In recent years, the dominant accuracy metric for vector search is the recall
of a result list of fixed size (top-k retrieval), considering as ground truth
the exact vector retrieval results. Although convenient to compute, this metric
is distantly related to the end-to-end accuracy of a full system that
integrates vector search. In this paper we focus on the common case where a
hard decision needs to be taken depending on the vector retrieval results, for
example, deciding whether a query image matches a database image or not. We
solve this as a range search task, where all vectors within a certain radius
from the query are returned.
We show that the value of a range search result can be modeled rigorously
based on the query-to-vector distance. This yields a metric for range search,
RSM, that is both principled and easy to compute without running an end-to-end
evaluation. We apply this metric to the case of image retrieval. We show that
indexing methods that are adapted for top-k retrieval do not necessarily
maximize the RSM. In particular, for inverted file based indexes, we show that
visiting a limited set of clusters and encoding vectors compactly yields near
optimal results.

研究了向量搜索中的召回率指标问题，提出基于范围搜索任务的 RSM 指标，以评估向量搜索的精确性，并证明了适应于 top-k 检索的索引方法不一定能最大化 RSM。

小半径向量搜索

Vector search with small radiuses

Agents that can understand and reason over the dynamics of objects can have a
better capability to act robustly and generalize to novel scenarios. Such an
ability, however, requires a suitable representation of the scene as well as an
understanding of the mechanisms that govern the interactions of different
subsets of objects. To address this problem, we propose RSM, or Reusable
Slotwise Mechanisms, that jointly learns a slotwise representation of the scene
and a modular architecture that dynamically chooses one mechanism among a set
of reusable mechanisms to predict the next state of each slot. RSM crucially
takes advantage of a \textit{Central Contextual Information (CCI)}, which lets
each selected reusable mechanism access the rest of the slots through a
bottleneck, effectively allowing for modeling higher order and complex
interactions that might require a sparse subset of objects. We show how this
model outperforms state-of-the-art methods in a variety of next-step prediction
tasks ranging from grid-world environments to Atari 2600 games. Particularly,
we challenge methods that model the dynamics with Graph Neural Networks (GNNs)
on top of slotwise representations, and modular architectures that restrict the
interactions to be only pairwise. Finally, we show that RSM is able to
generalize to scenes with objects varying in number and shape, highlighting its
out-of-distribution generalization capabilities. Our implementation is
available
online\footnote{\hyperlink{this https URL}{this http URL}}.

本研究提出了 RSM，一种采用可重复组件、学习场景的 “槽位式” 表示和动态选择机制的模块化架构，其可以很好地处理场景中不同对象之间的相互作用，可以应用于多种下一步预测任务中，并且在 Atari 2600 游戏中表现出色。