An Adaptive Descriptor Design for Object Recognition in the Wild
Bibliographic record
Abstract
Digital images nowadays show large appearance vari-abilities on picture styles, in terms of color tone, contrast, vignetting, and etc. These ‘picture styles ’ are directly re-lated to the scene radiance, image pipeline of the camera, and post processing functions (e.g., photography effect fil-ters). Due to the complexity and nonlinearity of these fac-tors, popular gradient-based image descriptors generally are not invariant to different picture styles, which could de-grade the performance for object recognition. Given that images shared online or created by individual users are taken with a wide range of devices and may be processed by various post processing functions, to find a robust ob-ject recognition system is useful and challenging. In this paper, we investigate the influence of picture styles on ob-ject recognition by making a connection between image de-scriptors and a pixel mapping function g, and accordingly propose an adaptive approach based on a g-incorporated kernel descriptor and multiple kernel learning, without es-timating or specifying the image styles used in training and testing. We conduct experiments on the Domain Adaptation data set, the Oxford Flower data set, and several variants of the Flower data set by introducing popular photography effects through post-processing. The results demonstrate that the proposedmethod consistently yields recognition im-provements over standard descriptors in all studied cases. 1.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".