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 class information to make predictions, we investigate a proactive approach, asking each class to search for itself in an image. We realize this idea via a Transformer encoder-decoder inspired by DEtection TRansformer (DETR). We learn "class-specific'' queries (one for each class) as input to the decoder, enabling each class to localize its patterns in an image via cross-attention. We name our approach INterpretable TRansformer (INTR), which is fairly easy to implement and exhibits several compelling properties. We show that INTR intrinsically encourages each class to attend distinctively; the cross-attention weights thus provide a faithful interpretation of the prediction. Interestingly, via "multi-head'' cross-attention, INTR could identify different "attributes'' of a class, making it particularly suitable for fine-grained classification and analysis, which we demonstrate on eight datasets.
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
DIPANJYOTI PAUL Arpita Chowdhury Xinqi Xiong Feng-Ju Chang David Edward Carlyn Samuel Stevens Kaiya L Provost Anuj Karpatne Bryan Carstens Daniel Rubenstein Charles Stewart Tanya Berger-Wolf Yu Su Wei-Lun Chao