CSE 455 Project Ideas

There are two options for the project, which will take roughly four weeks:

  1. Implement a state-of-the-art research paper from a recent computer vision conference or journal (CVPR, ICCV, ECCV or vision parts of NIPS, ICML, SIGGRAPH, SIGGRAPH Asia etc.).
  2. Complete a short research project (more fun!)

You can devise your own project from scratch, or use one of the ideas suggested below.

Option 1

Start by searching through recent computer vision conference proceedings or journal articles, and choosing a paper that interests you. The premiere vision conferences are ICCV, CVPR, ECCV. The premiere journals are TPAMI and IJCV. Sometimes computer vision papers also appear in graphics/machine learning conferences such as NIPS, ICML, SIGGRAPH, SIGGRAPH Asia. We recommend starting with the most recent years, i.e., CVPR 2019, ICCV 2019, ECCV 2018 etc. Most of the papers and project web sites are linked online from here. You should select a paper that is appropriate for a four-week project, i.e., it should be more involved than one of the homeworks. Our expectation is that you will implement the method yourself rather than using any code that is available online.

Option 2

For this option we'd like you to do a research project with some novelty, i.e., something that no one has published before. Naturally we're not expecting PhD-level research in this amount of time, but since four of you will be working together you should be able to come up with exciting results :)

Following are some examples of what we have in mind.

How ambitious/difficult should your project be? Each team member should count on committing substantially more effort than on the class homeworks.

Project Ideas for Option 2

Here are several ideas that would be appropriate for final projects. Feel free to choose variations of these or to devise your own problems that are not on this list. You can either leverage deep learning or not, depending on your skill set and target. We're happy to meet with you to discuss any of these (or other) project ideas in more detail.

Face/Pedestrian/Vehicle recognition. These are perhaps the most widely used object recognition scenarios. You will build your own detection framework and try it on a standard benchmark dataset. Example research problems include how to balance between speed and accuracy, and how object scale, density or lightning codition affect the detection resutls etc.

Tracking by detection. One classic approach to track a moving object is to remember it and then find it. The object model and tracking status are updated contineously along the video frames, so that appearance change, occlusion status etc. can be handeled. You will build an online object tracking framework and perhaps give a demo during our poster session.

Object re-identification. It is common that an object appears in different cameras with different viewpoint, zoom or lighting conditions. Object re-identification exactly finds such matches and enables many surveillance applications such as finding the criminals. You will design your object re-id pipeline and compare with several state-of-the-art approaches.

Product description. Given a product image, can you generate a paragraph describing the object? You can also develop projects that connect computer vision and natural language processing.

License plate recognition serves as a critical part in many surveillance applications. A typical routine for LPR includes license plate localization, character segmentation and character recognition. However, building a robust and fast LPR system is still very challenging. You will implement your own LPR approach and may try something different. You should demo your LPR system during the poster session.

Human pose/hand gesture recognition is popular in many medical/HCI applications. The basic idea of such articulated object recognition however is similar - finding object parts and their best combination.

Instance recognition. Object instance recognition attracts increasing interest recently; it tries to recognize different object instances that belong to the same class, for example, different kinds of dogs, birds or cars. Practially this is useful for warehouse management, robotics, traffic surveillance etc. You will read state-of-the-art papers, work on a standard instance recognition dataset and try your approach.

Cancer biopsy diagnosis. The task is to classify a given region of interest (ROI) from a whole slide biopsy to one of the four diagnostic categories: benign, atypia, DCIS and invasive. There are 428 ROIs marked and diagnosed by expert pathologists. ROIs have different sizes and shapes but each has only one diagnostic label. You can use different approaches to overcome size differences: sliding windows, resizing etc.