Titlebook: Computer Vision -- ACCV 2014; 12th Asian Conferenc Daniel Cremers,Ian Reid,Ming-Hsuan Yang Conference proceedings 2015 Springer Internation

refine

https://doi.org/10.1007/978-1-62703-266-7de-off in .-NN classification, evaluating the impact of related techniques (posterior probability estimation and metric learning). Experimental results show that a proper combination of .-NN and metric learning can be very effective and obtain good performance.

incredulity

Cytoskeleton Dynamics and Binding Factorserformance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training.

SPASM

Elena E. Grintsevich,Emil Reislerations discriminating character and non-character CCs, resulting in the improved detection accuracy. The proposed method is evaluated on ICDAR and SVT public datasets and achieves the state-of-the-art results, which reveal the effectiveness of the method.

钝剑

厚颜

Single Image Smoke Detectiontructed as a concatenation of the respective sparse coefficients. Extensive experiments were conducted and the results have shown that the proposed feature significantly outperforms the existing features for smoke detection.

CERE

Accuracy and Specificity Trade-off in ,-nearest Neighbors Classificationde-off in .-NN classification, evaluating the impact of related techniques (posterior probability estimation and metric learning). Experimental results show that a proper combination of .-NN and metric learning can be very effective and obtain good performance.

搬运工

Part Detector Discovery in Deep Convolutional Neural Networkserformance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training.

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使饥饿

Einat Sadot,Elison B. Blancaflord completeness of the final reconstruction. Unlike previous works, which only address the problem of efficient structure from motion (SfM), our technique is highly applicable to the whole reconstruction pipeline, and solves the problems of efficient bundle adjustment, multi-view stereo (MVS), and subsequent variational refinement.

鬼魂