Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths
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
Human eye movements provide a rich source of information into the human vi-sual information processing. The complex interplay between the task and the visual stimulus is believed to determine human eye movements, yet it is not fully understood, making it difficult to develop reliable eye movement prediction sys-tems. Our work makes three contributions towards addressing this problem. First, we complement one of the largest and most challenging static computer vision datasets, VOC 2012 Actions, with human eye movement recordings collected un-der the primary task constraint of action recognition, as well as, separately, for context recognition, in order to analyze the impact of different tasks. Our dataset is unique among the eyetracking datasets of still images in terms of large scale (over 1 million fixations recorded in 9157 images) and different task controls. Sec-ond, we propose Markov models to automatically discover areas of interest (AOI) and introduce novel sequential consistency metrics based on them. Our methods can automatically determine the number, the spatial support and the transitions between AOIs, in addition to their locations. Based on such encodings, we quan-titatively show that given unconstrained read-world stimuli, task instructions have significant influence on the human visual search patterns and are stable across subjects. Finally, we leverage powerful machine learning techniques and com-puter vision features in order to learn task-sensitive reward functions from eye movement data within models that allow to effectively predict the human visual search patterns based on inverse optimal control. The methodology achieves state of the art scanpath modeling results. 1
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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