When Machines Outshine Humans in Object Recognition, Benchmarking Dilemma
Bibliographic record
Abstract
In the field of vision science, recent endeavours have aimed to assess the comparative performance of artificial neural network models against human vision. Methodologies often involve the utilization of benchmarks that intentionally perturb or disturb images, thereby measuring noise sensitivity to gain insights into important features for object recognition. Recent studies employing critical frequency band masking have unveiled a perspective, positing that neural networks strategically exploit a wider band and less stable frequency channel compared to the one-octave band of human vision. In this work, we extend the inquiry to encompass diverse modern computer vision models, it becomes apparent that a considerable number of recently developed models outperform human capabilities in the presence of frequency noise. This ascendancy is not merely attributable to conventional techniques such as input image data augmentation but also crucially stems from the proficient exploitation of semantic information within expansive datasets, coupled with rigorous model scaling. Conceiving semantic information from multimodal training as a variant of output augmentation, we posit that augmenting input images and labels holds the potential to improve artificial neural networks to go beyond human performance in the current benchmarks. These advantages establish the idea that these models can be complementary agents for humans, particularly in challenging conditions. Despite acknowledging this progress, we must recognize a limitation in computer vision benchmarks, as they do not comprehensively quantify human vision. Consequently, we emphasize the imperative for vision science-inspired datasets to measure the alignment between models and human 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 | 0.068 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.012 |
| 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".