Attentional Push: A Deep Convolutional Network for Augmenting Image Salience with Shared Attention Modeling in Social Scenes
Bibliographic record
Abstract
We present a novel visual attention tracking technique based on Shared Attention modeling. By considering the viewer as a participant in the activity occurring in the scene, our model learns the loci of attention of the scene actors and use it to augment image salience. We go beyond image salience and instead of only computing the power of image regions to pull attention, we also consider the strength with which the scene actors push attention to the region in question, thus the term Attentional Push. We present a convolutional neural network (CNN) which augments standard saliency models with Attentional Push. Our model contains two pathways: an Attentional Push pathway which learns the gaze location of the scene actors and a saliency pathway. These are followed by a shallow augmented saliency CNN which combines them and generates the augmented saliency. For training, we use transfer learning to initialize and train the Attentional Push CNN by minimizing the classification error of following the actors gaze location on a 2-D grid using a large-scale gaze-following dataset. The Attentional Push CNN is then fine-tuned along with the augmented saliency CNN to minimize the Euclidean distance between the augmented saliency and ground truth fixations using an eye-tracking dataset, annotated with the head and the gaze location of the scene actors. We evaluate our model on three challenging eye fixation datasets, SALICON, iSUN and CAT2000, and illustrate significant improvements in predicting viewers fixations in social scenes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".