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pro vyhledávání: '"Baraldi, Lorenzo"'
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, however, is known to limit descriptiveness and semantic richness and
Externí odkaz:
http://arxiv.org/abs/2408.16827
The conventional training approach for image captioning involves pre-training a network using teacher forcing and subsequent fine-tuning with Self-Critical Sequence Training to maximize hand-crafted captioning metrics. However, when attempting to opt
Externí odkaz:
http://arxiv.org/abs/2408.14547
Smart autonomous agents are becoming increasingly important in various real-life applications, including robotics and autonomous vehicles. One crucial skill that these agents must possess is the ability to interact with their surrounding entities, su
Externí odkaz:
http://arxiv.org/abs/2408.04423
Discerning between authentic content and that generated by advanced AI methods has become increasingly challenging. While previous research primarily addresses the detection of fake faces, the identification of generated natural images has only recen
Externí odkaz:
http://arxiv.org/abs/2407.20337
Effectively aligning with human judgment when evaluating machine-generated image captions represents a complex yet intriguing challenge. Existing evaluation metrics like CIDEr or CLIP-Score fall short in this regard as they do not take into account t
Externí odkaz:
http://arxiv.org/abs/2407.20341
The objective of image captioning models is to bridge the gap between the visual and linguistic modalities by generating natural language descriptions that accurately reflect the content of input images. In recent years, researchers have leveraged de
Externí odkaz:
http://arxiv.org/abs/2405.13127
Autor:
Caffagni, Davide, Cocchi, Federico, Moratelli, Nicholas, Sarto, Sara, Cornia, Marcella, Baraldi, Lorenzo, Cucchiara, Rita
Multimodal LLMs are the natural evolution of LLMs, and enlarge their capabilities so as to work beyond the pure textual modality. As research is being carried out to design novel architectures and vision-and-language adapters, in this paper we concen
Externí odkaz:
http://arxiv.org/abs/2404.15406
In the last few years, the research interest in Vision-and-Language Navigation (VLN) has grown significantly. VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a
Externí odkaz:
http://arxiv.org/abs/2404.10054
Open-vocabulary semantic segmentation aims at segmenting arbitrary categories expressed in textual form. Previous works have trained over large amounts of image-caption pairs to enforce pixel-level multimodal alignments. However, captions provide glo
Externí odkaz:
http://arxiv.org/abs/2404.06542
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments. In the literature, there has been an extensive focus on object labeling and exhaustive scene graph generation; less ef
Externí odkaz:
http://arxiv.org/abs/2403.07076