Explore our research publications: papers, articles, and conference proceedings from AImageLab.
Authors: Cucchiara, R.; Del Bimbo, A.; Sclaroff, S.
Published in: LECTURE NOTES IN COMPUTER SCIENCE
Authors: Cucchiara, R.; Del Bimbo, A.; Sclaroff, S.
Published in: LECTURE NOTES IN COMPUTER SCIENCE
Authors: Cucchiara, R.; Del Bimbo, A.; Sclaroff, S.
Published in: LECTURE NOTES IN COMPUTER SCIENCE
Authors: Cucchiara, R.; Del Bimbo, A.; Sclaroff, S.
Published in: LECTURE NOTES IN COMPUTER SCIENCE
Authors: Cucchiara, R.; Bimbo, A. D.; Sclaroff, S.
Published in: LECTURE NOTES IN COMPUTER SCIENCE
Authors: Cucchiara, R.; Del Bimbo, A.; Sclaroff, S.
Published in: LECTURE NOTES IN COMPUTER SCIENCE
Authors: Cucchiara, R.; Bimbo, A. D.; Sclaroff, S.
Published in: LECTURE NOTES IN COMPUTER SCIENCE
Authors: Simoni, Alessandro; Bergamini, Luca; Palazzi, Andrea; Calderara, Simone; Cucchiara, Rita
Published in: INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION
In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene. Despite recent advances, generating the entire scene in an end-to-end fashion is still far from being achieved. Instead, here we follow a two stages approach, where interpretable information is included in the loop and each actor is modelled independently. We leverage a per-object novel view synthesis paradigm; i.e. generating a synthetic representation of an object undergoing a geometrical roto-translation in the 3D space. Our model can be easily conditioned with constraints (e.g. input trajectories) provided by state-of-the-art tracking methods or by the user itself. This allows us to generate a set of diverse realistic futures starting from the same input in a multi-modal fashion. We visually and quantitatively show the superiority of this approach over traditional end-to-end scene-generation methods on CityFlow, a challenging real world dataset.
Authors: Amoroso, Roberto; Baraldi, Lorenzo; Cucchiara, Rita
Published in: LECTURE NOTES IN COMPUTER SCIENCE
While most of the recent literature on semantic segmentation has focused on outdoor scenarios, the generation of accurate indoor segmentation maps has been partially under-investigated, although being a relevant task with applications in augmented reality, image retrieval, and personalized robotics. With the goal of increasing the accuracy of semantic segmentation in indoor scenarios, we develop and propose two novel boundary-level training objectives, which foster the generation of accurate boundaries between different semantic classes. In particular, we take inspiration from the Boundary and Active Boundary losses, two recent proposals which deal with the prediction of semantic boundaries, and propose modified geometric distance functions that improve predictions at the boundary level. Through experiments on the NYUDv2 dataset, we assess the appropriateness of our proposal in terms of accuracy and quality of boundary prediction and demonstrate its accuracy gain.
Authors: Cucchiara, Rita