Attentive Gated Graph Neural Network for Image Scene Graph Generation
Why this work is in the frame
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Bibliographic record
Abstract
Image scene graph is a semantic structural representation which can not only show what objects are in the image, but also infer the relationships and interactions among them. Despite the recent success in object detection using deep neural networks, automatically recognizing social relations of objects in images remains a challenging task due to the significant gap between the domains of visual content and social relation. In this work, we translate the scene graph into an Attentive Gated Graph Neural Network which can propagate a message by visual relationship embedding. More specifically, nodes in gated neural networks can represent objects in the image, and edges can be regarded as relationships among objects. In this network, an attention mechanism is applied to measure the strength of the relationship between objects. It can increase the accuracy of object classification and reduce the complexity of relationship classification. Extensive experiments on the widely adopted Visual Genome Dataset show the effectiveness of the proposed method.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it