GFF: Gated Fully Fusion for Semantic Segmentation

Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel. High-level features from Deep Convolutional Neural Networks already demonstrate their effectiveness in semantic segmentation tasks, however the coarse resolution of high-level features often leads to inferior results for small/thin objects where detailed information is important. It is natural to consider importing low level features to compensate for the lost detailed information in high-level features.Unfortunately, simply combining multi-level features suffers from the semantic gap among them. In this paper, we propose a new architecture, named Gated Fully Fusion (GFF), to selectively fuse features from multiple levels using gates in a fully connected way. Specifically, features at each level are enhanced by higher-level features with stronger semantics and lower-level features with more details, and gates are used to control the propagation of useful information which significantly reduces the noises during fusion. We achieve the state of the art results on four challenging scene parsing datasets including Cityscapes, Pascal Context, COCO-stuff and ADE20K.Comment: accepted by AAAI-2020(oral

TBPLaS: a Tight-Binding Package for Large-scale Simulation

TBPLaS is an open-source software package for the accurate simulation of physical systems with arbitrary geometry and dimensionality utilizing the tight-binding (TB) theory. It has an intuitive object-oriented Python application interface (API) and Cython/Fortran extensions for the performance critical parts, ensuring both flexibility and efficiency. Under the hood, numerical calculations are mainly performed by both exact diagonalizatin and the tight-binding propagation method (TBPM) without diagonalization. Especially, the TBPM is based on the numerical solution of time-dependent Schr\"odinger equation, achieving linear scaling with system size in both memory and CPU costs. Consequently, TBPLaS provides a numerically cheap approach to calculate the electronic, transport and optical properties of large tight-binding models with billions of atomic orbitals. Current capabilities of TBPLaS include the calculation of band structure, density of states, local density of states, quasi-eigenstates, optical conductivity, electrical conductivity, Hall conductivity, polarization function, dielectric function, plasmon dispersion, carrier mobility and velocity, localization length and free path, Z2 topological invariant, wave-packet propagation, etc. All the properties can be obtained with only a few lines of code. Other algorithms involving tight-binding Hamiltonians can be implemented easily thanks to its extensible and modular nature. In this paper, we discuss the theoretical framework, implementation details and common workflow of TBPLaS, and give a few demonstrations of its applications.Comment: 54 pages, 16 figure

SCFSAP controls organ size by targeting PPD proteins for degradation in Arabidopsis thaliana

Control of organ size by cell proliferation and growth is a fundamental process, but the mechanisms that determine the final size of organs are largely elusive in plants. We have previously revealed that the ubiquitin receptor DA1 regulates organ size by repressing cell proliferation in Arabidopsis. Here we report that a mutant allele of STERILE APETALA (SAP) suppresses the da1-1 mutant phenotype. We show that SAP is an F-box protein that forms part of a SKP1/Cullin/F-box E3 ubiquitin ligase complex and controls organ size by promoting the proliferation of meristemoid cells. Genetic analyses suggest that SAP may act in the same pathway with PEAPOD1 and PEAPOD2, which are negative regulators of meristemoid proliferation, to control organ size, but does so independently of DA1. Further results reveal that SAP physically associates with PEAPOD1 and PEAPOD2, and targets them for degradation. These findings define a molecular mechanism by which SAP and PEAPOD control organ size

Towards Robust Referring Image Segmentation

Referring Image Segmentation (RIS) aims to connect image and language via outputting the corresponding object masks given a text description, which is a fundamental vision-language task. Despite lots of works that have achieved considerable progress for RIS, in this work, we explore an essential question, "what if the description is wrong or misleading of the text description?". We term such a sentence as a negative sentence. However, we find that existing works cannot handle such settings. To this end, we propose a novel formulation of RIS, named Robust Referring Image Segmentation (R-RIS). It considers the negative sentence inputs besides the regularly given text inputs. We present three different datasets via augmenting the input negative sentences and a new metric to unify both input types. Furthermore, we design a new transformer-based model named RefSegformer, where we introduce a token-based vision and language fusion module. Such module can be easily extended to our R-RIS setting by adding extra blank tokens. Our proposed RefSegformer achieves the new state-of-the-art results on three regular RIS datasets and three R-RIS datasets, which serves as a new solid baseline for further research. The project page is at \url{https://lxtgh.github.io/project/robust_ref_seg/}.Comment: technical repor

Control of final seed and organ size by the DA1 gene family in Arabidopsis thaliana

Although the size of an organism is a defining feature, little is known about the mechanisms that set the final size of organs and whole organisms. Here we describe Arabidopsis DA1, encoding a predicted ubiquitin receptor, which sets final seed and organ size by restricting the period of cell proliferation. The mutant protein encoded by the da1-1 allele has a negative activity toward DA1 and a DA1-related (DAR) protein, and overexpression of a da1-1 cDNA dramatically increases seed and organ size of wild-type plants, identifying this small gene family as important regulators of seed and organ size in plants