
   • Standard: concise, accurate label list.
   • Underdetailed: missing qualifiers (e.g., “chair” instead of “red office chair”).
   • Overdetailed: overly specific adjectives and context.
   • Ambiguous: includes vague terms (“something like a table”) or contradictory cues.
3. OSOD inference – both GroundingDINO (a grounding‑based detector) and YOLO‑E (a region‑based detector with an unknown‑class head) were run on every image‑prompt pair.
4. Prompt‑enhancement pipelines – two post‑processing steps were tested:
   • Semantic expansion – enriches the prompt with synonyms and hypernyms extracted from a lexical database (WordNet).
   • Confidence‑based filtering – discards low‑confidence detections that conflict with the prompt’s semantic scope.
5. Metrics – mean Intersection‑over‑Union (mIoU) for localization, average detection confidence, and unknown‑class rejection rate were reported.

Results & Findings

• Stability under under‑detail – both models still locate objects correctly when prompts omit adjectives.
• Vulnerability to ambiguity – vague or contradictory language caused up to a 30% drop in mIoU.
• Over‑detail hurts grounding‑based models – GroundingDINO’s attention mechanism gets distracted by excessive qualifiers.
• Prompt enhancement rescues performance – semantic expansion alone recovers >50% of the lost mIoU on ambiguous prompts; confidence filtering further reduces false positives on unknown objects.

Practical Implications

• XR UI designers can embed a “prompt‑assistant” that automatically expands user‑typed queries (e.g., turning “a chair” into “chair, any style, indoor”) before feeding them to the OSOD engine.
• Developers of AR glasses can run the lightweight enhancement pipeline on‑device (it’s just a few dictionary look‑ups and a confidence threshold) to make object detection robust to noisy voice commands.
• Game and training simulators that rely on dynamic scene understanding can safely ignore unknown objects without retraining the detector, simply by applying the confidence filter.
• Cross‑platform SDKs (Unity, Unreal) can expose a “PromptStrategy” API that selects the appropriate style (standard vs. under‑detailed) based on the interaction modality (hand‑gesture vs. voice).

In short, the research shows that you don’t need a new model to handle real‑world XR prompting—just smarter preprocessing of the user’s natural language.

Limitations & Future Work

• The study uses synthetic prompt variations generated by a vision‑language model; real user data (voice transcripts, typed queries) may exhibit richer error patterns.
• Only two OSOD architectures were evaluated; newer transformer‑based detectors could behave differently, especially under over‑detailed prompts.
• Prompt‑enhancement methods rely on external lexical resources; multilingual or domain‑specific vocabularies were not explored.
• Future work could integrate the enhancement directly into the detector’s attention module, enabling end‑to‑end learning of prompt robustness.

Authors

• Junfeng Lin
• Yanming Xiu
• Maria Gorlatova

Paper Information

• arXiv ID: 2601.23281v1
• Categories: cs.CV
• Published: January 30, 2026
• PDF: Download PDF