Zobrazeno 1 - 10
of 14 605
pro vyhledávání: '"A. Baraldi"'
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-4-W1-2022, Pp 503-510 (2022)
Earth observation (EO) data cubes have revolutionized the way large volumes of EO data can be stored, accessed, and processed. However, users coming from application domains outside of traditional EO research still face some significant technical bar
Externí odkaz:
https://doaj.org/article/d4ad8d58c24e4ce8833c97e803f4a4f4
Autor:
M. Vaccari, M. Cremona, D. Orsi, V. Lorusso, A. Baraldi, A. Bosio, D. Pontiroli, L. Liggieri, F. Ravera, E. Santini, L. Cristofolini
Publikováno v:
Catalysis Communications, Vol 171, Iss , Pp 106527- (2022)
Photocatalytic oxidation (PCO) could provide energy-efficient purification of water and air. Its efficacy is constrained mainly by limited photocatalytic activity and active surface. To address both, solid foams with hierarchic porous structures span
Externí odkaz:
https://doaj.org/article/b06653a449514c94b5dd1b0e90ea47b6
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