Yibing Song

Computer Vision and Pattern Recognition · Neural Information Processing Systems · 2017–2022

A researcher in the Science corpus: 10 papers; trusts 1,477 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 505 citations into it; #5,071 of 21,575 by trust here, #3,680 by citations
• Artificial Intelligence — 236 citations into it
• Model Architectures — 86 citations into it
• Generative Models — 61 citations into it
• Representation & Self-Supervised Learning — 42 citations into it
• Evaluation & Benchmarks — 33 citations into it
• Media Technology — 31 citations into it
• Optimization & Training — 29 citations into it

Whom Yibing Song trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Ross B. Girshick
6. Diederik P. Kingma
7. Martin Danelljan
8. Yoshua Bengio
9. Andrew Zisserman
10. Ilya Sutskever

Who trusts Yibing Song overall

1. Martin Danelljan
2. Goutam Bhat
3. Ning Wang
4. Seyed Mojtaba Marvasti-Zadeh
5. Zhipeng Zhang
6. Yiming Li
7. Xin Li
8. Houwen Peng
9. Tianyu Yang
10. Eunbyung Park

Papers from Yibing Song's perspective

Ordered by how strongly the researchers Yibing Song 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training (2022) — 2,096 citations
• CREST: Convolutional Residual Learning for Visual Tracking (2017) — 503 citations
• Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations (2020) — 315 citations
• VITAL: VIsual Tracking via Adversarial Learning (2018) — 533 citations
• Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks (2018) — 381 citations
• Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations (2022) — 457 citations
• Unsupervised Deep Tracking (2019) — 361 citations
• DiffusionDet: Diffusion Model for Object Detection (2022) — 758 citations
• AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition (2022) — 1,174 citations
• Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection (2022) — 325 citations