Pixel-Semantic Revise of Position Learning A One-Stage Object Detector with A Shared Encoder-Decoder
Recently, many methods have been proposed for object detection. They cannot detect objects by semantic features, adaptively. In this work, according to channel and spatial attention mechanisms, we mainly analyze that different methods detect objects adaptively. Some state-of-the-art detectors combin...
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Zusammenfassung: | Recently, many methods have been proposed for object detection. They cannot
detect objects by semantic features, adaptively. In this work, according to
channel and spatial attention mechanisms, we mainly analyze that different
methods detect objects adaptively. Some state-of-the-art detectors combine
different feature pyramids with many mechanisms to enhance multi-level semantic
information. However, they require more cost. This work addresses that by an
anchor-free detector with shared encoder-decoder with attention mechanism,
extracting shared features. We consider features of different levels from
backbone (e.g., ResNet-50) as the basis features. Then, we feed the features
into a simple module, followed by a detector header to detect objects.
Meantime, we use the semantic features to revise geometric locations, and the
detector is a pixel-semantic revising of position. More importantly, this work
analyzes the impact of different pooling strategies (e.g., mean, maximum or
minimum) on multi-scale objects, and finds the minimum pooling improve
detection performance on small objects better. Compared with state-of-the-art
MNC based on ResNet-101 for the standard MSCOCO 2014 baseline, our method
improves detection AP of 3.8%. |
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DOI: | 10.48550/arxiv.2001.01057 |