Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

Automatic image description, also known as image captioning, aims to describe the
elements included in an image and their relationships. This task involves two research …

It is well believed that the higher uncertainty in a word of the caption, the more inter-
correlated context information is required to determine it. However, current image captioning …

The ability to judge whether a caption correctly describes an image is a critical part of vision-
language understanding. However, state-of-the-art models often misinterpret the correctness …

Recent advancements in single-stage Panoptic Narrative Grounding (PNG) have
demonstrated significant potential. These methods predict pixel-level masks by directly …

Transformer-based approaches to image captioning have shown great success by utilizing
long-term dependency for visual embedding. However, their coarse long-term dependency …

Having the difficulty of solving the semantic gap between images and texts for the image
captioning task, conventional studies in this area paid some attention to treating semantic …

Image captioning requires not only accurate recognition of objects and corresponding
relationships, but also full comprehension of the scene information. However, existing …

Image Captioning is an important Language and Vision task that finds application in a
variety of contexts, ranging from healthcare to autonomous vehicles. As many real-world …

Translation is necessary for the spreading new information, knowledge and ideas across the
world. Communication is very important to communicate between two countries or between …