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 …

Explaining an image with missing or non-existent objects is known as object bias
(hallucination) in image captioning. This behaviour is quite common in the state-of-the-art …

Recently, the state-of-the-art models for image captioning have overtaken human
performance based on the most popular metrics, such as BLEU, METEOR, ROUGE and …

A wide range of image captioning models has been developed, achieving significant
improvement based on popular metrics, such as BLEU, CIDEr, and SPICE. However …

Image captioning models typically follow an encoder-decoder architecture which uses
abstract image feature vectors as input to the encoder. One of the most successful …

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

The advent of vision-language pre-training techniques enhanced substantial progress in the
development of models for image captioning. However, these models frequently produce …

The contemporary visual captioning models frequently hallucinate objects that are not
actually in a scene, due to the visual misclassification or over-reliance on priors that …

Image captioning models have achieved impressive results on datasets containing limited
visual concepts and large amounts of paired image-caption training data. However, if these …

Modern image captioning models are usually trained with text similarity objectives. However,
since reference captions in public datasets often describe the most salient common objects …