OLIV: An Artificial Intelligence-Powered Assistant for Object Localization for Impaired Vision
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".