  We extend the classical tracking-by-detection paradigm to this
tracking-any-object task. Solid detection results are first extracted from TAO
dataset. Some state-of-the-art techniques like \textbf{BA}lanced-\textbf{G}roup
\textbf{S}oftmax (\textbf{BAGS}\cite{li2020overcoming}) and
DetectoRS\cite{qiao2020detectors} are integrated during detection. Then we
learned appearance features to represent any object by training feature
learning networks. We ensemble several models for improving detection and
feature representation. Simple linking strategies with most similar appearance
features and tracklet-level post association module are finally applied to
generate final tracking results. Our method is submitted as \textbf{AOA} on the
challenge website. Code is available at
https://github.com/feiaxyt/Winner_ECCV20_TAO.
