Beyond Convolutional Neural Networks...

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Beyond Convolutional Neural Networks (CNN) Industrial AI Lab. Prof. Seungchul Lee

Transcript of Beyond Convolutional Neural Networks...

Page 1: Beyond Convolutional Neural Networks (CNN)i-systems.github.io/HSE545/iAI/DL/topics/06_Modern_CNN/02_Beyond_CNN.pdf · Beyond Convolutional Neural Networks (CNN) Industrial AI Lab.

Beyond Convolutional Neural Networks(CNN)

Industrial AI Lab.

Prof. Seungchul Lee

Page 2: Beyond Convolutional Neural Networks (CNN)i-systems.github.io/HSE545/iAI/DL/topics/06_Modern_CNN/02_Beyond_CNN.pdf · Beyond Convolutional Neural Networks (CNN) Industrial AI Lab.

Beyond Classification

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An Architecture for Many Applications

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An Architecture for Many Applications

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Beyond Classification

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Localization

• Single object per image

• Predict coordinates of a bounding box (x, y, w, h)

• Evaluate via Intersection over Union (IoU)

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Localization as Regression

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Localization as Regression

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Classification + Localization

• Use a pre-trained CNN on ImageNet (ex. ResNet)

• The "localization head" is trained separately with regression

• Possible end-to-end fine-tuning of both tasks

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Object Detection

• We don't know in advance the number of objects in the image.

• Object detection relies on object proposal and object classification– Object proposal: find regions of interest (ROIs) in the image

– Object classification: classify the object in these regions

• Two main families:– A grid in the image where each cell is a proposal (SSD, YOLO)

– Region proposal (SPP, MultiBox, Faster RCNN, ⋯)

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Object Detection with R-CNNs

• R-CNN: – Find regions that we think have objects.

– Use CNN to classify

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Segmentation

• Output a class map for each pixel (here: dog vs background)

• Instance segmentation: specify each object instance as well (two dogs have different instances)

• This can be done through object detection + segmentation

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Semantic Segmentation: FCNs

• FCN: Fully Convolutional Network– Network designed with all convolutional layers, with down-sampling and up-sampling operations

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Image Captioning using RNNs

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Image Captioning using RNNs

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