  Video objection detection is a challenging task because isolated video frames
may encounter appearance deterioration, which introduces great confusion for
detection. One of the popular solutions is to exploit the temporal information
and enhance per-frame representation through aggregating features from
neighboring frames. Despite achieving improvements in detection, existing
methods focus on the selection of higher-level video frames for aggregation
rather than modeling lower-level temporal relations to increase the feature
representation. To address this limitation, we propose a novel solution named
TF-Blender,which includes three modules: 1) Temporal relation mod-els the
relations between the current frame and its neighboring frames to preserve
spatial information. 2). Feature adjustment enriches the representation of
every neigh-boring feature map; 3) Feature blender combines outputs from the
first two modules and produces stronger features for the later detection tasks.
For its simplicity, TF-Blender can be effortlessly plugged into any detection
network to improve detection behavior. Extensive evaluations on ImageNet VID
and YouTube-VIS benchmarks indicate the performance guarantees of using
TF-Blender on recent state-of-the-art methods.
