  Composed image retrieval (CIR) is a new and flexible image retrieval
paradigm, which can retrieve the target image for a multimodal query, including
a reference image and its corresponding modification text. Although existing
efforts have achieved compelling success, they overlook the conflict
relationship modeling between the reference image and the modification text for
improving the multimodal query composition and the adaptive matching degree
modeling for promoting the ranking of the candidate images that could present
different levels of matching degrees with the given query. To address these two
limitations, in this work, we propose a Target-Guided Composed Image Retrieval
network (TG-CIR). In particular, TG-CIR first extracts the unified global and
local attribute features for the reference/target image and the modification
text with the contrastive language-image pre-training model (CLIP) as the
backbone, where an orthogonal regularization is introduced to promote the
independence among the attribute features. Then TG-CIR designs a target-query
relationship-guided multimodal query composition module, comprising a
target-free student composition branch and a target-based teacher composition
branch, where the target-query relationship is injected into the teacher branch
for guiding the conflict relationship modeling of the student branch. Last,
apart from the conventional batch-based classification loss, TG-CIR
additionally introduces a batch-based target similarity-guided matching degree
regularization to promote the metric learning process. Extensive experiments on
three benchmark datasets demonstrate the superiority of our proposed method.
