Quiz 4 Topic List (tentative)

  1. Introduction to Recognition

  2. Machine Learning Basics

    1. Nearest neighbors

    2. Support Vector Machines

    3. Support Vector Machines with Kernels

    4. Bias variance trade-off

    5. Model Selection

  3. Feature Engineering

  4. Deep Neural Networks

    1. Back-propagation

    2. Development Cycle

    3. Hyper-parameters and their affect

  5. Image Classification

    1. Architectural Constructs and their Properties

      1. Convolutions

      2. Pooling

      3. Fully-connected Layers

      4. Non-linearities

      5. Dropout

      6. Batch-normalization

      7. Residual Connections

      8. Attention

    2. Popular Architectures

    3. Using ImageNet pre-trained models for vision tasks

  6. Segmentation

    1. Tasks and Metrics

      1. Bottom-up segmentation

      2. Semantic segmentation

      3. Instance segmentation

    2. Shift and Stitch, Dilated Convolutions

    3. Skip-connections

    4. Encoder-decoder architectures

  7. Detection

    1. Problem definition

    2. Metrics

    3. Basics of classical approaches

    4. CNN based Detectors

      1. R-CNN

      2. Fast R-CNN

      3. Faster R-CNN

      4. Single-shot Detectors

      5. Feature Pyramid Networks

    5. ROI Pooling

    6. Non-Maximum Suppression

    7. Region Proposal Networks

    8. Instance Segmentation

      1. Metrics

      2. RoI Align vs RoI Pooling