While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides,
they mostly fall prey to the limitation of only input-side multimodal understanding, without the …

In the recent shift towards human-centric AI, the need for machines to accurately use natural
language has become increasingly important. While a common approach to achieve this is …

Recent advances in vision-language models have shown notable generalization in broad
tasks through visual instruction tuning. However, bridging the gap between the pre-trained …

Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in
processing vision-language tasks. One of the crux of MLLMs lies in vision tokenization …

The task of image captioning demands an algorithm to generate natural language
descriptions of visual inputs. Recent advancements have seen a convergence between …

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 …

This work tackles the persistent challenge of image-text retrieval, a key problem at the
intersection of computer vision and natural language processing. Despite significant …

Image captioning systems are unable to generate fine-grained captions as they are trained
on data that is either noisy (alt-text) or generic (human annotations). This is further …

Fine-tuning image captioning models with hand-crafted rewards like the CIDEr metric has
been a classical strategy for promoting caption quality at the sequence level. This approach …

The task of image captioning has recently been gaining popularity, and with it the complex
task of evaluating the quality of image captioning models. In this work, we present the first …