We introduce a new approach, Generative mOdel finetun-ing with Reward-driven Sample selection (GORS), to boost the compositional text-to-image generation abilities of pretrained text-to-image models.

One of the most successful algorithms uses feature vectors extracted from the region proposals obtained from anobject detector.

Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data.

In the zero-shot setting, the system does notrequire anytraining orfinetuning before applied toreal-worldscenarios, andthegoal category can be freely specified by text in an open-vocabulary manner.