Contour Integration Benchmark
Testing if AI models 'see' fragmented objects like humans do.
The Challenge
Deep learning models excel at many vision tasks but fail to generalize like humans, especially in recognizing fragmented or partially obscured objects. Understanding this gap requires controlled benchmarks.
The Outcome
- Demonstrated that contour integration capability scales with training data size in models.
- Showed that models sharing human-like 'integration bias' perform better and are more robust.
- Established that contour integration is a learnable mechanism linked to shape bias.
- Provided a benchmark for evaluating model generalization and human-likeness.
Key Information
Modalities Used
App Stack
Multimodal Integration
Conceptual: Mixpeek could manage the large datasets of fragmented images, automate model testing pipelines, store results, and facilitate comparison across different model architectures and training runs.
Features Extracted:
Other Technologies
Python
Deep Learning Frameworks (e.g., PyTorch)
Data Analysis Libraries (e.g., Pandas, NumPy)
Benchmarking Tools