The task of Visual Place Recognition (VPR) aims to match a query image
against references from an extensive database of images from different places,
relying solely on visual cues. State-of-the-art pipelines focus on the
aggregation of features extracted from a deep backbone, in order to form a
global descriptor for each image. In this context, we introduce SALAD (Sinkhorn
Algorithm for Locally Aggregated Descriptors), which reformulates NetVLAD's
soft-assignment of local features to clusters as an optimal transport problem.
In SALAD, we consider both feature-to-cluster and cluster-to-feature relations
and we also introduce a 'dustbin' cluster, designed to selectively discard
features deemed non-informative, enhancing the overall descriptor quality.
Additionally, we leverage and fine-tune DINOv2 as a backbone, which provides
enhanced description power for the local features, and dramatically reduces the
required training time. As a result, our single-stage method not only surpasses
single-stage baselines in public VPR datasets, but also surpasses two-stage
methods that add a re-ranking with significantly higher cost. Code and models
are available at this https URL

通过 SALAD 方法，利用 DINOv2 作为 backbone，优化了视觉地点识别任务，提高了描述符的质量，并在公共 VPR 数据集中超越了单阶段和双阶段方法。

视觉地点识别的最优输运聚合

Optimal Transport Aggregation for Visual Place Recognition

There have been two streams in the 3D detection from point clouds:
single-stage methods and two-stage methods. While the former is more
computationally efficient, the latter usually provides better detection
accuracy. By carefully examining the two-stage approaches, we have found that
if appropriately designed, the first stage can produce accurate box regression.
In this scenario, the second stage mainly rescores the boxes such that the
boxes with better localization get selected. From this observation, we have
devised a single-stage anchor-free network that can fulfill these requirements.
This network, named AFDetV2, extends the previous work by incorporating a
self-calibrated convolution block in the backbone, a keypoint auxiliary
supervision, and an IoU prediction branch in the multi-task head. As a result,
the detection accuracy is drastically boosted in the single-stage. To evaluate
our approach, we have conducted extensive experiments on the Waymo Open Dataset
and the nuScenes Dataset. We have observed that our AFDetV2 achieves the
state-of-the-art results on these two datasets, superior to all the prior arts,
including both the single-stage and the two-stage 3D detectors. AFDetV2 won the
1st place in the Real-Time 3D Detection of the Waymo Open Dataset Challenge
2021. In addition, a variant of our model AFDetV2-Base was entitled the "Most
Efficient Model" by the Challenge Sponsor, showing a superior computational
efficiency. To demonstrate the generality of this single-stage method, we have
also applied it to the first stage of the two-stage networks. Without
exception, the results show that with the strengthened backbone and the
rescoring approach, the second stage refinement is no longer needed.

本文介绍了一种名为 AFDetV2 的单级 anchor-free 网络方法在 3D 检测中的应用，并证明其在 Waymo 开放数据集及 nuScenes 数据集上的表现优于以往所有单级和双级 3D 检测方法。该方法在第一阶段中能够准确地回归确定物体位置，无需为此执行第二阶段的调整。