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Content-Based Image Retrieval (CIR) aims to search for a target image byconcurrently comprehending the composition of an example image and acomplementary text, which potentially impacts a wide variety of real-worldapplications, such as internet search and fashion retrieval. In this scenario,the input image serves as an intuitive context and background for the search,while the corresponding language expressly requests new traits on how specificcharacteristics of the query image should be modified in order to get theintended target image. This task is challenging since it necessitates learningand understanding the composite image-text representation by incorporatingcross-granular semantic updates. In this paper, we tackle this task by a novel\underline{\textbf{B}}ottom-up cr\underline{\textbf{O}}ss-modal\underline{\textbf{S}}emantic compo\underline{\textbf{S}}ition (\textbf{BOSS})with Hybrid Counterfactual Training framework, which sheds new light on the CIRtask by studying it from two previously overlooked perspectives:\emph{implicitly bottom-up composition of visiolinguistic representation} and\emph{explicitly fine-grained correspondence of query-target construction}. Onthe one hand, we leverage the implicit interaction and composition ofcross-modal embeddings from the bottom local characteristics to the top globalsemantics, preserving and transforming the visual representation conditioned onlanguage semantics in several continuous steps for effective target imagesearch. On the other hand, we devise a hybrid counterfactual training strategythat can reduce the model's ambiguity for similar queries.

BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval