  Transformers are powerful visual learners, in large part due to their
conspicuous lack of manually-specified priors. This flexibility can be
problematic in tasks that involve multiple-view geometry, due to the
near-infinite possible variations in 3D shapes and viewpoints (requiring
flexibility), and the precise nature of projective geometry (obeying rigid
laws). To resolve this conundrum, we propose a "light touch" approach, guiding
visual Transformers to learn multiple-view geometry but allowing them to break
free when needed. We achieve this by using epipolar lines to guide the
Transformer's cross-attention maps, penalizing attention values outside the
epipolar lines and encouraging higher attention along these lines since they
contain geometrically plausible matches. Unlike previous methods, our proposal
does not require any camera pose information at test-time. We focus on
pose-invariant object instance retrieval, where standard Transformer networks
struggle, due to the large differences in viewpoint between query and retrieved
images. Experimentally, our method outperforms state-of-the-art approaches at
object retrieval, without needing pose information at test-time.
