Journal ArticleDOI
A spatial-temporal attention model for human trajectory prediction
TLDR
A novel spatial-temporal attention ( ST-Attention) model is proposed, which studies spatial and temporal affinities jointly and introduces an attention mechanism to extract temporal affinity, learning the importance for historical trajectory information at different time instants.Abstract:
Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory ( LSTM ) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention ( ST-Attention ) model, which studies spatial and temporal affinities jointly. Specifically, we introduce an attention mechanism to extract temporal affinity, learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.read more
Citations
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Journal ArticleDOI
An attention‐based category‐aware GRU model for the next POI recommendation
Yuwen Liu,Aixiang Pei,Fan Wang,Yihong Yang,Xuyun Zhang,Hao Wang,Hong-Ning Dai,Lianyong Qi,Rui Ma +8 more
TL;DR: A category‐aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check‐in data, capture long‐range dependence between user check‐ins and get better recommendation results of POI category, which is evaluated using a real‐world data set, named Foursquare.
Journal ArticleDOI
Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning
TL;DR: In this paper , the authors discuss the impact of different types of state input on the performance of DRL-based lane change decision-making in autonomous driving, and propose a method that combines the perception capability of deep learning and the decision making capability of reinforcement learning.
Journal ArticleDOI
Ship Roll Prediction Algorithm Based on Bi-LSTM-TPA Combined Model
TL;DR: The experimental results of real ship data show that the proposed Bi-LSTM-TPA combined model has a significant reduction in MAPE, MAE, and MSE compared with the LSTM model and the SVM model, which verifies the effectiveness of the proposed algorithm.
Proceedings ArticleDOI
Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction
TL;DR: This model aims to forecast multiple paths based on a historical trajectory by modeling multi-scale graph-based spatial transformers combined with a trajectory smoothing algorithm named “Memory Replay” utilizing a memory graph.
Journal ArticleDOI
HSA-Net: Hidden-State-Aware Networks for High-Precision QoS Prediction
TL;DR: Wang et al. as discussed by the authors proposed a hidden-state-aware network (HSA-Net) that includes three steps called hidden state initialization, hidden state perception, and QoS prediction.
References
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Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
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Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Proceedings Article
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu,Jimmy Ba,Ryan Kiros,Kyunghyun Cho,Aaron Courville,Ruslan Salakhudinov,Ruslan Salakhudinov,Rich Zemel,Rich Zemel,Yoshua Bengio,Yoshua Bengio +10 more
TL;DR: An attention based model that automatically learns to describe the content of images is introduced that can be trained in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound.
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu,Jimmy Ba,Ryan Kiros,Kyunghyun Cho,Aaron Courville,Ruslan Salakhutdinov,Richard S. Zemel,Yoshua Bengio +7 more
TL;DR: This paper proposed an attention-based model that automatically learns to describe the content of images by focusing on salient objects while generating corresponding words in the output sequence, which achieved state-of-the-art performance on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.