diff --git a/index.bs b/index.bs
index 792d120c..9cc45b7b 100644
--- a/index.bs
+++ b/index.bs
@@ -6,7 +6,7 @@ Status: CG-DRAFT
Group: webml
URL: https://webmachinelearning.github.io/webnn/
Editor: Your Name, Your Company http://example.com/your-company, your-email@example.com, http://example.com/your-personal-website
-Abstract: A dedicated API for neural network inference hardware acceleration.
+Abstract: This document describes a dedicated low-level API for neural network inference hardware acceleration.
Repository: https://github.com/webmachinelearning/webnn
@@ -18,5 +18,338 @@ Introduction here.
Use cases {#usecases}
=====================
-Use cases here.
+## High-Level Use Cases ## {#usecases-highlevel}
+This section illustrates application-level use cases for neural network
+inference hardware acceleration. All applications in those use cases can be
+built on top of pre-trained deep neural network (DNN) models.
+
+### Person Detection ### {#usecase-person-detection}
+
+A user opens a web-based video conferencing application, but she temporarily
+leaves from her room. The application is watching whether she is in front of her
+PC by using object detection (for example, using object detection approaches
+such as [[SSD]] or [[YOLO]] that use a single DNN) to detect regions in a camera
+input frame that include persons.
+
+When she comes back, the application automatically detects her and notifies
+other online users that she is active now.
+
+### Semantic Segmentation ### {#usecase-segmentation}
+
+A user joins a teleconference via a web-based video conferencing application at
+her desk since no meeting room in her office is available. During the
+teleconference, she does not wish that her room and people in the background are
+visible. To protect the privacy of the other people and the surroundings, the
+application runs a machine learning model such as [[DeepLabv3+]] or
+[[MaskR-CNN]] to semantically split an image into segments and replaces
+segments that represent other people and background with another picture.
+
+### Skeleton Detection ### {#usecase-skeleton-detection}
+
+A web-based video conferencing application tracks a pose of user's skeleton by
+running a machine learning model, which allows for real-time human pose
+estimation, such as [[PoseNet]] to recognize her gesture and body language. When
+she raises her hand, her microphone is automatically unmuted and she can start
+speaking on the teleconference.
+
+### Face Recognition ### {#usecase-face-recognition}
+
+There are multiple people in the conference room and they join an online meeting
+using a web-based video conferencing application. The application detects faces
+of participants by using object detection (for example, using object detection
+approaches such as [[SSD]]) and checks whether each face was present at the
+previous meeting or not by running a machine learning model such as [[FaceNet]],
+which verifies whether two faces would be identical or not.
+
+### Facial Landmark Detection ### {#usecase-facial-landmarks}
+
+A user wants to find new glasses that beautifully fits her on an online glasses
+store. The online store offers web-based try-on simulator that runs a machine
+learning model such as Face Alignment Network [[FAN]] to detect facial landmarks
+like eyes, nose, mouth, etc. When she chooses a pair of glasses, the simulator
+properly render the selected glasses on the detected position of eyes on her
+facial image.
+
+### Style Transfer ### {#usecase-style-transfer}
+
+A user is looking for cosmetics on an online store and wondering which color may
+fit her face. The online store shows sample facial makeup images of cosmetics,
+and offers makeup simulator that runs a machine learning model like
+[[ContextualLoss]] or [[PairedCycleGAN]] to transfer the makeup style of the
+sample makeup image to her facial image. She can check how the selected makeup
+looks like on her face by the simulator.
+
+### Super Resolution ### {#usecase-super-resolution}
+
+A web-based video conferencing is receiving a video stream from its peer, but
+the resolution of the video becomes lower due to network congestion. To prevent
+degradation of the perceived video quality, the application runs a machine
+learning model for super-resolution such as [[SRGAN]] to generate
+higher-resolution video frames.
+
+### Image Captioning ### {#usecase-image-captioning}
+
+For better accessibility, a web-based presentation application provides
+automatic image captioning by running a machine learning model such as
+[[im2txt]] which predicts explanatory words of the presentation slides.
+
+### Machine Translation ### {#usecase-translation}
+
+Multiple people from various countries are talking via a web-based real-time
+text chat application. The application translates their conversation by using a
+machine learning model such as [[GNMT]] or [[OpenNMT]], which translates every
+text into different language.
+
+### Emotion Analysis ### {#usecase-emotion-analysis}
+
+A user is talking to her friend via a web-based real-time text chat application,
+and she is wondering how the friend feels because she cannot see the friend's
+face. The application analyses the friend's emotion by using a machine learning
+model such as [[DeepMoji]], which infers emotion from input texts, and displays
+an emoji that represents the estimated emotion.
+
+### Video Summarization ### {#usecase-video-summalization}
+
+A web-based video conferencing application records received video streams, and
+it needs to reduce recorded video data to be stored. The application generates
+the short version of the recorded video by using a machine learning model for
+video summarization such as [[Video-Summarization-with-LSTM]].
+
+## Low-Level Use Cases ## {#usecases-lowlevel}
+
+This section collects API-level use cases for a dedicated low-level API for
+neural network inference hardware acceleration. It is expected that Machine
+Learning frameworks will be key consumers of the Web Neural Network API (WebNN
+API) and the low-level details exposed through the WebNN API are abstracted out
+from typical web developers. However, it is also expected that web developers
+with specific interest and competence in Machine Learning will want to interface
+with the WebNN API directly instead of a higher-level ML framework.
+
+### Custom Layer ### {#usecase-custom-layer}
+
+A web application developer wants to run a DNN model on the WebNN API. However,
+she has found that some of activation functions like [[LeakyReLU]], [[ELU]],
+etc. are not included in the WebNN API. To address this issue, she constructs
+custom layers of the additional activation functions on top of the WebNN API.
+Note that the scope of custom layers may include convolution, normalization,
+etc. as well as activation.
+
+### Network Concatenation ### {#usecase-network-concat}
+
+A web application uses a DNN model, and its model data of upper convolutional
+layers and lower fully-connected layers are stored in separate files, since
+model data of the fully-connected layers are periodically updated due to fine
+tuning at the server side.
+
+Therefore, the application downloads both partial model files at first and
+concatenates them into a single model. When the model is updated, the
+application downloads fine-tuned part of the model and replace only the
+fully-connected layers with it.
+
+### Performance Adaptation ### {#usecase-perf-adapt}
+
+A web application developer has a concern about performance of her DNN model on
+mobile devices. She has confirmed that it may run too slow on mobile devices
+which do not have GPU acceleration. To address this issue, her web application
+refers to the WebNN API to confirm whether acceleration is available or not, so
+that the application can display the warning for devices without acceleration.
+
+After several weeks, she has developed a tiny DNN model that can even run on
+CPU. In order to accommodate CPU execution, she modifies the application
+so that the application loads the tiny model in the case of CPU-only devices.
+
+