Transformer-based trackers have achieved high accuracy on standard benchmarks.

Visual question answering ( VQA) is a challenging task that requires an in-depth understanding of vision and language, as well as multi-modal reasoning.

Preference data is crucial for aligning generative models with text prompts.

In other words, the word order may undergo changes as a result and leads to a low text similarity.

Recently, text-to-image models have been thriving. Despite their powerful generative capacity, our research has uncovered a lack of robustness in this generation process.

To date, Transformer-based frameworks have demonstrated impressive results in single-image super-resolution (SISR).