Specifically, the framework consists of a Visual Representation Module to extract individual appearance features, a Knowledge Augmented Semantic Relation Module explore semantic representations of individual actions, and a Knowledge-Semantic-Visual Interaction Module aims to integrate visual and semantic information by the knowledge.
Specifically, we assign a set of arbitrarily chosen 3D keypoints to represent each unknown target 3D object and learn a network to detect their 2D projections that comply with the relative camera viewpoints.
The inaccessibility of reference videos with pristine quality and the complexity of authentic distortions pose great challenges for this kind of blind video quality assessment (BVQA) task.
Ranked #4 on Video Quality Assessment on MSU NR VQA Database
1 code implementation • 1 Nov 2020 • Jia-Hong Huang, Chao-Han Huck Yang, Fangyu Liu, Meng Tian, Yi-Chieh Liu, Ting-Wei Wu, I-Hung Lin, Kang Wang, Hiromasa Morikawa, Hernghua Chang, Jesper Tegner, Marcel Worring
To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset.
Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease which may result in loss of vision.
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists.
In this paper, we extend the ambient module to the hidden space of the generator, and provide the uniqueness condition and the corresponding strategy for the ambient hidden generator in the adversarial training process.
It has been demonstrated that deep neural networks are prone to noisy examples particular adversarial samples during inference process.