B
Bin Li
Researcher at University of Science and Technology of China
Publications - 187
Citations - 5324
Bin Li is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Evolutionary algorithm & Computer science. The author has an hindex of 23, co-authored 161 publications receiving 2743 citations. Previous affiliations of Bin Li include Microsoft.
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Deformable DETR: Deformable Transformers for End-to-End Object Detection
TL;DR: Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference, can achieve better performance than DETR (especially on small objects) with 10$\times less training epochs.
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VL-BERT: Pre-training of Generic Visual-Linguistic Representations
TL;DR: A new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT), which adopts the simple yet powerful Transformer model as the backbone, and extends it to take both visual and linguistic embedded features as input.
Proceedings Article
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
TL;DR: In this paper, a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short), is introduced.
Journal ArticleDOI
Self-adaptive learning based particle swarm optimization
TL;DR: A self-adaptive learning based PSO (SLPSO) is proposed to make up the above demerits, which can update the best solution records on 26 numerical optimization problems with different characteristics such as uni-modality, multi- modality, rotation, ill-condition, mis-scale and noise.
Journal ArticleDOI
Estimation of distribution and differential evolution cooperation for large scale economic load dispatch optimization of power systems
Yu Wang,Bin Li,Thomas Weise +2 more
TL;DR: A new algorithm named ''Estimation of Distribution and Differential Evolution Cooperation'' (ED-DE) is proposed, which is a serial hybrid of two effective evolutionary computation (EC) techniques: estimation of distribution and differential evolution.