ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation

We introduce ReConvNet, a recurrent convolutional architecture for semi-supervised video object segmentation that is able to fast adapt its features to focus on any specific object of interest at inference time. Generalization to new objects never observed during training is known to be a hard task for supervised approaches that would need to be retrained. To tackle this problem, we propose a more efficient solution that learns spatio-temporal features self-adapting to the object of interest via conditional affine transformations. This approach is simple, can be trained end-to-end and does not necessarily require extra training steps at inference time. Our method shows competitive results on DAVIS2016 with respect to state-of-the art approaches that use online fine-tuning, and outperforms them on DAVIS2017. ReConvNet shows also promising results on the DAVIS-Challenge 2018 winning the $10$-th position.Comment: CVPR Workshop - DAVIS Challenge 201

Multi-View Stereo with Single-View Semantic Mesh Refinement

While 3D reconstruction is a well-established and widely explored research topic, semantic 3D reconstruction has only recently witnessed an increasing share of attention from the Computer Vision community. Semantic annotations allow in fact to enforce strong class-dependent priors, as planarity for ground and walls, which can be exploited to refine the reconstruction often resulting in non-trivial performance improvements. State-of-the art methods propose volumetric approaches to fuse RGB image data with semantic labels; even if successful, they do not scale well and fail to output high resolution meshes. In this paper we propose a novel method to refine both the geometry and the semantic labeling of a given mesh. We refine the mesh geometry by applying a variational method that optimizes a composite energy made of a state-of-the-art pairwise photo-metric term and a single-view term that models the semantic consistency between the labels of the 3D mesh and those of the segmented images. We also update the semantic labeling through a novel Markov Random Field (MRF) formulation that, together with the classical data and smoothness terms, takes into account class-specific priors estimated directly from the annotated mesh. This is in contrast to state-of-the-art methods that are typically based on handcrafted or learned priors. We are the first, jointly with the very recent and seminal work of [M. Blaha et al arXiv:1706.08336, 2017], to propose the use of semantics inside a mesh refinement framework. Differently from [M. Blaha et al arXiv:1706.08336, 2017], which adopts a more classical pairwise comparison to estimate the flow of the mesh, we apply a single-view comparison between the semantically annotated image and the current 3D mesh labels; this improves the robustness in case of noisy segmentations.Comment: {\pounds}D Reconstruction Meets Semantic, ICCV worksho

Cosmic evolution of metal densities: the enrichment of the Inter-Galactic Medium

By means of chemo-photometric models for galaxies of different morhological types, we have carried out a detailed study of the history of element production by spheroidal and dwarf irregular galaxies. Spheroidal galaxies suffer a strong and intense star formation episode at early times. In dwarf irregulars, the SFR proceeds at a low regime but continuously. Both galactic types enrich the IGM with metals, by means of galactic winds. We have assumed that the galaxy number density is fixed and normalized to the value of the optical luminosity function observed in the local universe. Our models allow us to investigate in detail how the metal fractions locked up in spheroid and dwarf irregular stars, in the ISM and ejected into the IGM have changed with cosmic time. By relaxing the instantaneous recycling approximation and taking into account stellar lifetimes, for the first time we have studied the evolution of the chemical abundance ratios in the IGM and compared our predictions with a set of observations by various authors. Our results indicate that the bulk of the IGM enrichment is due to spheroids, with dwarf irregular galaxies playing a negligible role. Our predictions grossly account for the [O/H] observed in the IGM at high redshift, but overestimate the [C/H]. Furthermore, it appears hard to reproduce the abundance ratios observed in the high-redshift IGM. Some possible explanations are discussed in the text. This is the first attempt to study the abundance ratios in the IGM by means of detailed chemical evolution models which take into account the stellar lifetimes. Numerical simulations adopting our chemical evolution prescriptions could be useful to improve our understanding of the IGM chemical enrichment.Comment: 18 pages, 8 figures, MNRAS, accepted for publication. Minor changes after referee repor

The Star Formation History of the GRB 050730 Host Galaxy

The long GRB 050730 observed at redshift z ~ 4 allowed the determination of the elemental abundances for a set of different chemical elements. We use detailed chemical evolution models taking into account also dust production to constrain the star formation history of the host galaxy of this long GRB. For the host galaxy of GRB 050730, we derive also some dust-related quantities and the the specific star formation rate, namely the star formation rate per unit stellar mass. We copare the properties of the GRB host galaxy with the ones of Quasar Damped Lyman Alpha absorbers.Comment: 7 pages, talk presented at the conference "Low-Metallicity Star Formation: From the First Stars to Dwarf Galaxies" held in Rapallo, Italy, June 200