Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement

Last update: Nov 24, 2022

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Overview

Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement

In this project, we proposed a Domain Disentanglement Faster-RCNN (DDF) for cross-domain object detection, from the view of feature disentanglement.

The implementations are for our paper published in IEEE Transactions on Multimedia:

Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement

**The overall code will be released later on.

Visual Examples of the features

Our proposed framework: Domain Disentanglement Faster-RCNN (DDF)

Contact

Please contact Dongnan Liu ([email protected]) regarding any issues.

License

DDF is released under the MIT license. See LICENSE for additional details.

Owner

Dongnan Liu @ USYD [email protected]

GitHub Repository

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Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement

Related tags

Overview

Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement

Visual Examples of the features

Our proposed framework: Domain Disentanglement Faster-RCNN (DDF)

Contact

License

Owner

Implementation of "Deep Implicit Templates for 3D Shape Representation"

A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.

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Framework for evaluating ANNS algorithms on billion scale datasets.

Collection of machine learning related notebooks to share.

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An unofficial personal implementation of UM-Adapt, specifically to tackle joint estimation of panoptic segmentation and depth prediction for autonomous driving datasets.

Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

Hierarchical Aggregation for 3D Instance Segmentation (ICCV 2021)

Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other

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Code for Universal Semi-Supervised Semantic Segmentation models paper accepted in ICCV 2019

Implementation of "A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement" by pytorch

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PyTorch implementation of the Flow Gaussian Mixture Model (FlowGMM) model from our paper

Final project code: Implementing MAE with downscaled encoders and datasets, for ESE546 FA21 at University of Pennsylvania

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