恭喜我室钟准博士的‘Zhun Zhong, Liang Zheng, Donglin Cao, Shaozi Li (Correspongding Author), "Re-ranking Person Re-identification with k-reciprocal Encoding", IEEE Conference on Computer Vision and Pattern Recognition, 2017.’,罗志明博士的‘Luo Z, Mishra A, Achkar A, Eichel J, Li S-Z, Jodoin P-M, “Non-Local Deep Features for Salient Object Detection”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)
Saliency detection aims to highlight the most relevant objects in an image. Methods using conventional models struggle whenever salient objects are pictured on top of a cluttered background while deep neural nets suffer from excess complexity and slow evaluation speeds. In this paper, we propose a simplified convolutional neural network which combines local and global information through a multi-resolution 4*5 grid structure. Instead of enforcing spacial coherence with a CRF or superpixels as is usually the case, we implemented a loss function inspired by the Mumford-Shah functional which penalizes errors on the boundary. We trained our model on the MSRA-B dataset, and tested it on six different saliency benchmark datasets. Results show that our method is on par with the state-of-the-art while reducing computation time by a factor of 18 to 100 times, enabling near real-time, high performance saliency detection.
When considering person re-identification (re-ID) as a
retrieval process, re-ranking is a critical step to improve
its accuracy. Yet in the re-ID community, limited effort
has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose
a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it
is more likely to be a true match. Specifically, given an
image, a k-reciprocal feature is calculated by encoding its
k-reciprocal nearest neighbors into a single vector, which
is used for re-ranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking
method does not require any human interaction or any
labeled data, so it is applicable to large-scale datasets.
Experiments on the large-scale Market-1501, CUHK03,
MARS, and PRW datasets confirm the effectiveness of our
method. Code is available at: https://github.com/