96 research outputs found
Predicting Human Interaction via Relative Attention Model
Predicting human interaction is challenging as the on-going activity has to
be inferred based on a partially observed video. Essentially, a good algorithm
should effectively model the mutual influence between the two interacting
subjects. Also, only a small region in the scene is discriminative for
identifying the on-going interaction. In this work, we propose a relative
attention model to explicitly address these difficulties. Built on a
tri-coupled deep recurrent structure representing both interacting subjects and
global interaction status, the proposed network collects spatio-temporal
information from each subject, rectified with global interaction information,
yielding effective interaction representation. Moreover, the proposed network
also unifies an attention module to assign higher importance to the regions
which are relevant to the on-going action. Extensive experiments have been
conducted on two public datasets, and the results demonstrate that the proposed
relative attention network successfully predicts informative regions between
interacting subjects, which in turn yields superior human interaction
prediction accuracy.Comment: To appear in IJCAI 201
Skeleton-aided Articulated Motion Generation
This work make the first attempt to generate articulated human motion
sequence from a single image. On the one hand, we utilize paired inputs
including human skeleton information as motion embedding and a single human
image as appearance reference, to generate novel motion frames, based on the
conditional GAN infrastructure. On the other hand, a triplet loss is employed
to pursue appearance-smoothness between consecutive frames. As the proposed
framework is capable of jointly exploiting the image appearance space and
articulated/kinematic motion space, it generates realistic articulated motion
sequence, in contrast to most previous video generation methods which yield
blurred motion effects. We test our model on two human action datasets
including KTH and Human3.6M, and the proposed framework generates very
promising results on both datasets.Comment: ACM MM 201
Flexible Network Binarization with Layer-wise Priority
How to effectively approximate real-valued parameters with binary codes plays
a central role in neural network binarization. In this work, we reveal an
important fact that binarizing different layers has a widely-varied effect on
the compression ratio of network and the loss of performance. Based on this
fact, we propose a novel and flexible neural network binarization method by
introducing the concept of layer-wise priority which binarizes parameters in
inverse order of their layer depth. In each training step, our method selects a
specific network layer, minimizes the discrepancy between the original
real-valued weights and its binary approximations, and fine-tunes the whole
network accordingly. During the iteration of the above process, it is
significant that we can flexibly decide whether to binarize the remaining
floating layers or not and explore a trade-off between the loss of performance
and the compression ratio of model. The resulting binary network is applied for
efficient pedestrian detection. Extensive experimental results on several
benchmarks show that under the same compression ratio, our method achieves much
lower miss rate and faster detection speed than the state-of-the-art neural
network binarization method.Comment: More experiments on image classification are planne
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