3 research outputs found
Dual Semantic Fusion Network for Video Object Detection
Video object detection is a tough task due to the deteriorated quality of
video sequences captured under complex environments. Currently, this area is
dominated by a series of feature enhancement based methods, which distill
beneficial semantic information from multiple frames and generate enhanced
features through fusing the distilled information. However, the distillation
and fusion operations are usually performed at either frame level or instance
level with external guidance using additional information, such as optical flow
and feature memory. In this work, we propose a dual semantic fusion network
(abbreviated as DSFNet) to fully exploit both frame-level and instance-level
semantics in a unified fusion framework without external guidance. Moreover, we
introduce a geometric similarity measure into the fusion process to alleviate
the influence of information distortion caused by noise. As a result, the
proposed DSFNet can generate more robust features through the multi-granularity
fusion and avoid being affected by the instability of external guidance. To
evaluate the proposed DSFNet, we conduct extensive experiments on the ImageNet
VID dataset. Notably, the proposed dual semantic fusion network achieves, to
the best of our knowledge, the best performance of 84.1\% mAP among the current
state-of-the-art video object detectors with ResNet-101 and 85.4\% mAP with
ResNeXt-101 without using any post-processing steps.Comment: 9 pages,6 figure