A Framework for Open World Object Detection
Bibliographic record
Abstract
Open World Object Detection (OWOD) is a computer vision task that focuses on real-world scenarios where object detection algorithms need to not only detect known and labeled objects but also handle novel and unknown objects that were not seen during training. This distinguishes OWOD from traditional object detection benchmarks, where the scope is limited to detecting only known object classes. The main challenge in OWOD lies in detecting and classifying unknown objects, which were not part of the training data. In standard object detection, objects not overlapping with labeled objects are automatically classified as background. However, these approaches are not suitable for OWOD, as unknown objects may be wrongly predicted as background due to the lack of specific supervision for distinguishing unknown objects from the background. The paper proposes a novel framework for Open World Object Detection called Open World Object Detection based on Non-Parametric classification (OWOD-NP). This method aims to address the challenges of identifying unknown objects and extending the knowledge base by incrementally introducing new object categories. OWOD-NP incorporates a non-parametric learning approach based on mean prototypes and rejection criteria into a standard detector model. The non-parametric learning model allows the system to detect whether the perceived region contains an unknown object and perform incremental learning in an end-to-end manner. The extensive experiments conducted on the benchmark dataset of Pascal Visual Object Classes (VOC) validate the effectiveness of OWOD-NP. Compared to the standard faster RCNN model, OWOD-NP achieves approximately 14% higher mean Average Precision (mAP) in class incremental scenarios. This improvement showcases the capability of OWOD-NP to handle open-world object detection tasks more efficiently. By combining non-parametric learning with object detection, OWOD-NP provides a promising solution for open-world scenarios, where the environment is dynamic and new objects may appear over time. The ability to detect and classify both known and unknown objects makes OWOD-NP a valuable approach for real-world applications in robotics, autonomous systems, and other computer vision tasks. It allows for continuous adaptation and learning, enabling the system to extend its knowledge and cope with ever-changing environments effectively.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".