In contrast to traditional exhaustive search, selective search first clusters
documents into several groups before all the documents are searched
exhaustively by a query, to limit the search executed within one group or only
a few groups. Selective search is designed to reduce the latency and
computation in modern large-scale search systems. In this study, we propose
MICO, a Mutual Information CO-training framework for selective search with
minimal supervision using the search logs. After training, MICO does not only
cluster the documents, but also routes unseen queries to the relevant clusters
for efficient retrieval. In our empirical experiments, MICO significantly
improves the performance on multiple metrics of selective search and
outperforms a number of existing competitive baselines.

本论文提出了 MICO 互信息协同训练框架，使用搜索日志进行有监督的选择性搜索，可将文档聚类，并将未知的查询路由到相关群集以进行有效检索。在多种指标上，MICO 显著提高了选择性搜索的性能，并胜过了一些现有的竞争基线。

MICO: 基于相互信息协同训练的有选择性搜索

MICO: Selective Search with Mutual Information Co-training

Object detection is a fundamental task in many computer vision applications,
therefore the importance of evaluating the quality of object detection is well
acknowledged in this domain. This process gives insight into the capabilities
of methods in handling environmental changes. In this paper, a new method for
object detection is introduced that combines the Selective Search and
EdgeBoxes. We tested these three methods under environmental variations. Our
experiments demonstrate the outperformance of the combination method under
illumination and view point variations.

本文介绍了一种将 Selective Search 和 EdgeBoxes 相结合的目标检测方法，并在环境变化下对其进行了实验验证，结果表明此方法在光照和视角变化下性能优于其他两种方法。