OLIV: An Artificial Intelligence-Powered Assistant for Object Localization for Impaired Vision
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces OLIV, a novel end-to-end artificial intelligence-powered assistant system designed to aid individuals with impairedvision in their day-to-day tasks in locating displaced objects. Toachieve this goal, OLIV leverages the current advances in AI-basedspeech recognition, speech generation, and object detection to un-derstand the user’s request and give directions to the relative loca-tion of the displaced object. OLIV consists of three main modules:i) a speech module, ii) an object detection module, and iii) a logicunit module. The speech module interfaces with the user to inter-pret the verbal query of the user and verbally responds to the user.The object detection module identifies the objects of interest andtheir associated locations in a scene. Finally, the logic unit modulemakes sense of the user’s intent along with the localized objects ofinterest, and builds a semantic description that the user can under-stand for the speech module to convey verbally back to the user.Initial results from a proof-of-concept system trained to localize fourdifferent types of objects show promise to the feasibility of OLIV asa useful aid for individuals with impaired vision.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it