Zhaoxiang Zhang

Computer Vision and Pattern Recognition · 2008–2023

A researcher in the Science corpus: 17 papers; trusts 1,716 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 530 citations into it; #5,852 of 21,575 by trust here, #3,645 by citations
• Artificial Intelligence — 277 citations into it; #19,958 of 35,345 by trust here, #7,945 by citations
• Model Architectures — 72 citations into it
• Aerospace Engineering — 51 citations into it
• Generative Models — 41 citations into it
• Evaluation & Benchmarks — 35 citations into it
• Representation & Self-Supervised Learning — 35 citations into it
• Signal Processing — 35 citations into it

Whom Zhaoxiang Zhang trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Karen Simonyan
6. Diederik P. Kingma
7. Yoshua Bengio
8. Christian Szegedy
9. Yann LeCun
10. David G. Lowe

Who trusts Zhaoxiang Zhang overall

1. Vishwanath A. Sindagi
2. Zheng Xu
3. Daniel Oñoro-Rubio
4. Viresh Ranjan
5. Beomyoung Kim
6. Mufti Mahmud
7. Dina Bashkirova
8. Mateusz Budnik
9. Erik J. Bekkers
10. Ke Chen

Papers from Zhaoxiang Zhang's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Scale-Aware Trident Networks for Object Detection (2019) — 1,060 citations
• DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer (2017) — 236 citations
• GIFT: A Real-Time and Scalable 3D Shape Search Engine (2016) — 294 citations
• Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation (2020) — 218 citations
• Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection (2008) — 445 citations
• Give Me Your Trained Model: Domain Adaptive Semantic Segmentation without Source Data (2021) — 9 citations
• Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection (2019) — 58 citations
• Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition (2017) — 304 citations
• Driving Into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving (2023) — 358 citations
• The Devil Is in the Details: Window-based Attention for Image Compression (2022) — 347 citations
• BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision (2022) — 460 citations
• Embracing Single Stride 3D Object Detector with Sparse Transformer (2021) — 351 citations
• Image Inpainting by End-to-End Cascaded Refinement With Mask Awareness (2021) — 140 citations
• Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression (2021) — 356 citations
• Boosting Decision-Based Black-Box Adversarial Attacks with Random Sign Flip (2020) — 78 citations
• Spatiotemporal distilled dense-connectivity network for video action recognition (2019) — 53 citations
• Dynamic Multi-Task Learning with Convolutional Neural Network (2017) — 35 citations