In visual place recognition, accurately identifying and matching images of
locations under varying environmental conditions and viewpoints remains a
significant challenge. In this paper, we introduce a new technique, called
Bag-of-Queries (BoQ), which learns a set of global queries designed to capture
universal place-specific attributes. Unlike existing methods that employ
self-attention and generate the queries directly from the input features, BoQ
employs distinct learnable global queries, which probe the input features via
cross-attention, ensuring consistent information aggregation. In addition, our
technique provides an interpretable attention mechanism and integrates with
both CNN and Vision Transformer backbones. The performance of BoQ is
demonstrated through extensive experiments on 14 large-scale benchmarks. It
consistently outperforms current state-of-the-art techniques including NetVLAD,
MixVPR and EigenPlaces. Moreover, as a global retrieval technique (one-stage),
BoQ surpasses two-stage retrieval methods, such as Patch-NetVLAD, TransVPR and
R2Former, all while being orders of magnitude faster and more efficient. The
code and model weights are publicly available at
this https URL

在视觉地点识别中，通过引入一种名为查询包（BoQ）的新技术，该技术学习一组全局查询来捕捉普遍的地点特征，并利用交叉注意力实现一致的信息聚合。BoQ 通过与 CNN 和 Vision Transformer 骨干网络集成，并通过对 14 个大规模基准进行广泛实验表明了卓越的性能。同时，作为一种全局检索技术（一阶段），BoQ 在速度和效率上超过了 Patch-NetVLAD、TransVPR 和 R2Former 等两阶段检索方法。

BoQ：一个地方值得一袋可学习的查询

BoQ: A Place is Worth a Bag of Learnable Queries

Given a set of synchronous time series, each associated with a sensor-point
in space and characterized by inter-series relationships, the problem of
spatiotemporal forecasting consists of predicting future observations for each
point. Spatiotemporal graph neural networks achieve striking results by
representing the relationships across time series as a graph. Nonetheless, most
existing methods rely on the often unrealistic assumption that inputs are
always available and fail to capture hidden spatiotemporal dynamics when part
of the data is missing. In this work, we tackle this problem through
hierarchical spatiotemporal downsampling. The input time series are
progressively coarsened over time and space, obtaining a pool of
representations that capture heterogeneous temporal and spatial dynamics.
Conditioned on observations and missing data patterns, such representations are
combined by an interpretable attention mechanism to generate the forecasts. Our
approach outperforms state-of-the-art methods on synthetic and real-world
benchmarks under different missing data distributions, particularly in the
presence of contiguous blocks of missing values.

通过分层时空下采样，利用可解释的注意机制结合观测和缺失数据模式，我们的方法在不同的缺失数据分布以及连续缺失数据块存在的情况下，在合成和实际基准测试中优于最先进的方法。