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

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allergen

Evocative

3D Pick & Mix: Object Part Blending in Joint Shape and Image Manifoldses such as . our new approach can formulate advanced and semantically meaningful search queries such as: .. Many applications could benefit from such rich queries, users could browse through catalogues of furniture and . and . parts, combining for example the legs of a chair from one shop and the armrests from another shop.

CLAIM

Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translationsistency and better stability. Extensive experiments on six publicly available datasets with different scenarios, ., architectural buildings, seasons, landscape and human faces, demonstrate that the proposed G.GAN achieves superior model capacity and better generation performance comparing with exis

Aprope

Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneouslye can address both the generation and editing problem by training the proposed GANs, namely Editable GAN. For qualitative and quantitative evaluations, the proposed GANs outperform recent algorithms addressing the same problem. Also, we show that our model can achieve the competitive performance wit

Capture

Answer Distillation for Visual Question Answeringion architecture. The results show that our method can effectively compress the answer space and improve the accuracy on open-ended task, providing a new state-of-the-art performance on COCO-VQA dataset.

發(fā)酵劑

Spiral-Net with F1-Based Optimization for Image-Based Crack Detectionn effective optimization method to train the network. The proposed network is extended from U-Net to extract more detailed visual features, and the optimization method is formulated based on F1 score (F-measure) for properly learning the network even on the highly imbalanced training samples. The ex

獨(dú)行者

Minutiae-Based Gender Estimation for Full and Partial Fingerprints of Arbitrary Size and Shape obtain an enhanced gender decision. Unlike classical solutions this allows to deal with unconstrained fingerprint parts of arbitrary size and shape. We performed investigations on a publicly available database and our proposed solution proved to significantly outperform state-of-the-art approaches

aphasia

Progressive Feature Fusion Network for Realistic Image Dehazingpared with popular state-of-the-art methods. With efficient GPU memory usage, it can satisfactorily recover ultra high definition hazed image up?to 4K resolution, which is unaffordable by many deep learning based dehazing algorithms.

Connotation