/seminar

	<tr> 
		<td>	Dec. 20, 2018, 3 p.m. </td>
		<td>	ETF-C106 </td>
		<td>	Tianshu Hu </td>
  <td>	Tianshu Hu </td>
  <td>	<a href="/en/activities/cvl-seminar/detail/226/">Mixed-Supervised Domain Adaptive Semantic Segmentation</a>,<a href="">slides</a> </td>
	</tr>

<tr> 
		<td>	Dec. 20, 2018, 3 p.m. </td>
		<td>	ETF-C106 </td>
		<td>	Tianshu Hu </td>
  <td>	Tianshu Hu </td>
  <td>	<a href="/en/activities/cvl-seminar/detail/226/">Mixed-Supervised Domain Adaptive Semantic Segmentation</a>,<a href="">slides</a> </td>
	</tr>

<tr> 
		<td>	Dec. 20, 2018, 3 p.m. </td>
		<td>	ETF-C106 </td>
		<td>	Tianshu Hu </td>
  <td>	Tianshu Hu </td>
  <td>	<a href="/en/activities/cvl-seminar/detail/226/">Mixed-Supervised Domain Adaptive Semantic Segmentation</a>,<a href="">slides</a> </td>
	</tr>
	
	
	
</tbody>
Date and time Place Speaker Topic Papers

#Paper-Reading-Seminar-ISIS-UvA

#1. Topic: Pooling Mechanism Date: 10-12-2018, 4:00-5:00 pm, C3.101 Lecturer: Zenglin Shi Slides: Papers:

  1. Faraz Saeedan, Nicolas Weber, Michael Goesele and Stefan Roth. Detail-Preserving Pooling in Deep Networks. CVPR, 2018 (Oral).
  2. Y-Lan BoureauA, Jean Ponce and Yann LeCun. Theoretical Analysis of Feature Pooling in Visual Recognition. ICML, 2010.
  3. ZenglinShi, YangdongYe and Yunpeng Wu. Rank-based pooling for deep convolutional neural networks. Neural Networks,2016.

#2. Topic: Transfer Learning Date: 10-12-2018, 4:00-5:00 pm, C3.101 Lecturer: Riaan Zoetmulder Slides: Papers:

  1. Amir R. Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik and Silvio Savarese. Taskonomy: Disentangling Task Transfer Learning. CVPR, 2018 (Best Paper). [pdf]

#3 Topic: Model Pretraining Date: 17-12-2018, 4:00-5:00 pm, C3.101 Lecturer: William Thong Slides: Papers:

  1. Kaiming He, Ross Girshick and Piotr Dollar. Rethinking ImageNet Pre-training. Arxiv, 2018.

#4 Topic: Understanding Convolutions Date: 17-12-2018 and 07-01-2019 4:00-5:00 pm, C3.101 Lecturer: Tao Hu, Shuo Chen Slides: Papers:

  1. J. Dai et al. Deformable Convolutional Networks. ICCV, 2017.
  2. Xizhou Zhu, Han Hu, Stephen Lin and Jifeng Dai. Deformable ConvNets v2: More Deformable, Better Results2. Arxiv, 2018.
  3. Tianyi Wu et al. Tree-structured Kronecker Convolutional Network for Semantic Segmentation. Arxiv, 2018.

#5 Topic: Normalization Date: 07-01-2019, 4:00-5:00 pm, C3.101 Lecturer: Zenglin Shi Slides: Papers:

  1. Yuxin Wu, Kaiming He. Group Normalization. ECCV, 2018.
  2. Sergey Ioffe. Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models. NIPS, 2017
  3. Sergey Ioffe and Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. ICML, 2015.

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