Seminar 2021
![](https://www.google.com/images/icons/product/drive-32.png)
Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
YOLOv3: An Incremental Improvement
![](https://www.google.com/images/icons/product/drive-32.png)
Fast AutoAugment
![](https://www.google.com/images/icons/product/drive-32.png)
YOLO9000: Better, Faster, Stronger
![](https://www.google.com/images/icons/product/drive-32.png)
Efficient-CapsNet: Capsule Network with Self-Attention Routing
![](https://www.google.com/images/icons/product/drive-32.png)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
![](https://www.google.com/images/icons/product/drive-32.png)
You Only Look Once: Unified, Real-Time Object Detection
![](https://www.google.com/images/icons/product/drive-32.png)
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
![](https://www.google.com/images/icons/product/drive-32.png)
Knowledge Distillation: A Survey
![](https://www.google.com/images/icons/product/drive-32.png)
Going Deeper with Convolutions
![](https://www.google.com/images/icons/product/drive-32.png)
Language Models are Few-Shot Learners
![](https://www.google.com/images/icons/product/drive-32.png)
Attention Is All You Need
![](https://www.google.com/images/icons/product/drive-32.png)
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
Deep Residual Learning for Image Recognition
![](https://www.google.com/images/icons/product/drive-32.png)
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
![](https://www.google.com/images/icons/product/drive-32.png)
Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation
![](https://www.google.com/images/icons/product/drive-32.png)
Purifying Gaze Feature for Generalizable Gaze Estimation
![](https://www.google.com/images/icons/product/drive-32.png)
Very Deep Convolutional Networks for Large-Scale Image Recognition
![](https://www.google.com/images/icons/product/drive-32.png)
Meta Pseudo Labels
![](https://www.google.com/images/icons/product/drive-32.png)
Visualizing and Understanding Convolutional Networks
![](https://www.google.com/images/icons/product/drive-32.png)
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
![](https://www.google.com/images/icons/product/drive-32.png)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
![](https://www.google.com/images/icons/product/drive-32.png)
ImageNet Classification with Deep Convolutional Neural Networks
![](https://www.google.com/images/icons/product/drive-32.png)
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
Deep Face Recognition: A Survey
![](https://www.google.com/images/icons/product/drive-32.png)
Path Aggregation Network for Instance Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning
![](https://www.google.com/images/icons/product/drive-32.png)
Transformer: Attention is all you need
![](https://www.google.com/images/icons/product/drive-32.png)
PolyTransform: Deep Polygon Transformer for Instance Segmentation
![](https://www.google.com/images/icons/product/drive-32.png)
SSD:Single Shot MultiBox Detector