Neural information retrieval often adopts a retrieve-and-rerank framework: a
bi-encoder network first retrieves K (e.g., 100) candidates that are then
re-ranked using a more powerful cross-encoder model to rank the better
candidates higher. The re-ranker generally produces better candidate scores
than the retriever, but is limited to seeing only the top K retrieved
candidates, thus providing no improvements in retrieval performance as measured
by Recall@K. In this work, we leverage the re-ranker to also improve retrieval
by providing inference-time relevance feedback to the retriever. Concretely, we
update the retriever's query representation for a test instance using a
lightweight inference-time distillation of the re-ranker's prediction for that
instance. The distillation loss is designed to bring the retriever's candidate
scores closer to those of the re-ranker. A second retrieval step is then
performed with the updated query vector. We empirically show that our approach,
which can serve arbitrary retrieve-and-rerank pipelines, significantly improves
retrieval recall in multiple domains, languages, and modalities.

本文提出了利用轻量级的推理，将 re-ranker 的命中率反馈给 retriever 的方法，从而提高检索召回率。实验证明，此方法在多个领域、语言和模态下均明显提高了检索召回率。

神经信息检索推理时重排反馈

Inference-time Re-ranker Relevance Feedback for Neural Information  Retrieval

Referring image segmentation segments an image from a language expression.
With the aim of producing high-quality masks, existing methods often adopt
iterative learning approaches that rely on RNNs or stacked attention layers to
refine vision-language features. Despite their complexity, RNN-based methods
are subject to specific encoder choices, while attention-based methods offer
limited gains. In this work, we introduce a simple yet effective alternative
for progressively learning discriminative multi-modal features. The core idea
of our approach is to leverage a continuously updated query as the
representation of the target object and at each iteration, strengthen
multi-modal features strongly correlated to the query while weakening less
related ones. As the query is initialized by language features and successively
updated by object features, our algorithm gradually shifts from being
localization-centric to segmentation-centric. This strategy enables the
incremental recovery of missing object parts and/or removal of extraneous parts
through iteration. Compared to its counterparts, our method is more
versatile$\unicode{x2014}$it can be plugged into prior arts straightforwardly
and consistently bring improvements. Experimental results on the challenging
datasets of RefCOCO, RefCOCO+, and G-Ref demonstrate its advantage with respect
to the state-of-the-art methods.

本论文提出一种基于逐步学习区分性多模态特征的方法，通过不断更新查询作为目标对象的表示，强化与之相关的多模态特征，逐渐从定位中心转为分割中心，实现逐步修复缺失对象部分和 / 或去除多余部分，并在 RefCOCO、RefCOCO+ 和 G-Ref 数据集上的实验结果表明其优于现有方法。