One-shot detection of anatomical landmarks is gaining significant attention
for its efficiency in using minimal labeled data to produce promising results.
However, the success of current methods heavily relies on the employment of
extensive unlabeled data to pre-train an effective feature extractor, which
limits their applicability in scenarios where a substantial amount of unlabeled
data is unavailable. In this paper, we propose the first foundation
model-enabled one-shot landmark detection (FM-OSD) framework for accurate
landmark detection in medical images by utilizing solely a single template
image without any additional unlabeled data. Specifically, we use the frozen
image encoder of visual foundation models as the feature extractor, and
introduce dual-branch global and local feature decoders to increase the
resolution of extracted features in a coarse to fine manner. The introduced
feature decoders are efficiently trained with a distance-aware similarity
learning loss to incorporate domain knowledge from the single template image.
Moreover, a novel bidirectional matching strategy is developed to improve both
robustness and accuracy of landmark detection in the case of scattered
similarity map obtained by foundation models. We validate our method on two
public anatomical landmark detection datasets. By using solely a single
template image, our method demonstrates significant superiority over strong
state-of-the-art one-shot landmark detection methods.

提出了一种基于单一模板图像的基础模型支持的一次性解剖标记检测（FM-OSD）框架，通过仅利用单个模板图像而不需要任何额外未标记数据，在医学图像中实现准确的标记检测。

FM-OSD: 基于 Foundation Model 的一次性检测解剖标志物

FM-OSD: Foundation Model-Enabled One-Shot Detection of Anatomical  Landmarks

A practical long-term tracker typically contains three key properties, i.e.
an efficient model design, an effective global re-detection strategy and a
robust distractor awareness mechanism. However, most state-of-the-art long-term
trackers (e.g., Pseudo and re-detecting based ones) do not take all three key
properties into account and therefore may either be time-consuming or drift to
distractors. To address the issues, we propose a two-task tracking frame work
(named DMTrack), which utilizes two core components (i.e., one-shot detection
and re-identification (re-id) association) to achieve distractor-aware fast
tracking via Dynamic convolutions (d-convs) and Multiple object tracking (MOT)
philosophy. To achieve precise and fast global detection, we construct a
lightweight one-shot detector using a novel dynamic convolutions generation
method, which provides a unified and more flexible way for fusing target
information into the search field. To distinguish the target from distractors,
we resort to the philosophy of MOT to reason distractors explicitly by
maintaining all potential similarities' tracklets. Benefited from the strength
of high recall detection and explicit object association, our tracker achieves
state-of-the-art performance on the LaSOT, OxUvA, TLP, VOT2018LT and VOT2019LT
benchmarks and runs in real-time (3x faster than comparisons).

提出一种名为 DMTrack 的双重任务跟踪框架，通过动态卷积和多目标跟踪思想，利用一次性检测和重新识别关联两个核心组成部分，实现排除干扰因素的快速追踪，针对机器视觉领域中的长期跟踪问题，达到了目前同类算法中领先的性能表现。

动态卷积与 MOT 哲学下考虑分心的快速跟踪

Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT  Philosophy

The current advances in object detection depend on large-scale datasets to
get good performance. However, there may not always be sufficient samples in
many scenarios, which leads to the research on few-shot detection as well as
its extreme variation one-shot detection. In this paper, the one-shot detection
has been formulated as a conditional probability problem. With this insight, a
novel one-shot conditional object detection (OSCD) framework, referred as
Comparison Network (ComparisonNet), has been proposed. Specifically, query and
target image features are extracted through a Siamese network as mapped metrics
of marginal probabilities. A two-stage detector for OSCD is introduced to
compare the extracted query and target features with the learnable metric to
approach the optimized non-linear conditional probability. Once trained,
ComparisonNet can detect objects of both seen and unseen classes without
further training, which also has the advantages including class-agnostic,
training-free for unseen classes, and without catastrophic forgetting.
Experiments show that the proposed approach achieves state-of-the-art
performance on the proposed datasets of Fashion-MNIST and PASCAL VOC.

本文提出了一种新的一次检测条件概率问题的比较网络框架（ComparisonNet）， 该框架利用 Siamese 网络提取查询和目标图像特征，并引入两阶段检测器来比较查询和目标特征以实现最优非线性条件概率。实验证明，该方法在 Fashion-MNIST 和 PASCAL VOC 的数据集上均取得了最先进的性能。