New transformer networks have been integrated into object tracking pipelines
and have demonstrated strong performance on the latest benchmarks. This paper
focuses on understanding how transformer trackers behave under adversarial
attacks and how different attacks perform on tracking datasets as their
parameters change. We conducted a series of experiments to evaluate the
effectiveness of existing adversarial attacks on object trackers with
transformer and non-transformer backbones. We experimented on 7 different
trackers, including 3 that are transformer-based, and 4 which leverage other
architectures. These trackers are tested against 4 recent attack methods to
assess their performance and robustness on VOT2022ST, UAV123 and GOT10k
datasets. Our empirical study focuses on evaluating adversarial robustness of
object trackers based on bounding box versus binary mask predictions, and
attack methods at different levels of perturbations. Interestingly, our study
found that altering the perturbation level may not significantly affect the
overall object tracking results after the attack. Similarly, the sparsity and
imperceptibility of the attack perturbations may remain stable against
perturbation level shifts. By applying a specific attack on all transformer
trackers, we show that new transformer trackers having a stronger
cross-attention modeling achieve a greater adversarial robustness on tracking
datasets, such as VOT2022ST and GOT10k. Our results also indicate the necessity
for new attack methods to effectively tackle the latest types of transformer
trackers. The codes necessary to reproduce this study are available at
this https URL

新的 Transformer 跟踪器在最新的基准测试上表现出高效能，该研究关注于理解 Transformer 跟踪器在对抗性攻击中的行为以及各种攻击方法在参数变化时对跟踪数据集的表现。我们的实证研究发现，攻击干扰的程度和攻击干扰的稀疏性可能在不同干扰级别的变化下保持稳定。通过对所有 Transformer 跟踪器应用特定攻击，我们展示了具有更强交叉注意建模的新 Transformer 跟踪器在跟踪数据集上具有更强的对抗鲁棒性。同时，我们的研究结果也表明需要新的攻击方法来有效应对最新类型的 Transformer 跟踪器。

强化变压器追踪器对抗攻击的可复现性研究

Reproducibility Study on Adversarial Attacks Against Robust Transformer  Trackers

High-performance Transformer trackers have shown excellent results, yet they
often bear a heavy computational load. Observing that a smaller input can
immediately and conveniently reduce computations without changing the model, an
easy solution is to adopt the low-resolution input for efficient Transformer
tracking. Albeit faster, this hurts tracking accuracy much due to information
loss in low resolution tracking. In this paper, we aim to mitigate such
information loss to boost the performance of the low-resolution Transformer
tracking via dual knowledge distillation from a frozen high-resolution (but not
a larger) Transformer tracker. The core lies in two simple yet effective
distillation modules, comprising query-key-value knowledge distillation
(QKV-KD) and discrimination knowledge distillation (Disc-KD), across
resolutions. The former, from the global view, allows the low-resolution
tracker to inherit the features and interactions from the high-resolution
tracker, while the later, from the target-aware view, enhances the
target-background distinguishing capacity via imitating discriminative regions
from its high-resolution counterpart. With the dual knowledge distillation, our
Low-Resolution Transformer Tracker (LoReTrack) enjoys not only high efficiency
owing to reduced computation but also enhanced accuracy by distilling knowledge
from the high-resolution tracker. In extensive experiments, LoReTrack with a
256x256 resolution consistently improves baseline with the same resolution, and
shows competitive or even better results compared to 384x384 high-resolution
Transformer tracker, while running 52% faster and saving 56% MACs. Moreover,
LoReTrack is resolution-scalable. With a 128x128 resolution, it runs 25 fps on
a CPU with 64.9%/46.4% SUC scores on LaSOT/LaSOText, surpassing all other CPU
real-time trackers. Code will be released.

通过从冻结的高分辨率 Transformer 追踪器中进行双重知识蒸馏，采用低分辨率输入来增强低分辨率 Transformer 追踪的性能，从而实现高效率和精度的 LoReTrack 追踪器。

LoReTrack: 高效准确的低分辨率 Transformer 跟踪

LoReTrack: Efficient and Accurate Low-Resolution Transformer Tracking

One-stream Transformer trackers have shown outstanding performance in
challenging benchmark datasets over the last three years, as they enable
interaction between the target template and search region tokens to extract
target-oriented features with mutual guidance. Previous approaches allow free
bidirectional information flow between template and search tokens without
investigating their influence on the tracker's discriminative capability. In
this study, we conducted a detailed study on the information flow of the tokens
and based on the findings, we propose a novel Optimized Information Flow
Tracking (OIFTrack) framework to enhance the discriminative capability of the
tracker. The proposed OIFTrack blocks the interaction from all search tokens to
target template tokens in early encoder layers, as the large number of
non-target tokens in the search region diminishes the importance of
target-specific features. In the deeper encoder layers of the proposed tracker,
search tokens are partitioned into target search tokens and non-target search
tokens, allowing bidirectional flow from target search tokens to template
tokens to capture the appearance changes of the target. In addition, since the
proposed tracker incorporates dynamic background cues, distractor objects are
successfully avoided by capturing the surrounding information of the target.
The OIFTrack demonstrated outstanding performance in challenging benchmarks,
particularly excelling in the one-shot tracking benchmark GOT-10k, achieving an
average overlap of 74.6\%. The code, models, and results of this work are
available at https://github.com/JananiKugaa/OIFTrack

研究发现基于信息流的变压器跟踪器 (OIFTrack) 可以增强其辨别能力，并在挑战性的基准测试中取得杰出表现，特别是在一次跟踪基准测试 GOT-10k 中，平均重叠度达到 74.6%。

Transformer 跟踪的信息流优化

Optimized Information Flow for Transformer Tracking

Transformer trackers have achieved impressive advancements recently, where
the attention mechanism plays an important role. However, the independent
correlation computation in the attention mechanism could result in noisy and
ambiguous attention weights, which inhibits further performance improvement. To
address this issue, we propose an attention in attention (AiA) module, which
enhances appropriate correlations and suppresses erroneous ones by seeking
consensus among all correlation vectors. Our AiA module can be readily applied
to both self-attention blocks and cross-attention blocks to facilitate feature
aggregation and information propagation for visual tracking. Moreover, we
propose a streamlined Transformer tracking framework, dubbed AiATrack, by
introducing efficient feature reuse and target-background embeddings to make
full use of temporal references. Experiments show that our tracker achieves
state-of-the-art performance on six tracking benchmarks while running at a
real-time speed.

提出改进注意力机制的注意力内注意（AiA）模块及优化特征聚合和信息传递的 Transformer 跟踪框架 AiATrack，在多个定位基准测试上实现了最先进的性能表现。