5 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.CV2025★ 2 cited
Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis
Arpita Chowdhury, Dipanjyoti Paul, Zheda Mai +10
We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish vis…
cs.CV2023★ 5 cited
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
Dipanjyoti Paul, Arpita Chowdhury, Xinqi Xiong +11
We present a novel usage of Transformers to make image classification interpretable. Unlike mainstream classifiers that wait until the last fully connected layer to incorporate cla…