Stereoscopic Image Retargeting Based on Deep Convolutional Neural Network
Bibliographic record
Abstract
Stereoscopic image retargeting aims at converting stereoscopic images to the target resolution adaptively. Different from 2D image retargeting, stereoscopic image retargeting needs to preserve both the shape structure of salient objects and depth consistency of 3D scenes. In this paper, we present a stereoscopic image retargeting method based on deep convolutional neural network to obtain high-quality retargeted images with both object shape preservation and scene depth preservation. First, a cross-attention extraction mechanism is constructed to generate attention map, which contains the valuable attention features of the left and right images and the common attention features between them. Second, since the disparity map can provide accurate depth information of objects in 3D scenes, a disparity-assisted 3D significance map generation module is utilized to further preserve the valuable depth information of stereoscopic images. Finally, in order to predict the retargeted stereoscopic images accurately, an image consistency loss is developed to preserve the geometric structure of salient objects, and a disparity consistency loss is introduced to eliminate depth distortions. Experimental results demonstrate that the proposed deep convolutional neural network can provide favorable stereoscopic image retargeting results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".