Yuki M. Asano
Computer Vision and Pattern Recognition · 2019–2023
A researcher in the Science corpus: 7 papers; trusts 1,519 researchers and is trusted by 334 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 413 citations into it; #2,259 of 21,575 by trust here, #4,152 by citations
- Artificial Intelligence — 308 citations into it; #7,252 of 35,345 by trust here, #5,600 by citations
- Representation & Self-Supervised Learning — 206 citations into it; #932 of 3,145 by trust here, #723 by citations
- Multimodal Learning — 81 citations into it
- Natural Language Processing — 40 citations into it
- Signal Processing — 39 citations into it
- Model Architectures — 38 citations into it; #764 of 2,278 by trust here, #842 by citations
- Evaluation & Benchmarks — 37 citations into it
Whom Yuki M. Asano trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Andrew Zisserman
- Ross B. Girshick
- Yoshua Bengio
- Ilya Sutskever
- Diederik P. Kingma
- Yann LeCun
- David G. Lowe
Who trusts Yuki M. Asano overall
- Spyros Gidaris
- Mathilde Caron
- Ishan Misra
- Xinlei Chen
- Mandela Patrick
- Huili Huang
- Kaiming He
- Enrico Fini
- Priya Goyal
- Yannis Kalantidis
Papers from Yuki M. Asano's perspective
Ordered by how strongly the researchers Yuki M. Asano trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Self-labelling via simultaneous clustering and representation learning (2019) — 883 citations
- Support-set bottlenecks for video-text representation learning (2020) — 271 citations
- A critical analysis of self-supervision, or what we can learn from a single image (2019) — 153 citations
- Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers (2021) — 372 citations
- Towards Label-Efficient Incremental Learning: A Survey (2023) — 5 citations
- How True is GPT-2? An Empirical Analysis of Intersectional Occupational Biases (2021) — 10 citations
- Multi-modal Self-Supervision from Generalized Data Transformations (2020) — 196 citations