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Parallel learning: Overview and perspective for computational learning across Syn2Real and Sim2Real
Q Miao, Y Lv, M Huang, X Wang… - IEEE/CAA Journal of …, 2023 - ieeexplore.ieee.org
The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …
solve real-world problems, has attracted more and more attention from various domains by …
Robust monocular depth estimation under challenging conditions
S Gasperini, N Morbitzer, HJ Jung… - Proceedings of the …, 2023 - openaccess.thecvf.com
While state-of-the-art monocular depth estimation approaches achieve impressive results in
ideal settings, they are highly unreliable under challenging illumination and weather …
ideal settings, they are highly unreliable under challenging illumination and weather …
Low-light image and video enhancement: A comprehensive survey and beyond
This paper presents a comprehensive survey of low-light image and video enhancement,
addressing two primary challenges in the field. The first challenge is the prevalence of mixed …
addressing two primary challenges in the field. The first challenge is the prevalence of mixed …
Multitask aet with orthogonal tangent regularity for dark object detection
Dark environment becomes a challenge for computer vision algorithms owing to insufficient
photons and undesirable noises. Most of the existing studies tackle this by either targeting …
photons and undesirable noises. Most of the existing studies tackle this by either targeting …
2pcnet: Two-phase consistency training for day-to-night unsupervised domain adaptive object detection
M Kennerley, JG Wang… - Proceedings of the …, 2023 - openaccess.thecvf.com
Object detection at night is a challenging problem due to the absence of night image
annotations. Despite several domain adaptation methods, achieving high-precision results …
annotations. Despite several domain adaptation methods, achieving high-precision results …
Memory oriented transfer learning for semi-supervised image deraining
Deep learning based methods have shown dramatic improvements in image rain removal
by using large-scale paired data of synthetic datasets. However, due to the various …
by using large-scale paired data of synthetic datasets. However, due to the various …
Featenhancer: Enhancing hierarchical features for object detection and beyond under low-light vision
KA Hashmi, G Kallempudi… - Proceedings of the …, 2023 - openaccess.thecvf.com
Extracting useful visual cues for the downstream tasks is especially challenging under low-
light vision. Prior works create enhanced representations by either correlating visual quality …
light vision. Prior works create enhanced representations by either correlating visual quality …
Cat: Exploiting inter-class dynamics for domain adaptive object detection
M Kennerley, JG Wang… - Proceedings of the …, 2024 - openaccess.thecvf.com
Abstract Domain adaptive object detection aims to adapt detection models to domains
where annotated data is unavailable. Existing methods have been proposed to address the …
where annotated data is unavailable. Existing methods have been proposed to address the …
Dual-YOLO architecture from infrared and visible images for object detection
With the development of infrared detection technology and the improvement of military
remote sensing needs, infrared object detection networks with low false alarms and high …
remote sensing needs, infrared object detection networks with low false alarms and high …
Thermal infrared image colorization for nighttime driving scenes with top-down guided attention
Benefitting from insensitivity to light and high penetration of foggy environments, infrared
cameras are widely used for sensing in nighttime traffic scenes. However, the low contrast …
cameras are widely used for sensing in nighttime traffic scenes. However, the low contrast …