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 …

In the last few years, a large number of automatic evaluation metrics have been proposed for
evaluating Natural Language Generation (NLG) systems. The rapid development and …

We propose BERTScore, an automatic evaluation metric for text generation. Analogously to
common metrics, BERTScore computes a similarity score for each token in the candidate …

A robust evaluation metric has a profound impact on the development of text generation
systems. A desirable metric compares system output against references based on their …

Abstract Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-
vocabulary language grounding and segmentation but can suffer under false premises …

Despite continuously improving performance, contemporary image captioning models are
prone to" hallucinating" objects that are not actually in a scene. One problem is that standard …

We present a novel large-scale dataset and accompanying machine learning models aimed
at providing a detailed understanding of the interplay between visual content, its emotional …

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 …

User trust has been identified as a critical issue that is pivotal to the success of autonomous
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …

In the rapidly evolving domain of Natural Language Generation (NLG) evaluation,
introducing Large Language Models (LLMs) has opened new avenues for assessing …