To address this issue, we connect this structured output learning problem with the structured modeling framework in sequence transduction field.
Predictor-based Neural Architecture Search (NAS) continues to be an important topic because it aims to mitigate the time-consuming search procedure of traditional NAS methods.
During the training process, the polarization effect will drive a subset of gates to smoothly decrease to exact zero, while other gates gradually stay away from zero by a large margin.
To further improve the performance of these tasks, we propose a novel Hand Image Understanding (HIU) framework to extract comprehensive information of the hand object from a single RGB image, by jointly considering the relationships between these tasks.
The recommendation system (RS) plays an important role in the content recommendation and retrieval scenarios.
The goal of this work is to develop a novel learning framework for accurate and expressive fashion captioning.
Via training with regular SGD on the former but a novel update rule with penalty gradients on the latter, we realize structured sparsity.
Color compatibility is important for evaluating the compatibility of a fashion outfit, yet it was neglected in previous studies.
Neural Architecture Search (NAS) has shown great potentials in automatically designing scalable network architectures for dense image predictions.
Ranked #6 on Semantic Segmentation on Cityscapes test