Recently, various text-to-image generative models have been released, demonstrating their ability to generate high-quality synthesized images from text prompts. Despite these advancements, determining the appropriate text prompts to obtain desired images remains challenging. The quality of the synthesized images heavily depends on the user input, making it difficult to achieve consistent and satisfactory results. This limitation has sparked the need for an effective prompt optimization method to generate optimized text prompts automatically for text-to-image generative models. Thus, this study proposes a prompt optimization method that uses in-context few-shot learning in a pretrained language model. The proposed approach aims to generate optimized text prompts to guide the image synthesis process by leveraging the available contextual information in a few text examples. The results revealed that synthesized images using the proposed prompt optimization method achieved a higher performance, at 18% on average, based on an evaluation metric that measures the similarity between the generated images and prompts for generation. The significance of this research lies in its potential to provide a more efficient and automated approach to obtaining high-quality synthesized images. The findings indicate that prompt optimization may offer a promising pathway for text-to-image generative models.

Short-horizon collision–risk warning can use instantaneous assessment, forecast-then-assess, or direct prediction from encounter histories. We evaluate the third route with the Anticipatory TRAffic-context Collision–risk Transformer (ATRACT), which estimates the maximum near-future fuzzy Collision Risk Index (CRI) directly from own-ship kinematic and collision-geometry histories without trajectory rollout. Experiments use ten days of Automatic Identification System (AIS) data from the Danish straits, comprising 1.63 million decision windows. Using a vessel-day-track partition and five random initializations, ATRACT attains an area under the receiver operating characteristic curve (ROC-AUC) of $0.814\pm 0.003$, compared with $0.808\pm 0.006$ for the evaluated forecast-then-CRI baseline (VCRF) and 0.729 for instantaneous CRI. Thresholds fixed at a nominal 5% false-alarm rate (FAR) on disjoint calibration tracks yield test FARs of 4.98% and 5.38% for ATRACT and VCRF. Although seed-0 track-bootstrap intervals include zero at every evaluated offset, ATRACT shows 4.4–10.8 percentage points higher mean pre-onset detection across 0.5–4 min over five random initializations; VCRF detects more individual high-risk windows at this operating point. Four direct-risk encoders achieve a narrow ROC-AUC range of 0.813–0.819. These results support direct temporal interaction modeling as a viable route while limiting conclusions to the evaluated forecast-first implementations.