Publications

Fast and accurate upper-body and head pose estimation is a key task for automatic monitoring of driver attention, a challenging context characterized by severe illumination changes, occlusions and extreme poses. In this work, we present a new deep learning framework for head localization and pose estimation on depth images. The core of the proposal is a regression neural network, called POSEidon, which is composed of three independent convolutional nets followed by a fusion layer, specially conceived for understanding the pose by depth. In addition, to recover the intrinsic value of face appearance for understanding head position and orientation, we propose a new Face-from-Depth approach for learning image faces from depth. Results in face reconstruction are qualitatively impressive. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Results show that our method overcomes all recent state-of-art works, running in real time at more than 30 frames per second.

In this paper, we propose a novel scene detection algorithm which employs semantic, visual, textual and audio cues. We also show how the hierarchical decomposition of the storytelling video structure can improve retrieval results presentation with semantically and aesthetically effective thumbnails. Our method is built upon two advancements of the state of the art: 1) semantic feature extraction which builds video specific concept detectors; 2) multimodal feature embedding learning, that maps the feature vector of a shot to a space in which the Euclidean distance has task specific semantic properties. The proposed method is able to decompose the video in annotated temporal segments which allow for a query specific thumbnail extraction. Extensive experiments are performed on different data sets to demonstrate the effectiveness of our algorithm. An in-depth discussion on how to deal with the subjectivity of the task is conducted and a strategy to overcome the problem is suggested.

The current Cultural Heritage management system lacks of ICT platforms for the management and integration of heterogeneous and fragmented data sources and interconnection between private and public subjects involved in the process. The SACHER project intends to fill this gap, working both on a technological level and on a business model level: firstly providing a platform based on an open-source distributed cloud-computing environment for the management of the complete data lifecycle related to cultural assets; moreover providing new models based on participatory design for Cultural Heritage data directed towards social entrepreneurship. This paper presents the first implementation of a system for managing data based on the 3D model of the cultural object, with a focus on the process for cultural assets management and the interface design for cultural services.

In this paper we present a segmentation proposal method which employs a box-hypotheses generation step followed by a lightweight segmentation strategy. Inspired by interactive segmentation, for each automatically placed bounding-box we compute a precise segmentation mask. We introduce diversity in segmentation strategies enhancing a generic model performance exploiting class-independent regional appearance features. Foreground probability scores are learned from groups of objects with peculiar characteristics to specialize segmentation models. We demonstrate results comparable to the state-of-the-art on PASCAL VOC 2012 and a further improvement by merging our proposals with those of a recent solution. The ability to generalize to unseen object categories is demonstrated on Microsoft COCO 2014.

Stromal content heavily impacts the transcriptional classification of colorectal cancer (CRC), with clinical and biological implications. Lineage-dependent stromal transcriptional components could therefore dominate over more subtle expression traits inherent to cancer cells. Since in patient-derived xenografts (PDXs) stromal cells of the human tumour are substituted by murine counterparts, here we deploy human-specific expression profiling of CRC PDXs to assess cancer-cell intrinsic transcriptional features. Through this approach, we identify five CRC intrinsic subtypes (CRIS) endowed with distinctive molecular, functional and phenotypic peculiarities: (i) CRIS-A: mucinous, glycolytic, enriched for microsatellite instability or KRAS mutations; (ii) CRIS-B: TGF-β pathway activity, epithelial–mesenchymal transition, poor prognosis; (iii) CRIS-C: elevated EGFR signalling, sensitivity to EGFR inhibitors; (iv) CRIS-D: WNT activation, IGF2 gene overexpression and amplification; and (v) CRIS-E: Paneth cell-like phenotype, TP53 mutations. CRIS subtypes successfully categorize independent sets of primary and metastatic CRCs, with limited overlap on existing transcriptional classes and unprecedented predictive and prognostic performances.