Journal of Visual Communication and Image Representation

By Elsevier Sdol. 2009

208 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning to Simulate Complex Physics with Graph Networks (2020, methodology): “We chose MPM for the simulator because it can simulate a very wide range of materials, and also has some different properties than PBD and SPH, e.g., particles may become compressed over time.”
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models (2021, unknown): “Over the years, single image super-resolution (SISR) has drawn active attention due to its wide applications in computer vision such as object recognition [Fookes et al., 2012; Sajjadi et al., 2017], remote sensing [Li et al.”
A Survey of Sparse Representation: Algorithms and Applications (2015, methodology): “[157] first explicitly analyzed the rationales of the sparse representation theory in performing the super-reso lution task, and proposed to exploit the L2-Boosting strategy to learn coupled dictionaries, which were employed to constru ct sparse coding space.”
Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation (2014, methodology): “[17] initially processed the skele ton data from sensor coordinate to torso PCA frame in order to gain robust and invariant pose representation.”
Saliency Detection by Multitask Sparsity Pursuit (2012, background): “Saliency detection is rel ated to many applications, such as automatic image cropping [5], im age thumbnailing [6], image/video compressing [7] and imag e collection browsing [8].”