  Comparing two images in terms of Commonalities and Differences (CaD) is a
fundamental human capability that forms the basis of advanced visual reasoning
and interpretation. It is essential for the generation of detailed and
contextually relevant descriptions, performing comparative analysis, novelty
detection, and making informed decisions based on visual data. However,
surprisingly, little attention has been given to these fundamental concepts in
the best current mimic of human visual intelligence - Large Multimodal Models
(LMMs). We develop and contribute a new two-phase approach CaD-VI for
collecting synthetic visual instructions, together with an
instruction-following dataset CaD-Inst containing 349K image pairs with CaD
instructions collected using CaD-VI. Our approach significantly improves the
CaD spotting capabilities in LMMs, advancing the SOTA on a diverse set of
related tasks by up to 17.5%. It is also complementary to existing
difference-only instruction datasets, allowing automatic targeted refinement of
those resources increasing their effectiveness for CaD tuning by up to 10%.
Additionally, we propose an evaluation benchmark with 7.5K open-ended QAs to
assess the CaD understanding abilities of LMMs.
