Recent advances in image captioning have focused on scaling the data and model size,
substantially increasing the cost of pre-training and finetuning. As an alternative to large …

Generating textual descriptions from visual inputs is a fundamental step towards machine
intelligence, as it entails modeling the connections between the visual and textual …

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 …

Image captioning, like many tasks involving vision and language, currently relies on
Transformer-based architectures for extracting the semantics in an image and translating it …

Image captioning models aim at connecting Vision and Language by providing natural
language descriptions of input images. In the past few years, the task has been tackled by …

Inspired by retrieval-augmented language generation and pretrained Vision and Language
(V&L) encoders, we present a new approach to image captioning that generates sentences …

Neural captioners are typically trained to mimic human-generated references without
optimizing for any specific communication goal, leading to problems such as the generation …

Research related to fashion and e-commerce domains is gaining attention in computer
vision and multimedia communities. Following this trend, this article tackles the task of …

While recent low-light image enhancement (LLIE) methods have made significant
advancements, they still face challenges in terms of low visual quality and weak …

Remote sensing images contain a wealth of Earth-observation information. Efficient
extraction and application of hidden knowledge from these images will greatly promote the …