面向视觉目标跟踪的高效词元压缩框架
An Efficient Token Compression Framework for Visual Object Tracking
提出可学习的自适应词元压缩器与分层交互编码器，可复用于其他需要处理多帧历史模板的视觉跟踪或视频理解任务，以降低词元冗余和计算量。
原文依据 we propose a compress-then-interact tracking framework, ETCTrack, that learns to efficiently compress template tokens from historical template frames into a robust target representation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论通过消除视觉表示内部的特征级冗余来精炼视觉表示，对于同时优化视觉跟踪中模型的性能和计算成本至关重要。为了提升性能，许多当代基于Transformer的跟踪器利用更多的历史模板帧来捕获更丰富的时空线索。然而，这种策略导致输入视觉词元数量巨大。这带来了两个关键问题：它施加了二次计算负担，并且还可能降低跟踪器的整体性能。为了弥补这一差距，我们提出了一个先压缩后交互的跟踪框架ETCTrack，它学习将历史模板帧中的模板词元高效压缩为鲁棒的目标表示，超越了手工设计的规则。我们的方法首先采用自适应词元压缩器，通过过滤冗余的视觉词元来动态构建紧凑且具有高度判别力的模板词元。这些精炼后的模板词元随后由我们的分层交互编码器处理，以实现与搜索特征的深度自适应交互。精炼后的搜索特征确保了后续精确的目标定位。在七个基准上的实验表明，我们的方法优于当前最优的跟踪器。ETCTrack-B224将模板词元数量减少了60%，导致MACs降低21.4%，而准确率仅下降0.4%。源代码可在https://github.com/PJD-WJ/ETCTrack获取。
英文原文摘要 1519 字符
Refining visual representations by eliminating their internal feature-level redundancy is crucial for simultaneously optimizing the performance and computational cost of models in visual tracking. To enhance their performance, many contemporary Transformer-based trackers leverage a larger number of historical template frames to capture richer spatio-temporal cues. However, this strategy leads to a massive number of input visual tokens. This creates two critical issues: it imposes a quadratic computational burden and can also degrade the tracker's overall performance. To bridge this gap, we propose a compress-then-interact tracking framework, ETCTrack, that learns to efficiently compress template tokens from historical template frames into a robust target representation, moving beyond handcrafted rules. Our method first employs the Adaptive Token Compressor to dynamically construct compact yet highly discriminative template tokens by filtering out redundant visual tokens. These refined template tokens are then processed by our Hierarchical Interaction Encoder to achieve a deep, adaptive interaction with the search features. Refined search features ensure subsequent precise target localization. Experiments on seven benchmarks demonstrate that our method outperforms current state-of-the-art trackers. ETCTrack-B224 reduces the number of template tokens by 60%, leading to a 21.4% reduction in MACs with only a 0.4% drop in accuracy. The source code are available at https://github.com/PJD-WJ/ETCTrack.