CVPR 2021, The 3rd Tutorial on

Learning Representations via Graph-structured Networks

Slides and recorded videos are provided in this webpage.
Sunday morning (9AM - 12:30PM PDT), June 20, 2021

[YouTube Recordings]



Recent years have seen a dramatic rise in the adoption of convolutional neural networks (ConvNets) for a myriad of computer vision tasks. The structure of convolution is proved to be powerful in numerous tasks to capture correlations and abstract conceptions out of image pixels. However, ConvNets are also shown to be deficient in modeling quite a few properties when computer vision works towards more difficult AI tasks. These properties include pairwise relation, global context and the ability to process irregular data beyond spatial grids.

An effective direction is to reorganize the data to be processed with graphs according to the task at hand, while constructing network modules that relate and propagate information across the visual elements within the graphs. We call these networks with such propagation modules as graph-structured networks. In this tutorial, we will introduce a series of effective graph-structured networks, with their applications in visual recognition, video analysis, 3D scene understanding, intuitive physics, and reinfocement learning.


9:00 - 9:30 AM PDT. Swin Transformer and Five Reasons to use Transformer/Attention in Computer Vision. Han Hu. [recorded video]

9:30 - 10:00 AM PDT. Beyond Bounding-Box Structure Detection with Transformers. Zhuowen Tu. [recorded video]

10:00 - 10:30 AM PDT. Structured information for the built environment: Why, What, and How. Iro Armenni. [recorded video]

10:30 - 11:00 AM PDT. Break.

11:00 - 11:30 AM PDT. Graph-Structured Networks for Physical Inference and Model-Based Control. Yunzhu Li. [recorded video]

11:30 - 12:00 PM PDT. Learning Long-term Video Prediction with Graph Networks. Xiaolong Wang. [recorded video]

12:00 - 12:30 PM PDT. GNN for Compositional Generalizability in Vision-based Policy Learning. Hao Su. [recorded video]

Please contact Xiaolong Wang if you have question. The webpage template is by the courtesy of awesome Georgia.