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 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.
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.
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.
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Video Captioning With Attention-Based LSTM and Semantic Consistency
Lianli Gao;Zhao Guo;Hanwang Zhang;Xing Xu.
IEEE Transactions on Multimedia (2017)
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)
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)
Jingkuan Song;Lianli Gao;Li Liu;Xiaofeng Zhu.
Pattern Recognition (2018)
From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning
Jingkuan Song;Yuyu Guo;Lianli Gao;Xuelong Li.
IEEE Transactions on Neural Networks (2019)
Deep adversarial metric learning for cross-modal retrieval
Xing Xu;Li He;Huimin Lu;Lianli Gao.
World Wide Web (2019)
Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder
Jingkuan Song;Hanwang Zhang;Xiangpeng Li;Lianli Gao.
IEEE Transactions on Image Processing (2018)
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)
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)
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning
Jingkuan Song;Lianli Gao;Zhao Guo;Wu Liu.
international joint conference on artificial intelligence (2017)
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