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LSTM-in-LSTM for generating long descriptions of images [PDF]

Computational Visual Media, 2016
In this paper, we propose an approach for generating rich fine-grained textual descriptions of images. In particular, we use an LSTM-in-LSTM (long short-term memory) architecture, which consists of an inner LSTM and an outer LSTM. The inner LSTM effectively encodes the long-range implicit contextual interaction between visual cues (i.e., the ...
Jun Song, Siliang Tang, Jun Xiao
exaly   +2 more sources

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Host–Parasite: Graph LSTM-in-LSTM for Group Activity Recognition

IEEE Transactions on Neural Networks and Learning Systems, 2021
This article aims to tackle the problem of group activity recognition in the multiple-person scene. To model the group activity with multiple persons, most long short-term memory (LSTM)-based methods first learn the person-level action representations by several LSTMs and then integrate all the person-level action representations into the following ...
Xiangbo Shu, Liyan Zhang, Yunlian Sun
exaly   +3 more sources

CA-LSTM

The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018
Search task identification aims to understand a user's information needs to improve search quality for applications such as query suggestion, personalized search, and advertisement retrieval. To properly identify the search task within long query sessions, it is important to partition these sessions into segments before further processing.
Cong Du, Peng Shu, Yong Li
openaire   +1 more source

E-LSTM

Proceedings of the 56th Annual Design Automation Conference 2019, 2019
Various models with Long Short-Term Memory (LSTM) network have demonstrated prior art performances in sequential information processing. Previous LSTM-specific architectures set large on-chip memory for weight storage to alleviate the memory-bound issue and facilitate the LSTM inference in cloud computing. In this paper, E-LSTM is proposed for embedded
Runbin Shi, Junjie Liu, Hayden Kwok-Hay So, Shuo Wang 0009, Yun Liang 0001   +4 more
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Spatiotemporal Representation Learning with GAN Trained LSTM-LSTM Networks

2020 IEEE International Conference on Robotics and Automation (ICRA), 2020
Learning robot behaviors in unstructured environments often requires handcrafting the features for a given task. In this paper, we present and evaluate an unsupervised representation learning architecture, Layered Spatiotemporal Memory Long Short-Term Memory (LSTM-LSTM), that learns the underlying representation without knowledge of the task.
Yiwei Fu, Shiraj Sen, Johan Reimann, Charles Theurer   +3 more
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LSTM-BA: DDoS Detection Approach Combining LSTM and Bayes

2019 Seventh International Conference on Advanced Cloud and Big Data (CBD), 2019
The development of cyberspace brings both opportunities and threats, among which Distributed Denial of Service (DDoS) is one of the most destructive attacks. A mass of DDoS attack detection methods have been proposed. But more or less there are some problems, either the construction process is complex, or low accuracy, or poor generalization ability ...
Yan Li 0085, Yifei Lu 0001
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SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction

2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
Pedestrian trajectory prediction is an extremely challenging problem because of the crowdedness and clutter of the scenes. Previous deep learning LSTM-based approaches focus on the neighbourhood influence of pedestrians but ignore the scene layouts in pedestrian trajectory prediction.
Hao Xue 0001, Du Q. Huynh, Mark Reynolds 0001   +2 more
openaire   +1 more source

Forecasting agricultural commodities prices using deep learning-based models: basic LSTM, bi-LSTM, stacked LSTM, CNN LSTM, and convolutional LSTM

International Journal of Sustainable Agricultural Management and Informatics, 2022
R. Murugesan, Eva Mishra, Akash Hari Krishnan   +2 more
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AB-LSTM

ACM Transactions on Multimedia Computing, Communications, and Applications, 2019
Detection of scene text in arbitrary shapes is a challenging task in the field of computer vision. Most existing scene text detection methods exploit the rectangle/quadrangular bounding box to denote the detected text, which fails to accurately fit text with arbitrary shapes, such as curved text. In addition, recent progress on scene text detection has
Zhandong Liu, Wengang Zhou 0001, Houqiang Li   +2 more
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