Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation.
We get inspirations from the recently proposed lottery ticket hypothesis (LTH), which argues that the dense and over-parameterized model contains a much smaller and sparser sub-model that can reach comparable performance to the full model.
Then, we propose a novel temporal message propagation network to extract the graph feature from the normalized graph, and combine the graph feature with designed expert patterns to yield a final detection system.
In this paper, we explore combining deep learning with expert patterns in an explainable fashion.
Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways.
Multi-frame human pose estimation in complicated situations is challenging.
Ranked #1 on Multi-Person Pose Estimation on PoseTrack2017 (using extra training data)
In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN).
Predicting human motion from a historical pose sequence is at the core of many applications in computer vision.
In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization.
Anticipating the future motions of 3D articulate objects is challenging due to its non-linear and highly stochastic nature.
Next, we prove that our mechanism is an FPTAS, i. e., it can be approximated within $1 + \epsilon$ for any given $\epsilon > 0$, while the running time of our mechanism is polynomial in $n$ and $1/\epsilon$, where $n$ is the number of tenants in the datacenter.
Computer Science and Game Theory
As such, the key to an item-based CF method is in the estimation of item similarities.