TF-Blender: Temporal Feature Blender for Video Object Detection

• URL: http://arxiv.org/abs/2108.05821v1
• Date: Thu, 12 Aug 2021 16:01:34 GMT
• Title: TF-Blender: Temporal Feature Blender for Video Object Detection
• Authors: Yiming Cui, Liqi Yan, Zhiwen Cao, Dongfang Liu
• Abstract summary: Video objection detection is a challenging task because isolated video frames may encounter appearance deterioration. We propose 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. For its simplicity, TF-Blender can be effortlessly plugged into any detection network to improve detection behavior.
• Score: 6.369234802164117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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