1,647 research outputs found
Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
This paper presents a new method for shadow removal using unpaired data,
enabling us to avoid tedious annotations and obtain more diverse training
samples. However, directly employing adversarial learning and cycle-consistency
constraints is insufficient to learn the underlying relationship between the
shadow and shadow-free domains, since the mapping between shadow and
shadow-free images is not simply one-to-one. To address the problem, we
formulate Mask-ShadowGAN, a new deep framework that automatically learns to
produce a shadow mask from the input shadow image and then takes the mask to
guide the shadow generation via re-formulated cycle-consistency constraints.
Particularly, the framework simultaneously learns to produce shadow masks and
learns to remove shadows, to maximize the overall performance. Also, we
prepared an unpaired dataset for shadow removal and demonstrated the
effectiveness of Mask-ShadowGAN on various experiments, even it was trained on
unpaired data.Comment: Accepted to ICCV 201
Thermal-Infrared Remote Target Detection System for Maritime Rescue based on Data Augmentation with 3D Synthetic Data
This paper proposes a thermal-infrared (TIR) remote target detection system
for maritime rescue using deep learning and data augmentation. We established a
self-collected TIR dataset consisting of multiple scenes imitating human rescue
situations using a TIR camera (FLIR). Additionally, to address dataset scarcity
and improve model robustness, a synthetic dataset from a 3D game (ARMA3) to
augment the data is further collected. However, a significant domain gap exists
between synthetic TIR and real TIR images. Hence, a proper domain adaptation
algorithm is essential to overcome the gap. Therefore, we suggest a domain
adaptation algorithm in a target-background separated manner from 3D
game-to-real, based on a generative model, to address this issue. Furthermore,
a segmentation network with fixed-weight kernels at the head is proposed to
improve the signal-to-noise ratio (SNR) and provide weak attention, as remote
TIR targets inherently suffer from unclear boundaries. Experiment results
reveal that the network trained on augmented data consisting of translated
synthetic and real TIR data outperforms that trained on only real TIR data by a
large margin. Furthermore, the proposed segmentation model surpasses the
performance of state-of-the-art segmentation methods.Comment: 12 page
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