Lianli Gao

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 11 papers; trusts 923 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 252 citations into it; #20,183 of 21,575 by trust here, #7,584 by citations
• Artificial Intelligence — 213 citations into it; #12,874 of 35,345 by trust here, #8,439 by citations
• Natural Language Processing — 73 citations into it; #2,432 of 5,688 by trust here, #2,529 by citations
• Representation & Self-Supervised Learning — 71 citations into it
• Multimodal Learning — 37 citations into it
• Meta & Continual Learning — 35 citations into it
• Model Architectures — 29 citations into it
• Optimization & Training — 29 citations into it

Whom Lianli Gao trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. Kaiming He
6. Christian Szegedy
7. Yann LeCun
8. Karen Simonyan
9. Ilya Sutskever
10. Ross B. Girshick

Who trusts Lianli Gao overall

1. Jingkuan Song
2. Lei Wang
3. Wenzhong Guo
4. Bairui Wang
5. Qinzhuo Wu
6. Zhiyuan Fang
7. Jun Cheng
8. Jipeng Zhang
9. Dongxiang Zhang
10. Xiangpeng Li

Papers from Lianli Gao's perspective

Ordered by how strongly the researchers Lianli Gao trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Video Captioning With Attention-Based LSTM and Semantic Consistency (2017) — 589 citations
• Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering (2019) — 299 citations
• Perceptual Pyramid Adversarial Networks for Text-to-Image Synthesis (2019) — 46 citations
• MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning (2018) — 122 citations
• Template-Based Math Word Problem Solvers with Recursive Neural Networks (2019) — 168 citations
• Class Gradient Projection For Continual Learning (2022) — 26 citations
• Progressive Meta-Learning With Curriculum (2022) — 71 citations
• Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression (2022) — 86 citations
• Curriculum-Based Meta-learning (2021) — 28 citations
• Learning Cross-Modal Common Representations by Private–Shared Subspaces Separation (2020) — 51 citations
• Learning in high-dimensional multimedia data: the state of the art (2015) — 129 citations