Modeling Visual Data Structures for Semantic Segmentation

Talk by Tianfei ZHOU

Sep 14, 2023 Thursday


Semantic segmentation aims at predicting one semantic category for each pixel in an image. It is a vital step towards intelligent scene understanding. With the renaissance of connectionism, rapid progress has been made in the field. However, the vast majority of modern segmentation models is pure data-driven, and ignores the structured nature of visual data. In this talk, I will first introduce a pixel-wise metric learning paradigm for semantic segmentation, which explicitly explores cross-image pixel relations in large-scale training dataset for semantic segmentation learning. Moreover, I will present our investigations towards hierarchy-aware segmentation, which aims to better reflect the structured nature of our visual world and echo the hierarchical reasoning mode of human visual perception.


September 14th, 2023, Thursday

16:30 – 17:20


Rm101, E1


628 334 1826 (PW: 234567)

Bilibili Live:

ID: 30748067

Speaker Bio:

Dr. Tianfei Zhou

Researcher, Computer Vision Lab, ETH

Dr. Tianfei Zhou is currently a researcher at Computer Vision Lab, ETH Zurich. He obtained his Ph.D. degree from Beijing Institute of Technology and has worked as a researcher at Lenovo Research and Inception Institute of Artificial Intelligence. His general research interests are computer vision and machine learning, and specifically his recent focus is on building customizable multi-modal large models that can understand and follow humans’ intent. He has published over 50 academic papers in top-tier journals conferences, such as IEEE TPAMI, ICML, ICLR, CVPR, and MICCAI. As the first authorship, he received the Best Paper Award at MICCAI 2022. He has won 5 champions in international academic competitions in conjunction with, e.g., CVPR and ICCV. He was a Guest Editor at IEEE TCSVT.