Dual Branch Multi-Level Semantic Learning for Few-Shot Segmentation.

Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Although progress has been made recently by combining prototype-based metric learning, existing methods still face two main challenges. First, various intra-class objects between the support and query images or semantically similar inter-class objects can seriously harm the segmentation performance due to their poor feature representations.

Efficient Binocular Rendering of Volumetric Density Fields With Coupled Adaptive Cube-Map Ray Marching for Virtual Reality.

Creating visualizations of multiple volumetric density fields is demanding in virtual reality (VR) applications, which often include divergent volumetric density distributions mixed with geometric models and physics-based simulations. Real-time rendering of such complex environments poses significant challenges for rendering quality and performance. This article presents a novel scheme for efficient real-time rendering of varying translucent volumetric density fields with global illumination (GI) effects on high-resolution binocular VR displays.

Learning Versatile Convolution Filters for Efficient Visual Recognition.

This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of efficient deep learning techniques running on cost-effective hardware, a number of methods have been developed to learn compact neural networks. Most of these works aim to slim down filters in different ways, e.

Adapted SIMPLE Algorithm for Incompressible SPH Fluids With a Broad Range Viscosity.

In simulating viscous incompressible SPH fluids, incompressibility and viscosity are typically solved in two separate stages. However, the interference between pressure and shear forces could cause the missing of behaviors that include preservation of sharp surface details and remarkable viscous behaviors such as buckling and rope coiling. To alleviate this problem, we introduce for the first time the semi-implicit method for pressure linked equations (SIMPLE) into SPH to solve incompressible fluids with a broad range viscosity.

Squeeze-and-Excitation Networks.

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels.

Simulation of Textile Stains.

Modeling virtual textiles has long been an appealing topic in computer graphics. To date, considerable effort has been devoted to their distinctive appearance and physically-based simulation. The apperance of staining patterns, commonly seen on textiles, has received comparatively little attention.

Projective Peridynamics for Modeling Versatile Elastoplastic Materials.

Unified simulation of versatile elastoplastic materials and different dimensions offers many advantages in animation production, contact handling, and hardware acceleration. The unstructured particle representation is particularly suitable for this task, thanks to its simplicity. However, previous meshless techniques either need too much computational cost for addressing stability issues, or lack physical meanings and fail to generate interesting deformation behaviors, such as the Poisson effect.

Temporal Coherence-Based Deblurring Using Non-Uniform Motion Optimization.

Non-uniform motion blur due to object movement or camera jitter is a common phenomenon in videos. However, the state-of-the-art video deblurring methods used to deal with this problem can introduce artifacts, and may sometimes fail to handle motion blur due to the movements of the object or the camera. In this paper, we propose a non-uniform motion model to deblur video frames.

Toward adaptive fusion of multiple cues for salient region detection.

Boundary priors have been extensively studied in salient region detection problems over the past few decades. Although several models based on the boundary prior have achieved good detection performance, there still exist drawbacks. The most common one is that they fail to detect the salient object when the background is complex or the salient object touches the image boundary.

Multiphase Interface Tracking with Fast Semi-Lagrangian Contouring.

We propose a semi-Lagrangian method for multiphase interface tracking. In contrast to previous methods, our method maintains an explicit polygonal mesh, which is reconstructed from an unsigned distance function and an indicator function, to track the interface of arbitrary number of phases. The surface mesh is reconstructed at each step using an efficient multiphase polygonization procedure with precomputed stencils while the distance and indicator function are updated with an accurate semi-Lagrangian path tracing from the meshes of the last step.

Multi-level discriminative dictionary learning with application to large scale image classification.

The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy.

Multiple ray cluster rendering for interactive integral imaging system.

In this paper, we present an efficient Computer Generated Integral Imaging (CGII) method, called multiple ray cluster rendering (MRCR). Based on the MRCR, an interactive integral imaging system is realized, which provides accurate 3D image satisfying the changeable observers' positions in real time. The MRCR method can generate all the elemental image pixels within only one rendering pass by ray reorganization of multiple ray clusters and 3D content duplication.

A Closed-Form Solution to Retinex with Nonlocal Texture Constraints.

We propose a method for intrinsic image decomposition based on retinex theory and texture analysis. While most previous methods approach this problem by analyzing local gradient properties, our technique additionally identifies distant pixels with the same reflectance through texture analysis, and uses these nonlocal reflectance constraints to significantly reduce ambiguity in decomposition. We formulate the decomposition problem as the minimization of a quadratic function which incorporates both the retinex constraint and our nonlocal texture constraint.

Using time-varying texels to simulate withering grassland.

This approach employs a hybrid texel-and-points scheme, allowing the volume models to handle time-varying simulations. The modeling of grass carries out physically based calculations on the point-based structure. These calculations express the geometric deformation of each grass blade while providing a basis for further transformation of the desired texel array.

Colorization using the rotation-invariant feature space.

Current colorization based on image segmentation makes it difficult to add or update color reliably and requires considerable user intervention A new approach gives similar colors to pixels with similar texture features. To do this, it uses rotation-invariant Gabor filter banks and applies optimization in the feature space.

Layer-based representation of polyhedrons for point containment tests.

This paper presents the layer-based representation of polyhedrons and its use for point-in-polyhedron tests. In the representation, the facets and edges of a polyhedron are sequentially arranged, and so, the binary search algorithm is efficiently used to speed up inclusion tests. In comparison with conventional representation for polyhedrons, the layer-based representation we propose greatly reduces the storage requirement because it represents much information implicitly, though it still has a storage complexity O(n).