Following the top-down paradigm, we decompose the task into two stages, i. e. person localization and pose estimation.
Then, a compact set of the possible combinations for different token pooling and attention sharing mechanisms are constructed.
We introduce the first Neural Architecture Search (NAS) method to find a better transformer architecture for image recognition.
Ranked #184 on Image Classification on ImageNet
In this MCGN, the labels and features of support data are used by the CRF for inferring GNN affinities in a principled and probabilistic way.
As a special design of this transformer, the information encoded in the encoder is different from that in the decoder, i. e. the encoder encodes temporal-channel information of multiple frames while the decoder decodes the spatial-channel information for the current frame in a voxel-wise manner.
Accurate demand forecasting of different public transport modes(e. g., buses and light rails) is essential for public service operation. However, the development level of various modes often varies sig-nificantly, which makes it hard to predict the demand of the modeswith insufficient knowledge and sparse station distribution (i. e., station-sparse mode).
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class.
We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers and to predict the top-$N$ purchase destinations of a consumer.
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Ranked #3 on Traffic Prediction on PeMS04
Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models.
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.