Search Results for author:
Found 5 papers, 3 papers with code
PhD Thesis: Exploring the role of (self-)attention in cognitive and computer vision architecture
We investigate the role of attention and memory in complex reasoning tasks.
Conviformers: Convolutionally guided Vision Transformer
Vision transformers are nowadays the de-facto choice for image classification tasks.
Ranked #1 on
Fine-Grained Image Classification
on Herbarium 2022
A Benchmark for Compositional Visual Reasoning
Overall, we hope that our challenge will spur interest in the development of neural architectures that can learn to harness compositionality toward more efficient learning.
GAMR: A Guided Attention Model for (visual) Reasoning
Humans continue to outperform modern AI systems in their ability to flexibly parse and understand complex visual scenes.
Understanding the computational demands underlying visual reasoning
Our analysis reveals a novel taxonomy of visual reasoning tasks, which can be primarily explained by both the type of relations (same-different vs. spatial-relation judgments) and the number of relations used to compose the underlying rules.
