Relieving Triplet Ambiguity: Consensus Network for Language-Guided Image
  Retrieval
Xu Zhang Zhedong Zheng Xiaohan Wang Yi Yang
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.