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[HTML][HTML] Survey and performance analysis of deep learning based object detection in challenging environments
Recent progress in deep learning has led to accurate and efficient generic object detection
networks. Training of highly reliable models depends on large datasets with highly textured …
networks. Training of highly reliable models depends on large datasets with highly textured …
Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection
This study addresses the issue of fusing infrared and visible images that appear differently
for object detection. Aiming at generating an image of high visual quality, previous …
for object detection. Aiming at generating an image of high visual quality, previous …
U2-Net: Going deeper with nested U-structure for salient object detection
In this paper, we design a simple yet powerful deep network architecture, U 2-Net, for salient
object detection (SOD). The architecture of our U 2-Net is a two-level nested U-structure. The …
object detection (SOD). The architecture of our U 2-Net is a two-level nested U-structure. The …
Multi-scale interactive network for salient object detection
Deep-learning based salient object detection methods achieve great progress. However, the
variable scale and unknown category of salient objects are great challenges all the time …
variable scale and unknown category of salient objects are great challenges all the time …
Specificity-preserving RGB-D saliency detection
RGB-D saliency detection has attracted increasing attention, due to its effectiveness and the
fact that depth cues can now be conveniently captured. Existing works often focus on …
fact that depth cues can now be conveniently captured. Existing works often focus on …