Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C.V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Swi

黃瓜

Adversarial Learning for Visual Storytelling with Sense Group Partitionision and language research, the techniques for sequential vision-to-language are still far away from being perfect. Due to the limitation of maximum likelihood estimation on training, the majority of existing models encourage high resemblance to texts in the training database, which makes the descr

aspect

Laser Scar Detection in Fundus Images Using Convolutional Neural Networksbehind circular or irregular scars in the retina. Laser scar detection in fundus images is thus important for automated DR screening. Despite its importance, the problem is understudied in terms of both datasets and methods. This paper makes the first attempt to detect laser-scar images by deep lear

陳腐思想

傳染

精密

A Joint Local and Global Deep Metric Learning Method for Caricature Recognitioneen photographs and caricatures, making it nontrivial to match the features of photographs and caricatures. To address the problem, a joint local and global metric learning method (LGDML) is proposed. First, joint local and global feature representation is learnt with convolutional neural networks t

wreathe

Fast Single Shot Instance Segmentationg every individual instance (instance segmentation) in a flexible and fast way. In the pipeline of FSSI, the instance segmentation task is divided into three parallel sub-tasks: object detection, semantic segmentation, and direction prediction. The instance segmentation result is then generated from

absorbed

改進(jìn)

Symmetry-Aware Face Completion with Generative Adversarial Networksthout taking these characteristics into account, existing face completion techniques usually fail to produce a photo-realistic result, especially for the missing key components (e.g., eyes and mouths). In this paper, we propose a symmetry-aware face completion method based on facial structural featu

fringe

NIL

https://doi.org/10.1007/978-3-030-20870-7artificial intelligence; computer vision; databases; image coding; image processing; image reconstruction