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Found 21 papers, 2 papers with code
Weak Supervision with Arbitrary Single Frame for Micro- and Macro-expression Spotting
Frame-level micro- and macro-expression spotting methods require time-consuming frame-by-frame observation during annotation.
Nighttime Thermal Infrared Image Colorization with Feedback-based Object Appearance Learning
Extensive experiments illustrate that the proposed FoalGAN is not only effective for appearance learning of small objects, but also outperforms other image translation methods in terms of semantic preservation and edge consistency for the NTIR2DC task.
Weakly-supervised Micro- and Macro-expression Spotting Based on Multi-level Consistency
The inter-sample gap is primarily from the sample distribution and duration.
Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization
Nighttime thermal infrared (NTIR) image colorization, also known as translation of NTIR images into daytime color images (NTIR2DC), is a promising research direction to facilitate nighttime scene perception for humans and intelligent systems under unfavorable conditions (e. g., complete darkness).
Learning to Adapt to Light
Light adaptation or brightness correction is a key step in improving the contrast and visual appeal of an image.
Learning Crisp Boundaries Using Deep Refinement Network and Adaptive Weighting Loss
Significant progress has been made in boundary detection with the help of convolutional neural networks.
A Unified Structure for Efficient RGB and RGB-D Salient Object Detection
The proposed structure is simple yet effective; the rich context information of RGB and depth can be appropriately extracted and fused by the proposed structure efficiently.
Line Drawings of Natural Scenes Guide Visual Attention
In order to verify the specific roles of scene layout and regional cues in guiding visual attention, we executed a psychophysical experiment to record the human fixations on line drawings of natural scenes with an eye-tracking system in this work.
Improving Color Constancy by Discounting the Variation of Camera Spectral Sensitivity
Most color constancy (CC) models have been designed to first estimate the illuminant color, which is then removed from the color biased image to obtain an image taken under white light, without the explicit consideration of CSS effect on CC.
Clustering by Hierarchical Nearest Neighbor Descent (H-NND)
Due to some beautiful and effective features, this IT structure proves well suited for data clustering.
IT-Dendrogram: A New Member of the In-Tree (IT) Clustering Family
But if we can effectively map those IT structures into a visualized space in which the salient features of those undesired edges are preserved, then the undesired edges in the IT structures can still be visually determined in a visualization environment.
A general framework for the IT-based clustering methods
Previously, we proposed a physically inspired rule to organize the data points in a sparse yet effective structure, called the in-tree (IT) graph, which is able to capture a wide class of underlying cluster structures in the datasets, especially for the density-based datasets.
Efficient Illuminant Estimation for Color Constancy Using Grey Pixels
Illuminant estimation is a key step for computational color constancy.
Salient Structure Detection by Context-Guided Visual Search
We define the task of salient structure (SS) detection to unify the saliency-related tasks like fixation prediction, salient object detection, and other detection of structures of interest.
Nonparametric Nearest Neighbor Descent Clustering based on Delaunay Triangulation
In our physically inspired in-tree (IT) based clustering algorithm and the series after it, there is only one free parameter involved in computing the potential value of each point.
Clustering by Descending to the Nearest Neighbor in the Delaunay Graph Space
In our previous works, we proposed a physically-inspired rule to organize the data points into an in-tree (IT) structure, in which some undesired edges are allowed to occur.
IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering
Scientists in many fields have the common and basic need of dimensionality reduction: visualizing the underlying structure of the massive multivariate data in a low-dimensional space.
Clustering based on the In-tree Graph Structure and Affinity Propagation
A recently proposed clustering method, called the Nearest Descent (ND), can organize the whole dataset into a sparsely connected graph, called the In-tree.
An Effective Semi-supervised Divisive Clustering Algorithm
Nowadays, data are generated massively and rapidly from scientific fields as bioinformatics, neuroscience and astronomy to business and engineering fields.
Nearest Descent, In-Tree, and Clustering
In particular, the rule of ND works to select the nearest node in the descending direction of potential as the parent node of each node, which is in essence different from the classical Gradient Descent or Steepest Descent.
Efficient Color Boundary Detection with Color-Opponent Mechanisms
Color information plays an important role in better understanding of natural scenes by at least facilitating discriminating boundaries of objects or areas.
