Overview
Discover the "Multiple Distribution Shift - Aerial" (MDS-A) dataset for evaluating test-time adaptation models, featuring multiple training and test sets for researching object detection across various distributions.
Syllabus
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- Introduction to Test-Time Adaptation
-- Overview of test-time adaptation in object detection
-- Importance and challenges of adapting models to new distributions
- Introduction to MDS-A Dataset
-- Overview of Multiple Distribution Shift - Aerial (MDS-A) dataset
-- Dataset collection and annotation process
-- Key features and unique aspects of MDS-A
- Structure of MDS-A Dataset
-- Description of multiple training sets
-- Description of multiple test sets
-- Types of distribution shifts represented
- Evaluation Metrics
-- Standard object detection metrics
-- Metrics for evaluating test-time adaptation
-- Comparative analysis of adaptation methods
- Data Preparation and Preprocessing
-- Techniques for preparing MDS-A for model training
-- Handling multiple distributions in a dataset
- Test-Time Adaptation Models
-- Overview of adaptation models for object detection
-- State-of-the-art methods relevant to MDS-A
-- Discussion on model robustness and efficiency
- Experimental Setup
-- Guidelines for setting up experiments with MDS-A
-- Parameters for evaluating different test-time adaptation strategies
- Analysis and Interpretation
-- Analyzing results across different distribution shifts
-- Visualizing adaptation performance
- Practical Workshop
-- Hands-on session with MDS-A dataset
-- Implementing a basic test-time adaptation model
- Research Directions and Future Work
-- Current challenges in test-time adaptation
-- Potential research avenues using MDS-A
- Conclusion
-- Recap of the importance of test-time adaptation
-- Summary of insights gained from using MDS-A dataset
Taught by
Tags
On-Demand
