Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and
  Gallery Banks
Yimu Wang Xiangru Jian Bo Xue
In this work, we present a post-processing solution to address the hubness
problem in cross-modal retrieval, a phenomenon where a small number of gallery
data points are frequently retrieved, resulting in a decline in retrieval
performance. We first theoretically demonstrate the necessity of incorporating
both the gallery and query data for addressing hubness as hubs always exhibit
high similarity with gallery and query data. Second, building on our
theoretical results, we propose a novel framework, Dual Bank Normalization
(DBNorm). While previous work has attempted to alleviate hubness by only
utilizing the query samples, DBNorm leverages two banks constructed from the
query and gallery samples to reduce the occurrence of hubs during inference.
Next, to complement DBNorm, we introduce two novel methods, dual inverted
softmax and dual dynamic inverted softmax, for normalizing similarity based on
the two banks. Specifically, our proposed methods reduce the similarity between
hubs and queries while improving the similarity between non-hubs and queries.
Finally, we present extensive experimental results on diverse language-grounded
benchmarks, including text-image, text-video, and text-audio, demonstrating the
superior performance of our approaches compared to previous methods in
addressing hubness and boosting retrieval performance. Our code is available at
https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval.