Meng Wang

Computer Vision and Pattern Recognition · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · 2011–2021

A researcher in the Science corpus: 29 papers; trusts 1,738 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 616 citations into it; #4,330 of 21,575 by trust here, #2,240 by citations
• Artificial Intelligence — 520 citations into it; #4,188 of 35,345 by trust here, #3,457 by citations
• Information Systems — 277 citations into it; #498 of 8,771 by trust here, #406 by citations
• Representation & Self-Supervised Learning — 98 citations into it
• Management Science and Operations Research — 97 citations into it; #11,595 of 11,946 by trust here, #5,787 by citations
• Recommender Systems — 78 citations into it
• Multimodal Learning — 74 citations into it
• Model Architectures — 73 citations into it

Whom Meng Wang trusts overall

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

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

Who trusts Meng Wang overall

1. Xiangnan He
2. Xiang Wang
3. Jun Yu
4. Tat-seng Chua
5. Chen Gao
6. Liang Zheng
7. Junliang Yu
8. Le Wu
9. Liqiang Nie
10. Mang Ye

Papers from Meng Wang's perspective

Ordered by how strongly the researchers Meng Wang 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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

• DisturbLabel: Regularizing CNN on the Loss Layer (2016) — 263 citations
• Multimodal Graph-Based Reranking for Web Image Search (2012) — 366 citations
• Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search (2013) — 463 citations
• Adaptive Transfer Network for Cross-Domain Person Re-Identification (2019) — 284 citations
• Multimodal Deep Autoencoder for Human Pose Recovery (2015) — 553 citations
• Understanding Blooming Human Groups in Social Networks (2015) — 29 citations
• Enhancing Person Re-identification in a Self-Trained Subspace (2017) — 86 citations
• More Grounded Image Captioning by Distilling Image-Text Matching Model (2020) — 148 citations
• 3-D Object Retrieval and Recognition With Hypergraph Analysis (2012) — 627 citations
• Bias and Debias in Recommender System: A Survey and Future Directions (2020) — 923 citations
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021) — 445 citations
• ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration (2021) — 60 citations
• Adversarial co-distillation learning for image recognition (2021) — 48 citations
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (2020) — 5,414 citations
• Feature Pyramid Transformer (2020) — 306 citations
• Iterative Context-Aware Graph Inference for Visual Dialog (2020) — 54 citations
• Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach (2020) — 654 citations
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation (2020) — 350 citations
• A Neural Influence Diffusion Model for Social Recommendation (2019) — 566 citations
• Neural Graph Collaborative Filtering (2019) — 3,905 citations