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Domain Generalization with Adversarial Feature Learning
TLDR
This paper presents a novel framework based on adversarial autoencoders to learn a generalized latent feature representation across domains for domain generalization, and proposed an algorithm to jointly train different components of the proposed framework.
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NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
TLDR
This work introduces a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames, and investigates a novel one-shot 3D activity recognition problem on this dataset.
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Eigenfeature Regularization and Extraction in Face Recognition
TLDR
Experiments comparing the proposed approach with some other popular subspace methods on the FERET, ORL, AR, and GT databases show that the method consistently outperforms others.
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Global Context-Aware Attention LSTM Networks for 3D Action Recognition
TLDR
This work proposes a new class of LSTM network, Global Context-Aware Attention L STM (GCA-LSTM), for 3D action recognition, which is able to selectively focus on the informative joints in the action sequence with the assistance of global contextual information.
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Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
TLDR
A new gating mechanism within LSTM module is introduced, with which the network can learn the reliability of the sequential data and accordingly adjust the effect of the input data on the updating procedure of the long-term context representation stored in the unit's memory cell.
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Distance-reciprocal distortion measure for binary document images
TLDR
The results show that the proposed distortion measure matches well to subjective evaluation by human visual perception.
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A Fast Approach for No-Reference Image Sharpness Assessment Based on Maximum Local Variation
TLDR
The proposed approach has a very low computational complexity and the performance analysis shows that the approach outperforms the state-of-the-art techniques in terms of correlation with human vision system on several commonly used databases.
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Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks
TLDR
A new class of L STM network is proposed, global context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a global context memory cell.
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Two-Dimensional Polar Harmonic Transforms for Invariant Image Representation
TLDR
A set of 2D transforms, based on a set of orthogonal projection bases, to generate aSet of features which are invariant to rotation, called Polar Harmonic Transforms (PHTs), which encompass the orthogonality and invariance advantages of Zernike and pseudo-Zernike moments, but are free from their inherent limitations.
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Deep Coupled ResNet for Low-Resolution Face Recognition
TLDR
Thorough evaluation on LFW and SCface databases shows that the proposed DCR model achieves consistently and considerably better performance than the state of the arts.
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