In recent years, deep-learning-based visual object trackers have been studied
thoroughly, but handling occlusions and/or rapid motion of the target remains
challenging. In this work, we argue that conditioning on the natural language
(NL) description of a target provides information for longer-term invariance,
and thus helps cope with typical tracking challenges. However, deriving a
formulation to combine the strengths of appearance-based tracking with the
language modality is not straightforward. We propose a novel deep
tracking-by-detection formulation that can take advantage of NL descriptions.
Regions that are related to the given NL description are generated by a
proposal network during the detection phase of the tracker. Our LSTM based
tracker then predicts the update of the target from regions proposed by the NL
based detection phase. In benchmarks, our method is competitive with state of
the art trackers, while it outperforms all other trackers on targets with
unambiguous and precise language annotations. It also beats the
state-of-the-art NL tracker when initializing without a bounding box. Our
method runs at over 30 fps on a single GPU.

该研究提出一种利用自然语言描述的深度追踪器，通过使用 LSTM 跟踪器预测目标的更新，可以以每秒超过 30 帧的速度处理具有模糊语言注释的目标，展示出色的性能。

使用自然语言描述的实时视觉目标跟踪

Real-time Visual Object Tracking with Natural Language Description

This paper improves state-of-the-art visual object trackers that use online
adaptation. Our core contribution is an offline meta-learning-based method to
adjust the initial deep networks used in online adaptation-based tracking. The
meta learning is driven by the goal of deep networks that can quickly be
adapted to robustly model a particular target in future frames. Ideally the
resulting models focus on features that are useful for future frames, and avoid
overfitting to background clutter, small parts of the target, or noise. By
enforcing a small number of update iterations during meta-learning, the
resulting networks train significantly faster. We demonstrate this approach on
top of the high performance tracking approaches: tracking-by-detection based
MDNet and the correlation based CREST. Experimental results on standard
benchmarks, OTB2015 and VOT2016, show that our meta-learned versions of both
trackers improve speed, accuracy, and robustness.

本文提出一种基于元学习的离线方法，以调整在线适应式跟踪中使用的初始深度网络，以实现更快速、准确和鲁棒的跟踪。实验证明，该方法在 OTB2015 和 VOT2016 标准基准测试中优于当前最先进的跟踪方法。