This paper presents a novel Multi-metadata Embedding based Cross-Transformer (MECT) to improve the performance of Chinese NER by fusing the structural information of Chinese characters.
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 way, all the operations in the training and inference can be bit-wise operations, pushing towards faster processing speed, decreased memory cost, and higher energy efficiency.
Compact convolutional neural networks gain efficiency mainly through depthwise convolutions, expanded channels and complex topologies, which contrarily aggravate the training process.
To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID.
In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification.
Batch Normalization (BN) has been proven to be quite effective at accelerating and improving the training of deep neural networks (DNNs).
Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics.
When the vocabulary size is large, the space taken to store the model parameters becomes the bottleneck for the use of recurrent neural language models.
Current state-of-the-art systems for visual content analysis require large training sets for each class of interest, and performance degrades rapidly with fewer examples.