TransInpaint: Transformer-based Image Inpainting with Context Adaptation
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
Image inpainting aims to generate realistic content for missing regions of an image. Existing methods often struggle to produce visually coherent content for missing regions of an image, which results in blurry or distorted structures around the damaged areas. These methods rely on surrounding texture information and have difficulty in generating content that harmonizes well with the broader context of the image. To address this limitation, we propose a novel model that generates plausible content for missing regions while ensuring that the generated content is consistent with the overall context of the original image. In particular, we introduce a novel context-adaptive transformer for image inpainting (TransInpaint) that relies on the visible content and the position of the missing regions. Additionally, we design a texture enhancement network that combines skip features from the encoder with the coarse features produced by the generator, yielding a more comprehensive and robust representation of image content. Based on extensive evaluations on challenging datasets, our proposed TransInpaint outperforms the cutting-edge generative models for image inpainting in terms of quality, textures, and structures.
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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 | 0.001 |
| Open science | 0.000 | 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