ECS 269: Visual Recognition

ECS 269
Visual Recognition
Effective Term
2018 Fall Quarter
Learning Activities
Lecture: 3 hours
Discussion: 1 hour
Graduate seminar course on computer vision with an emphasis on object recognition, activity recognition, and scene understanding.
ECS 171 or ECS 174; or equivalent.

Summary of Course Content:

The course will survey papers in the following areas:

  • Image classification
  • Object detection
  • Unsupervised/Weakly-supervised visual discovery
  • Segmentation
  • Human-in-the-loop learning
  • Activity recognition
  • Human pose
  • Attributes
  • Image search and mining
  • Language and images
  • Big data
  • First-person vision

Illustrative Reading:

A selection of research papers updated every year. Examples include:

ImageNet classification with deep convolutional neural networks. A. Krizhevsky, I. Sutskever, and G. E. Hinton. NIPS 2012.

Deep Residual Learning for Image Recognition. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. CVPR 2016.

Potential Course Overlap:
There is no significant overlap with any 1XX or 2XX level course.  There may be a lecture or two in ECS 270 and 271 that overlap in their coverage of neural networks, but the focus will be different as 269 will teach neural network algorithms specific to visual recognition whereas 270 and 271 will focus on more general-purpose neural networks.

Course Category