472 research outputs found
Discriminative Feature Learning with Foreground Attention for Person Re-Identification
The performance of person re-identification (Re-ID) has been seriously
effected by the large cross-view appearance variations caused by mutual
occlusions and background clutters. 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
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A new patch selection method based on parsing and saliency detection for person re-identification
Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples
Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance in the number of noisy-labeled samples in the PAR training datasets, thus leading to suboptimal performance. To address the above problem of imbalanced noisy-labeled samples, we propose a novel and effective loss called drop loss for PAR. In the drop loss, the attributes are treated differently in an easy-to-hard way. In particular, the noisy-labeled candidates, which are identified according to their gradient norms, are dropped with a higher drop rate for the harder attribute. Such a manner adaptively alleviates the adverse effect of imbalanced noisy-labeled samples on model learning. To illustrate the effectiveness of the proposed loss, we train a simple ResNet-50 model based on the drop loss and term it DropNet. Experimental results on two representative PAR tasks (including facial attribute recognition and pedestrian attribute recognition) demonstrate that the proposed DropNet achieves comparable or better performance in terms of both balanced accuracy and classification accuracy over several state-of-the-art PAR methods
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach
Reformulating computer vision problems over Riemannian manifolds has
demonstrated superior performance in various computer vision applications. This
is because visual data often forms a special structure lying on a lower
dimensional space embedded in a higher dimensional space. However, since these
manifolds belong to non-Euclidean topological spaces, exploiting their
structures is computationally expensive, especially when one considers the
clustering analysis of massive amounts of data. To this end, we propose an
efficient framework to address the clustering problem on Riemannian manifolds.
This framework implements random projections for manifold points via kernel
space, which can preserve the geometric structure of the original space, but is
computationally efficient. Here, we introduce three methods that follow our
framework. We then validate our framework on several computer vision
applications by comparing against popular clustering methods on Riemannian
manifolds. Experimental results demonstrate that our framework maintains the
performance of the clustering whilst massively reducing computational
complexity by over two orders of magnitude in some cases
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