BOSS: Bottom-up Cross-modal Semantic Composition With Hybrid Counterfactual Training For Robust Content-based Image Retrieval
2022 · Wenqiao Zhang, Jiannan Guo, Mengze Li, et al.
Abstract
Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text, which potentially impacts a wide variety of real-world applications, 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 specific characteristics of the query image should be modified in order to get the intended target image. This task is challenging since it necessitates learning and understanding the composite image-text representation by incorporating cross-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 CIR
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