1,136 research outputs found
Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
For human pose estimation in monocular images, joint occlusions and
overlapping upon human bodies often result in deviated pose predictions. Under
these circumstances, biologically implausible pose predictions may be produced.
In contrast, human vision is able to predict poses by exploiting geometric
constraints of joint inter-connectivity. To address the problem by
incorporating priors about the structure of human bodies, we propose a novel
structure-aware convolutional network to implicitly take such priors into
account during training of the deep network. Explicit learning of such
constraints is typically challenging. Instead, we design discriminators to
distinguish the real poses from the fake ones (such as biologically implausible
ones). If the pose generator (G) generates results that the discriminator fails
to distinguish from real ones, the network successfully learns the priors.Comment: Fixed typos. 14 pages. Demonstration videos are
http://v.qq.com/x/page/c039862eira.html,
http://v.qq.com/x/page/f0398zcvkl5.html,
http://v.qq.com/x/page/w0398ei9m1r.htm
Deep Descriptor Transforming for Image Co-Localization
Reusable model design becomes desirable with the rapid expansion of machine
learning applications. In this paper, we focus on the reusability of
pre-trained deep convolutional models. Specifically, different from treating
pre-trained models as feature extractors, we reveal more treasures beneath
convolutional layers, i.e., the convolutional activations could act as a
detector for the common object in the image co-localization problem. We propose
a simple but effective method, named Deep Descriptor Transforming (DDT), for
evaluating the correlations of descriptors and then obtaining the
category-consistent regions, which can accurately locate the common object in a
set of images. Empirical studies validate the effectiveness of the proposed DDT
method. On benchmark image co-localization datasets, DDT consistently
outperforms existing state-of-the-art methods by a large margin. Moreover, DDT
also demonstrates good generalization ability for unseen categories and
robustness for dealing with noisy data.Comment: Accepted by IJCAI 201
Learning Semantically Enhanced Feature for Fine-Grained Image Classification
We aim to provide a computationally cheap yet effective approach for
fine-grained image classification (FGIC) in this letter. Unlike previous
methods that rely on complex part localization modules, our approach learns
fine-grained features by enhancing the semantics of sub-features of a global
feature. Specifically, we first achieve the sub-feature semantic by arranging
feature channels of a CNN into different groups through channel permutation.
Meanwhile, to enhance the discriminability of sub-features, the groups are
guided to be activated on object parts with strong discriminability by a
weighted combination regularization. Our approach is parameter parsimonious and
can be easily integrated into the backbone model as a plug-and-play module for
end-to-end training with only image-level supervision. Experiments verified the
effectiveness of our approach and validated its comparable performance to the
state-of-the-art methods. Code is available at https://github.com/cswluo/SEFComment: Accepted by IEEE Signal Processing Letters. 5 pages, 4 figures, 4
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