Text-guided image generation based on ternary attention mechanism generative adversarial network
Bibliographic record
Abstract
Synthesizing high-quality photorealistic images from textual descriptions is a challenging mission. Existing text-to-image generative adversarial networks typically use a stacking structure as the core network, but still have drawbacks: the model network structure becomes increasingly complex and large, and the generated images are not natural and clear enough, looking like a simple combination of rough shapes and trace detail features, lacking fine-grained information, visual realism, and diversity to be further enhanced. To this end, we propose a simple and effective model for text-to-image synthesis - a ternary attention-based generative adversarial network, which uses a pair of generators and discriminators as the underlying structure, with the generators combining triple attention mechanisms to fuse fine-grained image features, and the discriminators combining match-aware gradient penalties and one-way output mechanisms for game training. Our proposed method effectively synthesizes real and text-matched images and achieves better performance on the widely used CUB and COCO datasets compared to the current state-of-the-art methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".