Qixiang Ye
Computer Vision and Pattern Recognition · 2005–2025
A researcher in the Science corpus: 21 papers; trusts 1,947 researchers and is trusted by 56 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 699 citations into it; #3,977 of 21,575 by trust here, #2,117 by citations
- Artificial Intelligence — 393 citations into it; #34,887 of 35,345 by trust here, #9,557 by citations
- Model Architectures — 150 citations into it; #1,338 of 2,278 by trust here, #1,378 by citations
- Representation & Self-Supervised Learning — 71 citations into it
- Evaluation & Benchmarks — 57 citations into it
- Media Technology — 57 citations into it; #1,428 of 3,621 by trust here, #746 by citations
- Efficiency & Compression — 49 citations into it
- Aerospace Engineering — 38 citations into it; #2,138 of 6,421 by trust here, #1,402 by citations
Whom Qixiang Ye trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Ross B. Girshick
- Geoffrey E. Hinton
- Yann LeCun
- Yoshua Bengio
- Karen Simonyan
- Diederik P. Kingma
- Ilya Sutskever
- David G. Lowe
Who trusts Qixiang Ye overall
- Xuejian Rong
- Baoguang Shi
- Xiang Bai
- Diego Marcos
- Chenggang Clarence Yan
- Minghui Liao
- Gong Cheng
- Cong Yao
- Shangbang Long
- Huizhong Chen
Papers from Qixiang Ye's perspective
Ordered by how strongly the researchers Qixiang Ye 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers (2022) — 439 citations
- Oriented Response Networks (2017) — 283 citations
- Weakly Supervised Instance Segmentation Using Class Peak Response (2018) — 294 citations
- Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification (2017) — 970 citations
- Learning to Match Anchors for Visual Object Detection (2021) — 235 citations
- Text Detection and Recognition in Imagery: A Survey (2015) — 755 citations
- Conformer: Local Features Coupling Global Representations for Visual Recognition (2021) — 757 citations
- FreeAnchor: Learning to Match Anchors for Visual Object Detection (2019) — 328 citations
- C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection (2019) — 258 citations
- Towards Optimal Structured CNN Pruning via Generative Adversarial Learning (2019) — 578 citations
- Component Divide-and-Conquer for Real-World Image Super-Resolution (2020) — 385 citations
- Orientation robust object detection in aerial images using deep convolutional neural network (2015) — 386 citations
- SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images (2019) — 316 citations
- YOLOv12: Attention-Centric Real-Time Object Detectors (2025) — 1,926 citations
- VMamba: Visual State Space Model (2024) — 2,640 citations
- HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling (2022) — 46 citations
- Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation (2021) — 60 citations
- Prototype Mixture Models for Few-shot Semantic Segmentation (2020) — 480 citations
- Scale Match for Tiny Person Detection (2019) — 349 citations
- Min-Entropy Latent Model for Weakly Supervised Object Detection (2018) — 230 citations