The performance of person re-identification (Re-ID) has been seriously affected by the large cross-view appearance variations caused by mutual occlusions and background clutter. Hence, learning a feature representation that can adaptively emphasize the foreground persons becomes very critical to solve the person Re-ID problem. In this paper, we propose a simple yet effective foreground attentive neural network (FANN) to learn a discriminative feature representation for person Re-ID, which can adaptively enhance the positive side of foreground and weaken the negative side of background. Specifically, a novel foreground attentive subnetwork is designed to drive the network’s attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons. The resulting feature maps of encoder network are further fed into the body part subnetwork and feature fusion subnetwork to learn discriminative features. Besides, a novel symmetric triplet loss function is introduced to supervise feature learning, in which the intra-class distance is minimized and the inter-class distance is maximized in each triplet unit, simultaneously. Training our FANN in a multi-task learning framework, a discriminative feature representation can be learned to find out the matched reference to each probe among various candidates in the gallery. Extensive experimental results on several public benchmark datasets are evaluated, which have shown clear improvements of our method over the state-of-the-art approaches.
Metric learning based method. This category of methods aim to find a mapping function from the feature space to distance space, in which distances between images of the same person are closer than those between different identities. For example, @cite_72 proposed a relative distance learning method from the probabilistic prospective. @cite_18 , learned a distance metric with the sparse pairwise similarity constraints. @cite_67 utilized the Local Fisher Discriminant Analysis (LFDA) to map the high dimensional features into a more discriminative low dimensional space. In @cite_58 , further extended the LFDA and several other metrics by using the kernel tricks and different regularizers. @cite_56 measured the similarity between image pairs through the cosine similarity, which was closely related to the inner product similarity. @cite_49 , casted the person Re-ID problem as an image retrieval task by considering the listwise similarity. @cite_6 proposed a kernel based metric learning method to explore the nonlinear relationship of samples in feature space. @cite_50 , learned a discriminative metric by using the relaxed pairwise constraints. These methods try to learn a specific distance metric based on features extracted from the fixed feature descriptors, which could not fully discover the potential of metric learning.
@cite_67 @cite_18 @cite_6 @cite_56 @cite_72 @cite_49 @cite_50 @cite_58
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Metric learning aims at finding a distance that approximates a task-specific notion of semantic similarity. Typically, a Mahalanobis distance is learned from pairs of data labeled as being semantically similar or not. In this paper, we learn such metrics in a weakly supervised setting where "bags" of instances are labeled with "bags" of labels. We formulate the problem as a multiple instance learning (MIL) problem over pairs of bags. If two bags share at least one label, we label the pair positive, and negative otherwise. We propose to learn a metric using those labeled pairs of bags, leading to MildML, for multiple instance logistic discriminant metric learning. MildML iterates between updates of the metric and selection of putative positive pairs of examples from positive pairs of bags. To evaluate our approach, we introduce a large and challenging data set, Labeled Yahoo! News, which we have manually annotated and contains 31147 detected faces of 5873 different people in 20071 images. We group the faces detected in an image into a bag, and group the names detected in the caption into a corresponding set of labels. When the labels come from manual annotation, we find that MildML using the bag-level annotation performs as well as fully supervised metric learning using instance-level annotation. We also consider performance in the case of automatically extracted labels for the bags, where some of the bag labels do not correspond to any example in the bag. In this case MildML works substantially better than relying on noisy instance-level annotations derived from the bag-level annotation by resolving face-name associations in images with their captions. This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild. Different from existing metric learning-based face verification methods which aim to learn a Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, simultaneously, the proposed DDML trains a deep neural network which learns a set of hierarchical nonlinear transformations to project face pairs into the same feature subspace, under which the distance of each positive face pair is less than a smaller threshold and that of each negative pair is higher than a larger threshold, respectively, so that discriminative information can be exploited in the deep network. Our method achieves very competitive face verification performance on the widely used LFW and YouTube Faces (YTF) datasets. Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images. The majority of current approaches are non-parametric, learning the metric space directly through the supervision of similar (pairs) or relatively similar (triplets) sets of images. A difficult challenge for training these approaches is mining informative samples of images as the metric space is learned with only the local context present within a single mini-batch. Alternative approaches use parametric metric learning to eliminate the need for sampling through supervision of images to proxies. Although this simplifies optimization, such proxy-based approaches have lagged behind in performance. In this work, we demonstrate that a standard classification network can be transformed into a variant of proxy-based metric learning that is competitive against non-parametric approaches across a wide variety of image retrieval tasks. We address key challenges in proxy-based metric learning such as performance under extreme classification and describe techniques to stabilize and learn higher dimensional embeddings. We evaluate our approach on the CAR-196, CUB-200-2011, Stanford Online Product, and In-Shop datasets for image retrieval and clustering. Finally, we show that our softmax classification approach can learn high-dimensional binary embeddings that achieve new state-of-the-art performance on all datasets evaluated with a memory footprint that is the same or smaller than competing approaches. Metric learning makes it plausible to learn semantically meaningful distances for complex distributions of data using label or pairwise constraint information. However, to date, most metric learning methods are based on a single Mahalanobis metric, which cannot handle heterogeneous data well. Those that learn multiple metrics throughout the feature space have demonstrated superior accuracy, but at a severe cost to computational efficiency. Here, we adopt a new angle on the metric learning problem and learn a single metric that is able to implicitly adapt its distance function throughout the feature space. This metric adaptation is accomplished by using a random forest-based classifier to underpin the distance function and incorporate both absolute pairwise position and standard relative position into the representation. We have implemented and tested our method against state of the art global and multi-metric methods on a variety of data sets. Overall, the proposed method outperforms both types of method in terms of accuracy (consistently ranked first) and is an order of magnitude faster than state of the art multi-metric methods (16x faster in the worst case).