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Liqiang Nie
Researcher at Shandong University
Publications - 336
Citations - 19665
Liqiang Nie is an academic researcher from Shandong University. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 50, co-authored 255 publications receiving 12952 citations. Previous affiliations of Liqiang Nie include National University of Singapore.
Papers
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Proceedings ArticleDOI
Neural Collaborative Filtering
TL;DR: This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.
Proceedings ArticleDOI
SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
TL;DR: This paper introduces a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channel-wise Attentions in a CNN that significantly outperforms state-of-the-art visual attention-based image captioning methods.
Proceedings ArticleDOI
Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention
TL;DR: A novel attention mechanism in CF is introduced to address the challenging item- and component-level implicit feedback in multimedia recommendation, dubbed Attentive Collaborative Filtering (ACF), which significantly outperforms state-of-the-art CF methods.
Posted Content
SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning
TL;DR: SCA-CNN as mentioned in this paper incorporates spatial and channel-wise attentions in a CNN to dynamically modulate the sentence generation context in multi-layer feature maps, encoding where attentive spatial locations at multiple layers and what (i.e., attentive channels) the visual attention is.
Journal ArticleDOI
From action to activity
TL;DR: Experiments demonstrated that the approach is able to recognize activities with high accuracy from temporal patterns, and that temporal patterns can be used effectively as a mid-level feature for activity representation.