An Impartial Transformer for Story Visualization
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Abstract (arXiv)
Story Visualization is an advanced task of computed vision that targets sequential image synthesis, where the generated samples need to be realistic, faithful to their conditioning and sequentially consistent. Our work proposes a novel architectural and training approach: the Impartial Transformer achieves both text-relevant plausible scenes and sequential consistency utilizing as few trainable parameters as possible. This enhancement is even able to handle synthesis of 'hard' samples with occluded objects, achieving improved evaluation metrics comparing to past approaches.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 20 of its 25 references that are in the map. First posted on arXiv 9 Jan 2023.
Builds on
20 of 25 references.
References · 20
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8 more
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Citing papers per year: 2023: 2; 2024: 1.
3 papers.