Titlebook: Computer Vision – ECCV 2018; 15th European Confer Vittorio Ferrari,Martial Hebert,Yair Weiss Conference proceedings 2018 Springer Nature Sw

inculpate

中和

籠子

Deep Boosting for Image Denoisingion to derive a lightweight yet efficient convolutional network as the boosting unit, named Dilated Dense Fusion Network (DDFN). Comprehensive experiments demonstrate that our DBF outperforms existing methods on widely used benchmarks, in terms of different denoising tasks.

GOAT

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagesling and physically accurate 3D geometry. Extensive experiments show that our method not only qualitatively produces mesh model with better details, but also achieves higher 3D shape estimation accuracy compared to the state-of-the-art.

貞潔

Fighting Fake News: Image Splice Detection via Learned Self-Consistencyains state-of-the-art performance on several image forensics benchmarks, despite never seeing any manipulated images at training. That said, it is merely a step in the long quest for a truly general purpose visual forensics tool.

Ebct207

Depth-Aware CNN for RGB-D Segmentationany additional parameters, both operators can be easily integrated into existing CNNs. Extensive experiments and ablation studies on challenging RGB-D semantic segmentation benchmarks validate the effectiveness and flexibility of our approach.

和平

Integrating Egocentric Videos in Top-View Surveillance Videos: Joint Identification and Temporal Aliendent on the other two. We propose a unified framework to jointly solve all three problems. We evaluate the efficacy of the proposed approach on a publicly available dataset containing a variety of videos recorded in different scenarios.

landmark

Pastry

不再流行

A Study of Chaos in the Asteroid Beltf attention for image captioning. In particular, we highlight the complementary nature of the two types of attention and develop a model (Boosted Attention) to integrate them for image captioning. We validate the proposed approach with state-of-the-art performance across various evaluation metrics.