Robust Tracking via Bidirectional Transduction With Mask Information

In the tracking literature, foreground and background information have been extensively investigated to discriminate a target from its surrounding background. However, both foreground and background possess their own spatial-temporal correlation relationship that provide significant information to separate the target from its surrounding background, which has been usually ignored by existing work. To address this issue, we propose a bidirectional transductive network based tracker, which incorporates long-range spatial-temporal and bidirectional constraints. Specifically, our tracker consists of two modules, namely the mask generation module (MGM) and the transduction attention module (TAM). MGM aggregates long-range interdependencies of a target along the history frames for generating accurate target masks. TAM retrieves back to the history frames to find patches similar to the current frame, which are then forwarded along with the target masks generated by MGM. In this manner, each position in the current frame can determine its own identity, whether belonging to either the background or the foreground, hence accurately distinguishing the target from its distractors. We conduct systematically experiments and achieve state-of-the-art performance on several benchmarks, obtaining 69.2% AO on GOT-10k and 82.1% on TrackingNet.