Bo Pang
Computer Vision and Pattern Recognition · with Soricut · 2019–2023
A researcher in the Science corpus: 6 papers; trusts 677 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 233 citations into it; #16,104 of 21,575 by trust here, #9,305 by citations
- Artificial Intelligence — 152 citations into it
- Multimodal Learning — 83 citations into it; #860 of 1,211 by trust here, #736 by citations
- Natural Language Processing — 45 citations into it
- Evaluation & Benchmarks — 43 citations into it
- Representation & Self-Supervised Learning — 20 citations into it
- Model Architectures — 15 citations into it
- Human-Computer Interaction — 10 citations into it
Whom Bo Pang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Ross B. Girshick
- Jimmy Ba
- Yann LeCun
- Ilya Sutskever
- Christian Szegedy
Who trusts Bo Pang overall
Papers from Bo Pang's perspective
Ordered by how strongly the researchers Bo Pang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023) — 301 citations
- A Case Study on Combining ASR and Visual Features for Generating Instructional Video Captions (2019) — 40 citations
- Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering (2019) — 20 citations
- Human Pose Regression with Residual Log-likelihood Estimation (2021) — 300 citations
- Understanding Guided Image Captioning Performance across Domains (2020) — 32 citations
- Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube (2020) — 4 citations