Language-guided image retrieval enables users to search for images and
interact with the retrieval system more naturally and expressively by using a
reference image and a relative caption as a query. Most existing studies mainly
focus on designing image-text composition architecture to extract
discriminative visual-linguistic relations. Despite great success, we identify
an inherent problem that obstructs the extraction of discriminative features
and considerably compromises model training: \textbf{triplet ambiguity}. This
problem stems from the annotation process wherein annotators view only one
triplet at a time. As a result, they often describe simple attributes, such as
color, while neglecting fine-grained details like location and style. This
leads to multiple false-negative candidates matching the same modification
text. We propose a novel Consensus Network (Css-Net) that self-adaptively
learns from noisy triplets to minimize the negative effects of triplet
ambiguity. Inspired by the psychological finding that groups perform better
than individuals, Css-Net comprises 1) a consensus module featuring four
distinct compositors that generate diverse fused image-text embeddings and 2) a
Kullback-Leibler divergence loss, which fosters learning among the compositors,
enabling them to reduce biases learned from noisy triplets and reach a
consensus. The decisions from four compositors are weighted during evaluation
to further achieve consensus. Comprehensive experiments on three datasets
demonstrate that Css-Net can alleviate triplet ambiguity, achieving competitive
performance on benchmarks, such as $+2.77\%$ R@10 and $+6.67\%$ R@50 on
FashionIQ.

本文提出了一种名为 Css-Net 的共识网络模型，通过包含四个不同组成者的共识模块，采用 Kullback-Leibler 散度损失来使它们通过学习减少从噪声三元组中学习到的偏差并达成一致，进而减轻三元组歧义问题，提高基准测试的重要性能。