Connecting Vision and Language plays an essential role in Generative Intelligence. For this
reason, large research efforts have been devoted to image captioning, ie describing images …

Image captioning refers to automatic generation of descriptive texts according to the visual
content of images. It is a technique integrating multiple disciplines including the computer …

The CLIP model has been recently proven to be very effective for a variety of cross-modal
tasks, including the evaluation of captions generated from vision-and-language …

Tremendous progress has been made in recent years in develo** better image captioning
models, yet most of them rely on a separate object detector to extract regional features …

Effectively aligning with human judgment when evaluating machine-generated image
captions represents a complex yet intriguing challenge. Existing evaluation metrics like …

Current metrics for video captioning are mostly based on the text-level comparison between
reference and candidate captions. However, they have some insuperable drawbacks, eg …

State-of-The-Art (SoTA) image captioning models often rely on the Microsoft COCO (MS-
COCO) dataset for training. This dataset contains annotations provided by human …

Automatic image captioning evaluation is critical for benchmarking and promoting advances
in image captioning research. Existing metrics only provide a single score to measure …

Despite significant advancements in caption generation, existing evaluation metrics often fail
to capture the full quality or fine-grained details of captions. This is mainly due to their …

Generative intelligence relies heavily on the integration of vision and language. Much of the
research has focused on image captioning, which involves describing images with …