Git Product home page Git Product logo

video_motion_synthetic3's Introduction

Endpoint Error Results Summary

Exp 01 02 03 04 05 06 07 08 09 10 11 12 Comp Eval Note
001 0.06 0.06 0.14 0.13 0.14 0.12 0.13 0.12 0.24 0.24 0.26 0.23 ---- ---- The simplest baseline
002 0.03 0.03 0.12 0.13 0.09 0.08 0.07 0.06 0.23 0.25 0.18 0.16 001 Good Moving pixel occlude static pixel
003 0.04 0.03 0.11 0.12 0.08 0.07 0.29 0.25 0.32 0.37 0.11 0.23 002 Bad New appear pixel not in loss
004 0.03 0.03 0.11 0.11 0.08 0.06 0.04 0.04 0.18 0.23 0.10 0.11 002 Good Old pixel loss divided by total number of old pixels
005 0.04 0.03 0.14 0.14 0.10 0.12 0.06 0.06 0.24 0.22 0.16 0.22 004 Bad New appear and occlude location both not in loss
006 0.08 0.05 0.15 0.14 0.14 0.12 0.08 0.06 0.25 0.21 0.20 0.17 004 Bad Neural net predict disappear
007 0.03 0.03 0.11 0.12 0.08 0.07 0.06 0.06 0.19 0.23 0.14 0.15 004 Bad Use avearge value at occlusion

| 008 | 0.05 | 0.05 | 0.14 | 0.13 | 0.12 | 0.12 | 0.08 | 0.18 | 0.24 | 0.25 | 0.22 | 0.35 | 004 | Bad | Predict relative depth | | 009 | | | | | | | | | | | | | | | Predict relative depth using only one image | | 010 | | | | | | | | | | | | | | | Add segmentation temporal consistency loss |

| 009 | | | | | | | | | | | | | | | Decompose x and y | | 010 | | | | | | | | | | | | | | | Wider network, proves exp008 is bad | | 011 | | | | | | | | | | | | | | | Wider network | | 014 | | | | | | | | | | | | | | | Predict relative depth | | 015 | | | | | | | | | | | | | | | Old pixel loss divided by total number of old pixels | | 016 | | | | | | | | | | | | | | | Bidirectional model | | 017 | | | | | | | | | | | | | | | Add a few more layers at the bottom of neural net | | 018 | | | | | | | | | | | | | | | Predict depth using only one image | | 019 | | | | | | | | | | | | | | | Add segmentation temporal consistency loss | | 020 | | | | | | | | | | | | | | | Bidirectional model | | 000 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 000 | | |

Exp 01 02 03 04 05 06 07 08 09 10 11 12 Comp Eval Note
The simplest baseline
Moving pixel occlude static pixel
Old pixel loss divided by total number of old pixels
Old pixel loss divided by total number of old pixels
Wider network
Old pixel loss divided by total number of old pixels
Extra loss for total number of new and conflicting pixels

Take Home Message

  • We should use: new appear pixel not in loss, loss divided by total number of old pixels

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.