  Visual object tracking performance has been dramatically improved in recent
years, but some severe challenges remain open, like distractors and occlusions.
We suspect the reason is that the feature representations of the tracking
targets are only expressively learned but not fully discriminatively modeled.
In this paper, we propose a novel Instance-Aware Tracker (IAT) to explicitly
excavate the discriminability of feature representations, which improves the
classical visual tracking pipeline with an instance-level classifier. First, we
introduce a contrastive learning mechanism to formulate the classification
task, ensuring that every training sample could be uniquely modeled and be
highly distinguishable from plenty of other samples. Besides, we design an
effective negative sample selection scheme to contain various intra and inter
classes in the instance classification branch. Furthermore, we implement two
variants of the proposed IAT, including a video-level one and an object-level
one. They realize the concept of \textbf{instance} in different granularity as
videos and target bounding boxes, respectively. The former enhances the ability
to recognize the target from the background while the latter boosts the
discriminative power for mitigating the target-distractor dilemma. Extensive
experimental evaluations on 8 benchmark datasets show that both two versions of
the proposed IAT achieve leading results against state-of-the-art methods while
running at 30FPS. Code will be available when it is published.
