Skip to content
View s0sasaki's full-sized avatar

Block or report s0sasaki

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
s0sasaki/README.md

Fueled by a passion for math and computer science, I've tackled diverse projects in machine learning, computer vision, algorithms, and programming. Here are some highlights from my work in computer vision, followed by lower-level programming projects like compilers:

Highlights from My Projects
Vision Question Answering (VQA)
VQA is a task in computer vision that involves answering questions about an image. My work focused on Bottom-Up and Top-Down Attention Mechanism, optimizing object-level attention. The improved implementation achieved 63.61% accuracy, surpassing the original.
Flowers
Vision Language Navigation (VLN)
VLN is a task where agents learn to navigate following natural language instructions. In contrast to VQA, VLN model encodes texts and outputs actions while observing the new information. I explored a robot navigation in multi-floor buildings.
Flowers
Adaptive Zoom Mechanism for Vision Language Navition
I integrated an Adaptive Zoom mechanism into VLN, enabling agents to locate large landmarks with wide-FOV vision and identify smaller or distant objects with magnified vision.
Flowers
Trajectory Encoding for Vision Language Navition
I designed a model that leverages pre-exploration information in 3D buildings, achieving a 45.8% success rate. (The state of the art was 46.5% at the time.)
Flowers
Simultaneous Localization And Mapping (SLAM)
SLAM enables robotic mapping and navigation by constructing an environment map and tracking the robot's location simultaneously. I utilized a particle filter model to create a texture map from a differential-drive robot's two-minute activity in a building.
Flowers
Corner Detection and Sparse Stereo Matching with Epipolar Geometry
Employing epipolar geometry, the geometry of stereo vision, I detected and identified corresponding features in images taken from two distinct camera positions.
Flowers
Photometric Stereo for Surface Reconstruction and Phong Illumination for Surface Rendering
I used photometric stereo, a computer vision technique, to estimate surface normals under varying lighting conditions. The reconstructed surface was then rendered using the Phong reflection model, a computer graphics method for calculating local illumination.
Flowers

Pinned Loading

  1. x86compiler x86compiler Public

    A compiler for x86-64 with an SLR parser.

    Python 10 2

  2. ExecutableBlockchain ExecutableBlockchain Public

    A blockchain influenced by Ethereum, featuring a Forth compiler.

    C++ 3 1

  3. ComputerVisionTechniques ComputerVisionTechniques Public

    Homography, Photometric stereo, Phong illumination rendering, Corner detection, Epipolar geometry, Sparse stereo matching, SLAM, etc

    Jupyter Notebook 15 4

  4. MemoryAllocator MemoryAllocator Public

    Simple malloc, calloc, realloc, and free.

    C 8 2