  We consider the problem of composed image retrieval that takes an input query
consisting of an image and a modification text indicating the desired changes
to be made on the image and retrieves images that match these changes. Current
state-of-the-art techniques that address this problem use global features for
the retrieval, resulting in incorrect localization of the regions of interest
to be modified because of the global nature of the features, more so in cases
of real-world, in-the-wild images. Since modifier texts usually correspond to
specific local changes in an image, it is critical that models learn local
features to be able to both localize and retrieve better. To this end, our key
novelty is a new gradient-attention-based learning objective that explicitly
forces the model to focus on the local regions of interest being modified in
each retrieval step. We achieve this by first proposing a new visual image
attention computation technique, which we call multi-modal gradient attention
(MMGrad) that is explicitly conditioned on the modifier text. We next
demonstrate how MMGrad can be incorporated into an end-to-end model training
strategy with a new learning objective that explicitly forces these MMGrad
attention maps to highlight the correct local regions corresponding to the
modifier text. By training retrieval models with this new loss function, we
show improved grounding by means of better visual attention maps, leading to
better explainability of the models as well as competitive quantitative
retrieval performance on standard benchmark datasets.
