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AE-Net:Adjoint Enhancement Network for Efficient Action Recognition in Video Understanding
Action recognition in video understanding is a challenging task, largely because of the complexity and difficulty in temporal modeling, making it suffer from motion information loss and misalignment of temporal attention in spatial dimensions.
Ranked #28 on
Action Recognition
on Something-Something V1
Guidance Module Network for Video Captioning
In this paper, we present a novel architecture which introduces a guidance module to encourage the encoder-decoder model to generate words related to the past and future words in a caption.
Tensor p-shrinkage nuclear norm for low-rank tensor completion
In particular, it can be proved that p-TNN is a better approximation of the tensor average rank than the tensor nuclear norm when p < 1.
Binary matrix completion with nonconvex regularizers
Many practical problems involve the recovery of a binary matrix from partial information, which makes the binary matrix completion (BMC) technique received increasing attention in machine learning.
