ReCap improves character consistency in story visualization by 2.63% on FlintstonesSV and 5.65% on PororoSV using a selective pronoun-based conditioning module and training-only semantic drift correction.
In: ICCV (2021) 4
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Mixing 3-10% of visually grounded self-supervised instructions into visual instruction tuning consistently boosts MLLM performance on vision-centric benchmarks.
citing papers explorer
-
ReCap: Lightweight Referential Grounding for Coherent Story Visualization
ReCap improves character consistency in story visualization by 2.63% on FlintstonesSV and 5.65% on PororoSV using a selective pronoun-based conditioning module and training-only semantic drift correction.
-
Boosting Visual Instruction Tuning with Self-Supervised Guidance
Mixing 3-10% of visually grounded self-supervised instructions into visual instruction tuning consistently boosts MLLM performance on vision-centric benchmarks.