H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 3,849 107 World Ranking 8763 National Ranking 849

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Pattern recognition

Lianli Gao spends much of his time researching Artificial intelligence, Closed captioning, Machine learning, Visualization and Feature extraction. His Artificial intelligence study frequently links to other fields, such as Pattern recognition. His Pattern recognition study combines topics in areas such as Subspace topology, Hash function, Leverage and Embedding.

He has researched Closed captioning in several fields, including Speech recognition, Latent variable and Natural language processing. His study in Machine learning is interdisciplinary in nature, drawing from both Multimedia and Computer graphics. His biological study spans a wide range of topics, including Semi-supervised learning, Regularization and Training set.

His most cited work include:

  • Video Captioning With Attention-Based LSTM and Semantic Consistency (292 citations)
  • Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition (151 citations)
  • Two-Stream 3-D convNet Fusion for Action Recognition in Videos With Arbitrary Size and Length (149 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Hash function and Data mining. His work in Artificial intelligence addresses issues such as Natural language processing, which are connected to fields such as Representation. His studies in Pattern recognition integrate themes in fields like Artificial neural network, Binary code and Pyramid.

His study in the field of Support vector machine and Deep learning also crosses realms of Knowledge transfer. He studied Hash function and Image retrieval that intersect with Algorithm and Feature vector. In general Data mining study, his work on Data stream mining often relates to the realm of Data quality, thereby connecting several areas of interest.

He most often published in these fields:

  • Artificial intelligence (63.57%)
  • Pattern recognition (33.33%)
  • Machine learning (17.83%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (63.57%)
  • Pattern recognition (33.33%)
  • Machine learning (17.83%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Artificial intelligence, Pattern recognition, Machine learning, Natural language processing and Context. He has included themes like Pixel, Generator and Modality in his Pattern recognition study. His Machine learning study incorporates themes from Ontology, Semantic reasoner and Semantic Web.

When carried out as part of a general Natural language processing research project, his work on Natural language is frequently linked to work in Graph, therefore connecting diverse disciplines of study. Lianli Gao focuses mostly in the field of Natural language, narrowing it down to matters related to Visualization and, in some cases, Feature extraction. His research in Closed captioning intersects with topics in BLEU and Visual Word.

Between 2019 and 2021, his most popular works were:

  • Hierarchical LSTMs with Adaptive Attention for Visual Captioning (89 citations)
  • Unified Binary Generative Adversarial Network for Image Retrieval and Compression (24 citations)
  • Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences (12 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Pattern recognition

Lianli Gao focuses on Artificial intelligence, Pattern recognition, Natural language processing, Machine learning and Artificial neural network. His work on Discriminative model, Benchmark and Closed captioning is typically connected to Context and Task analysis as part of general Artificial intelligence study, connecting several disciplines of science. His work deals with themes such as BLEU and Visual Word, which intersect with Closed captioning.

The study of Pattern recognition is intertwined with the study of Semantics in a number of ways. His Natural language processing research integrates issues from Classifier, Object detection, Inference and Softmax function. Lianli Gao combines subjects such as Text recognition and Vulnerability with his study of Machine learning.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Video Captioning With Attention-Based LSTM and Semantic Consistency

Lianli Gao;Zhao Guo;Hanwang Zhang;Xing Xu.
IEEE Transactions on Multimedia (2017)

348 Citations

Two-Stream 3-D convNet Fusion for Action Recognition in Videos With Arbitrary Size and Length

Xuanhan Wang;Lianli Gao;Peng Wang;Xiaoshuai Sun.
IEEE Transactions on Multimedia (2018)

197 Citations

Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition

Xuanhan Wang;Lianli Gao;Jingkuan Song;Heng Tao Shen.
IEEE Signal Processing Letters (2017)

184 Citations

Quantization-based hashing

Jingkuan Song;Lianli Gao;Li Liu;Xiaofeng Zhu.
Pattern Recognition (2018)

168 Citations

From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning

Jingkuan Song;Yuyu Guo;Lianli Gao;Xuelong Li.
IEEE Transactions on Neural Networks (2019)

159 Citations

Deep adversarial metric learning for cross-modal retrieval

Xing Xu;Li He;Huimin Lu;Lianli Gao.
World Wide Web (2019)

156 Citations

Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder

Jingkuan Song;Hanwang Zhang;Xiangpeng Li;Lianli Gao.
IEEE Transactions on Image Processing (2018)

146 Citations

Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-nearest neighbour algorithm.

Owen R. Bidder;Hamish A. Campbell;Agustina Gómez-Laich;Patricia Urgé.
PLOS ONE (2014)

122 Citations

Hierarchical LSTMs with Adaptive Attention for Visual Captioning

Lianli Gao;Xiangpeng Li;Jingkuan Song;Heng Tao Shen.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)

119 Citations

Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning

Jingkuan Song;Lianli Gao;Zhao Guo;Wu Liu.
international joint conference on artificial intelligence (2017)

108 Citations

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Best Scientists Citing Lianli Gao

Heng Tao Shen

Heng Tao Shen

University of Electronic Science and Technology of China

Publications: 38

Yang Yang

Yang Yang

University of Electronic Science and Technology of China

Publications: 26

Fumin Shen

Fumin Shen

University of Electronic Science and Technology of China

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Jingkuan Song

Jingkuan Song

Columbia University

Publications: 23

Zi Huang

Zi Huang

University of Queensland

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Zhou Zhao

Zhou Zhao

Zhejiang University

Publications: 18

Jiebo Luo

Jiebo Luo

University of Rochester

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Xianglong Liu

Xianglong Liu

Beihang University

Publications: 16

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 16

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 15

Qingming Huang

Qingming Huang

Chinese Academy of Sciences

Publications: 13

Wei Liu

Wei Liu

Tencent (China)

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Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 13

Lin Ma

Lin Ma

Harbin Institute of Technology

Publications: 12

Tat-Seng Chua

Tat-Seng Chua

National University of Singapore

Publications: 11

Huimin Lu

Huimin Lu

Kyushu Institute of Technology

Publications: 11

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