Pixel-Semantic Revise of Position Learning A One-Stage Object Detector with A Shared Encoder-Decoder
This work addresses object detection for computer vision applications, but it is incremental as it builds on existing attention and anchor-free methods.
The paper tackles object detection by proposing an anchor-free detector with a shared encoder-decoder and attention mechanism to adaptively detect objects using semantic features, achieving a 3.8% improvement in AP on MSCOCO 2014 compared to a state-of-the-art baseline.
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%.