Xiaoliang Dai

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 13 papers; trusts 1,026 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 418 citations into it; #7,998 of 21,575 by trust here, #2,228 by citations
• Artificial Intelligence — 331 citations into it; #17,316 of 35,345 by trust here, #6,103 by citations
• Efficiency & Compression — 144 citations into it; #508 of 1,264 by trust here, #200 by citations
• Model Architectures — 112 citations into it
• Optimization & Training — 44 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Evaluation & Benchmarks — 30 citations into it
• Natural Language Processing — 20 citations into it

Whom Xiaoliang Dai trusts overall

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

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

Who trusts Xiaoliang Dai overall

1. Hongxu Yin
2. Shiwei Liu
3. Xuanyi Dong
4. Dimitrios Stamoulis
5. Hesham Mostafa
6. Bichen Wu
7. Mingxing Tan
8. Wuyang Chen
9. Wujie Wen
10. Tao Lin

Papers from Xiaoliang Dai's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search (2018) — 1,467 citations
• Visual Transformers: Token-based Image Representation and Processing for Computer Vision (2020) — 755 citations
• ChamNet: Towards Efficient Network Design Through Platform-Aware Model Adaptation (2018) — 294 citations
• Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP (2022) — 767 citations
• FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions (2020) — 334 citations
• FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function (2020) — 77 citations
• Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack (2023) — 312 citations
• NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm (2017) — 258 citations
• Fully Dynamic Inference With Deep Neural Networks (2020) — 55 citations
• Cross-Domain Adaptive Teacher for Object Detection (2021) — 296 citations
• Token Merging: Your ViT But Faster (2022) — 1,128 citations
• EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything (2023) — 303 citations
• Incremental Learning Using a Grow-and-Prune Paradigm With Efficient Neural Networks (2019) — 36 citations