12 citations · 15 across the 8 of their papers we have counts for
8 papers
Zero-Shot Temporal Action Localization Through Textual Guidance
Benedetta Liberatori, Alessandro Conti, Lorenzo Vaquero +3
Zero-shot temporal action localization (ZS-TAL) consists of classifying and localizing actions in untrimmed videos, where action classes are unseen at training time. Existing work…
ConViS-Bench: Estimating Video Similarity Through Semantic Concepts
Benedetta Liberatori, Alessandro Conti, Lorenzo Vaquero +3
What does it mean for two videos to be similar? Videos may appear similar when judged by the actions they depict, yet entirely different if evaluated based on the locations where t…
Automatic benchmarking of large multimodal models via iterative experiment programming
Alessandro Conti, Enrico Fini, Paolo Rota +3
Assessing the capabilities of large multimodal models (LMMs) often requires the creation of ad-hoc evaluations. Currently, building new benchmarks requires tremendous amounts of ma…
Vocabulary-free Image Classification and Semantic Segmentation
Alessandro Conti, Enrico Fini, Massimiliano Mancini +3
Large vision-language models revolutionized image classification and semantic segmentation paradigms. However, they typically assume a pre-defined set of categories, or vocabulary,…
Socially Pertinent Robots in Gerontological Healthcare
Xavier Alameda-Pineda, Angus Addlesee, Daniel Hernández García +41
Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation o…
AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation
Giacomo Zara, Subhankar Roy, Paolo Rota +1
Open-set Unsupervised Video Domain Adaptation (OUVDA) deals with the task of adapting an action recognition model from a labelled source domain to an unlabelled target domain that…
Simplifying Open-Set Video Domain Adaptation with Contrastive Learning
Giacomo Zara, Victor Guilherme Turrisi da Costa, Subhankar Roy +2
In an effort to reduce annotation costs in action recognition, unsupervised video domain adaptation methods have been proposed that aim to adapt a predictive model from a labelled…
MOA-2006-BLG-074: recognizing xallarap contaminants in planetary microlensing
P. Rota, Y. Hirao, V. Bozza +23
MOA-2006-BLG-074 was selected as one of the most promising planetary candidates in a retrospective analysis of the MOA collaboration: its asymmetric high-magnification peak can be…