Prototypical parts-based networks are becoming increasingly popular due to
their faithful self-explanations. However, their similarity maps are calculated
in the penultimate network layer. Therefore, the receptive field of the
prototype activation region often depends on parts of the image outside this
region, which can lead to misleading interpretations. We name this undesired
behavior a spatial explanation misalignment and introduce an interpretability
benchmark with a set of dedicated metrics for quantifying this phenomenon. In
addition, we propose a method for misalignment compensation and apply it to
existing state-of-the-art models. We show the expressiveness of our benchmark
and the effectiveness of the proposed compensation methodology through
extensive empirical studies.

分类研究了典型的基于部件的网络及其自解释特性，指出其相似度映射存在的空间解释错位问题，并提供了一种解决方法，通过实证研究验证了该方法的有效性。

评估原型部分解释的空间不对齐的可解释性基准

Interpretability Benchmark for Evaluating Spatial Misalignment of  Prototypical Parts Explanations

Temporal contexts among consecutive frames are far from being fully utilized
in existing visual trackers. In this work, we present TCTrack, a comprehensive
framework to fully exploit temporal contexts for aerial tracking. The temporal
contexts are incorporated at \textbf{two levels}: the extraction of
\textbf{features} and the refinement of \textbf{similarity maps}. Specifically,
for feature extraction, an online temporally adaptive convolution is proposed
to enhance the spatial features using temporal information, which is achieved
by dynamically calibrating the convolution weights according to the previous
frames. For similarity map refinement, we propose an adaptive temporal
transformer, which first effectively encodes temporal knowledge in a
memory-efficient way, before the temporal knowledge is decoded for accurate
adjustment of the similarity map. TCTrack is effective and efficient:
evaluation on four aerial tracking benchmarks shows its impressive performance;
real-world UAV tests show its high speed of over 27 FPS on NVIDIA Jetson AGX
Xavier.

本文提出了一种全面利用时间上下文进行航空追踪的框架，名为 TCTrack，该框架在特征提取和相似度图优化方面使用了时间上下文。实验表明，TCTrack 在四个航空追踪基准测试数据集上的效果令人印象深刻，并且在 NVIDIA Jetson AGX Xavier 上实现了高达 27 FPS 的高速。

TCTrack: 航空跟踪的时间上下文

TCTrack: Temporal Contexts for Aerial Tracking

Most existing Siamese-based tracking methods execute the classification and
regression of the target object based on the similarity maps. However, they
either employ a single map from the last convolutional layer which degrades the
localization accuracy in complex scenarios or separately use multiple maps for
decision making, introducing intractable computations for aerial mobile
platforms. Thus, in this work, we propose an efficient and effective
hierarchical feature transformer (HiFT) for aerial tracking. Hierarchical
similarity maps generated by multi-level convolutional layers are fed into the
feature transformer to achieve the interactive fusion of spatial (shallow
layers) and semantics cues (deep layers). Consequently, not only the global
contextual information can be raised, facilitating the target search, but also
our end-to-end architecture with the transformer can efficiently learn the
interdependencies among multi-level features, thereby discovering a
tracking-tailored feature space with strong discriminability. Comprehensive
evaluations on four aerial benchmarks have proven the effectiveness of HiFT.
Real-world tests on the aerial platform have strongly validated its
practicability with a real-time speed. Our code is available at
this https URL

本文提出了一种基于 Siamese 的跟踪方法，采用分层特征变换对多级卷积层生成的分层相似度图进行交互式融合，提高了全局的上下文信息，更高效地学习多级特征之间的相互依赖关系，并且实现了在实时速度情况下的实际应用，该方法在四个空中基准测试中被证明具有很好的效果。