Deep adaptive feature embedding with local sample distributions for person re-identification
TLDR
A novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep feature embedding for person re-id by proposing a novel sampling to mine suitable positives within a local range to improve the deep embedding in the context of large intra-class variations.About:
This article is published in Pattern Recognition.The article was published on 2018-01-01 and is currently open access. It has received 196 citations till now. The article focuses on the topics: Euclidean distance & Metric (mathematics).read more
Citations
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Journal ArticleDOI
Improving person re-identification by attribute and identity learning
TL;DR: An attribute-person recognition (APR) network is proposed, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes, and demonstrates that by learning a more discriminative representation, APR achieves competitive re-IDs performance compared with the state-of-the-art methods.
Journal ArticleDOI
Binary Neural Networks: A Survey
TL;DR: A comprehensive survey of algorithms proposed for binary neural networks, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error are presented.
Journal ArticleDOI
Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization
TL;DR: An iterative multiview agreement strategy by minimizing the divergence objective among all factorized latent data-cluster representations during each iteration of optimization process, where such latent representation from each view serves to regulate those from other views, and such an intuitive process iteratively coordinates all views to be agreeable.
Proceedings ArticleDOI
Visible thermal person re-identification via dual-constrained top-ranking
TL;DR: A dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations and identity loss is further incorporated to model the identity-specific information to handle large intra-class variations.
Book ChapterDOI
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection
TL;DR: Wang et al. as discussed by the authors presented a deep attention model based on recurrent neural networks (RNNs) to selectively learn temporal representations of sequential posts for rumor identification, which is able to capture long-range dependencies among postings and produce distinct representations for the accurate early detection.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.