TF-Blender: Temporal Feature Blender for Video Object Detection

Yiming Cui, Liqi Yan, Zhiwen Cao, Dongfang Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 8138-8147


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 models the relations between the current frame and its neighboring frames to preserve spatial information. 2). Feature adjustment enriches the representation of every neighboring 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.

Related Material

@InProceedings{Cui_2021_ICCV, author = {Cui, Yiming and Yan, Liqi and Cao, Zhiwen and Liu, Dongfang}, title = {TF-Blender: Temporal Feature Blender for Video Object Detection}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {8138-8147} }