Multi-stage architectures have exhibited efficacy in image dehazing, which
usually decomposes a challenging task into multiple more tractable sub-tasks
and progressively estimates latent hazy-free images. Despite the remarkable
progress, existing methods still suffer from the following shortcomings: (1)
limited exploration of frequency domain information; (2) insufficient
information interaction; (3) severe feature redundancy. To remedy these issues,
we propose a novel Mutual Information-driven Triple interaction Network
(MITNet) based on spatial-frequency dual domain information and two-stage
architecture. To be specific, the first stage, named amplitude-guided haze
removal, aims to recover the amplitude spectrum of the hazy images for haze
removal. And the second stage, named phase-guided structure refined, devotes to
learning the transformation and refinement of the phase spectrum. To facilitate
the information exchange between two stages, an Adaptive Triple Interaction
Module (ATIM) is developed to simultaneously aggregate cross-domain,
cross-scale, and cross-stage features, where the fused features are further
used to generate content-adaptive dynamic filters so that applying them to
enhance global context representation. In addition, we impose the mutual
information minimization constraint on paired scale encoder and decoder
features from both stages. Such an operation can effectively reduce information
redundancy and enhance cross-stage feature complementarity. Extensive
experiments on multiple public datasets exhibit that our MITNet performs
superior performance with lower model complexity.The code and models are
available at this https URL

提出了一种基于空间频率双域信息和两阶段架构的互信息驱动的三重交互网络（MITNet），解决了现有方法中对频域信息的有限探索、信息交互不足和特征冗余严重等问题。MITNet 在多个公共数据集上展示出卓越的性能，并具有较低的模型复杂性。

基于互信息的三重交互网络用于高效图像去雾

Mutual Information-driven Triple Interaction Network for Efficient Image  Dehazing

The advent of multilingual language models has generated a resurgence of
interest in cross-lingual information retrieval (CLIR), which is the task of
searching documents in one language with queries from another. However, the
rapid pace of progress has led to a confusing panoply of methods and
reproducibility has lagged behind the state of the art. In this context, our
work makes two important contributions: First, we provide a conceptual
framework for organizing different approaches to cross-lingual retrieval using
multi-stage architectures for mono-lingual retrieval as a scaffold. Second, we
implement simple yet effective reproducible baselines in the Anserini and
Pyserini IR toolkits for test collections from the TREC 2022 NeuCLIR Track, in
Persian, Russian, and Chinese. Our efforts are built on a collaboration of the
two teams that submitted the most effective runs to the TREC evaluation. These
contributions provide a firm foundation for future advances.

该研究旨在提供一种用于跨语言检索模型的组织框架，并在 TREC 2022 NeuCLIR 中的三种语言测试集上实现基于此的可重复现的基准线。

跨语言信息检索的简洁有效神经排序和重排序基线

Simple Yet Effective Neural Ranking and Reranking Baselines for  Cross-Lingual Information Retrieval

This paper investigates two techniques for developing efficient
self-supervised vision transformers (EsViT) for visual representation learning.
First, we show through a comprehensive empirical study that multi-stage
architectures with sparse self-attentions can significantly reduce modeling
complexity but with a cost of losing the ability to capture fine-grained
correspondences between image regions. Second, we propose a new pre-training
task of region matching which allows the model to capture fine-grained region
dependencies and as a result significantly improves the quality of the learned
vision representations. Our results show that combining the two techniques,
EsViT achieves 81.3% top-1 on the ImageNet linear probe evaluation,
outperforming prior arts with around an order magnitude of higher throughput.
When transferring to downstream linear classification tasks, EsViT outperforms
its supervised counterpart on 17 out of 18 datasets. The code and models are
publicly available: this https URL

本文研究了发展高效的自监督视觉变换器（EsViT）的两种技术，第一，我们通过全面的实证研究显示具有稀疏自我注意力的多阶段架构可以显着减少建模复杂性，但代价是失去捕捉图像区域之间的细粒度对应关系的能力。第二，我们提出了新的预训练任务区域匹配，允许模型捕捉细粒度区域依赖性，从而显着提高了学习到的视觉表示的质量。我们的结果表明，结合这两种技术，EsViT 在 ImageNet 线性探针评估中达到 81.3％的 top-1，超过以前的艺术水平，吞吐量大约高一个数量级。在转移到下游线性分类任务时，EsViT 在 18 个数据集中的 17 个数据集上优于其受监督的对应物。代码和模型可公开获取：该 URL。