  Current metric learning approaches for image retrieval are usually based on
learning a space of informative latent representations where simple approaches
such as the cosine distance will work well. Recent state of the art methods
such as HypViT move to more complex embedding spaces that may yield better
results but are harder to scale to production environments. In this work, we
first construct a simpler model based on triplet loss with hard negatives
mining that performs at the state of the art level but does not have these
drawbacks. Second, we introduce a novel approach for image retrieval
postprocessing called Siamese Transformer for Image Retrieval (STIR) that
reranks several top outputs in a single forward pass. Unlike previously
proposed Reranking Transformers, STIR does not rely on global/local feature
extraction and directly compares a query image and a retrieved candidate on
pixel level with the usage of attention mechanism. The resulting approach
defines a new state of the art on standard image retrieval datasets: Stanford
Online Products and DeepFashion In-shop. We also release the source code at
https://github.com/OML-Team/open-metric-learning/tree/main/pipelines/postprocessing/
and an interactive demo of our approach at
https://dapladoc-oml-postprocessing-demo-srcappmain-pfh2g0.streamlit.app/
