Pinned Repositories
eth-digihum-t2
The Student Cluster Guide, tutorial 2 of ETH's Digital Humans 2024 course.
fer-tar
"Essays are a Fickle Thing", a project done as part of the "ID222452 Text Analysis and Retrieval" course by Prof. Jan Šnajder at UniZG-FER
focal-loss-against-heuristics
Using Focal Loss to Fight Shallow Heuristics: An Empirical Analysis of Modulated Cross-Entropy in Natural Language Inference
pips
This is a somewhat refactored PIPs with a batched chained prediction. It provides the setup to evaluate PIPs on the TAP-Vid dataset. Also, a basic integration of the point trajectories from PIPs (or RAFT) with Sam is provided, to get video segmentation.
svg-latte
Exploring useful latent representation for SVGs
vo2021
Robustness of Embodied Point Navigation Agents: Codebase of the VO2021 agent
sam-pt
SAM-PT: Extending SAM to zero-shot video segmentation with point-based tracking.
m43's Repositories
m43/eth-digihum-t2
The Student Cluster Guide, tutorial 2 of ETH's Digital Humans 2024 course.
m43/focal-loss-against-heuristics
Using Focal Loss to Fight Shallow Heuristics: An Empirical Analysis of Modulated Cross-Entropy in Natural Language Inference
m43/svg-latte
Exploring useful latent representation for SVGs
m43/fer-tar
"Essays are a Fickle Thing", a project done as part of the "ID222452 Text Analysis and Retrieval" course by Prof. Jan Šnajder at UniZG-FER
m43/pips
This is a somewhat refactored PIPs with a batched chained prediction. It provides the setup to evaluate PIPs on the TAP-Vid dataset. Also, a basic integration of the point trajectories from PIPs (or RAFT) with Sam is provided, to get video segmentation.
m43/vo2021
Robustness of Embodied Point Navigation Agents: Codebase of the VO2021 agent
m43/AutoBots
m43/davis2016-davis2017-davis2019-evaluation
Evaluation Framework for DAVIS 2016/2017/2019 Semi-supervised and Unsupervised used in the DAVIS Challenges
m43/deepsvg
[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.
m43/epfl-ada-2021
Materials for Applied Data Analysis CS-401, Fall 2021
m43/fer-deep-learning
Solutions to lab exercises in the "ID222565 Deep Learning" course at UniZG-FER
m43/ada-2021-project-hivemind
ada-2021-project-hivemind created by GitHub Classroom
m43/co-tracker
CoTracker is a model for tracking any point (pixel) on a video.
m43/fer-java-course
This repository contains solutions to lab exercises in the Introduction to Java Programming Language course at FER.
m43/fer-nos
This repository contains solutions to lab exercises in the Advanced Operating Systems course at FER.
m43/fer-opp
m43/fer-scripting
This repository contains solutions to lab exercises in the Scripting Languages course at FER.
m43/fer-seminar-dipl
This repository contains evaluation scripts used for Masters Seminar course at FER.
m43/few-shot-cnn-learning
Few-shot learning of deep convolutional models
m43/kth-ml
Solutions to lab exercises in the Machine Learning course at KTH.
m43/kubric
A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation masks, depth maps, and optical flow.
m43/Personalize-SAM
PerSAM-F with HQ-SAM
m43/Python-RVO2
Optimal Reciprocal Collision Avoidance, Python bindings
m43/sam-hq
Fork of HQ-SAM that renames the package from segment_anything to segment_anything_hq. This is done to avoid interference with the original SAM package.
m43/tapnet
m43/Trajectron_plus_plus
Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).
m43/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
m43/ucu-mlab
Robustness of Embodied Point Navigation Agents: Codebase of the UCU Mlab agent and the baselines
m43/XDEnsembles
Robustness via Cross-Domain Ensembles
m43/youtubevos-cocoapi
COCO API Customized for YouTubeVIS evaluation