Imagine trying to track one particular fruitfly in a swarm of hundreds.
Higher biological visual systems have evolved to track moving objects by
relying on both appearance and motion features. We investigate if
state-of-the-art deep neural networks for visual tracking are capable of the
same. For this, we introduce PathTracker, a synthetic visual challenge that
asks human observers and machines to track a target object in the midst of
identical-looking "distractor" objects. While humans effortlessly learn
PathTracker and generalize to systematic variations in task design,
state-of-the-art deep networks struggle. To address this limitation, we
identify and model circuit mechanisms in biological brains that are implicated
in tracking objects based on motion cues. When instantiated as a recurrent
network, our circuit model learns to solve PathTracker with a robust visual
strategy that rivals human performance and explains a significant proportion of
their decision-making on the challenge. We also show that the success of this
circuit model extends to object tracking in natural videos. Adding it to a
transformer-based architecture for object tracking builds tolerance to visual
nuisances that affect object appearance, resulting in a new state-of-the-art
performance on the large-scale TrackingNet object tracking challenge. Our work
highlights the importance of building artificial vision models that can help us
better understand human vision and improve computer vision.

研究深度神经网络在视觉追踪中是否具有物体外观和运动特征模拟的能力，并提出了一种追踪对象基于动态线索电路模型，并将之加到基于转换器的体系结构进行追踪，使其具有更好的普遍性和鲁棒性。