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Found 9 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.
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.
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.
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.
