DecompoVision: Reliability Analysis of Machine Vision Components through Decomposition and Reuse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Analyzing reliability of Machine Vision Components (MVC) against scene changes (such as rain or fog) in their operational environment is crucial for safety-critical applications. Safety analysis relies on the availability of precisely specified and, ideally, machine-verifiable requirements. The state-of-the-art reliability framework ICRAF developed machine-verifiable requirements obtained using human performance data. However, ICRAF is limited to analyzing reliability of MVCs solving simple vision tasks, such as image classification. Yet, many real-world safety-critical systems require solving more complex vision tasks, such as object detection and instance segmentation. Fortunately, many complex vision tasks (which we call “c-tasks”) can be represented as a sequence of simple vision subtasks. For instance, object detection can be decomposed as object localization followed by classification. Based on this fact, in this paper, we show that the analysis of c-tasks can also be decomposed as a sequential analysis of their simple subtasks, which allows us to apply existing techniques for analyzing simple vision tasks. Specifically, we propose a modular reliability framework, DecompoVision, that decomposes: (1) the problem of solving a c-task, (2) the reliability requirements, and (3) the reliability analysis, and, as a result, provides deeper insights into MVC reliability. DecompoVision extends ICRAF to handle complex vision tasks and enables reuse of existing artifacts across different c-tasks. We capture new reliability gaps by checking our requirements on 13 widely used object detection MVCs, and, for the first time, benchmark segmentation MVCs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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