GSA: guided supervised attention for enhancing real-time dense object detection via parameter-free scheme

Real-time object detection has consistently confronted the challenges of the trade-off between inference latency and detection accuracy, especially in dense scenarios. The attention mechanism is widely used in improving detection accuracy by obtaining the awareness of networks on the critical region of the raw inputs. Previous works proposed to generate attention along different feature dimensions to strengthen selective attention, but the optimal leverage of attention is still a challenging problem. We argue that the extra trainable parameters and lacking multi-scale capabilities induced by current attention designs make it difficult to detect dense targets accurately in real time. We propose a novel parameter-free attention called guided supervised attention (GSA). Instead of using trainable parameters, GSA innovatively explores the objectness from the detector’s output to generate a reliable spatial attention mask. With the inherent characteristic of label assignment, GSA is also scale-aware, thus improving the multi-scale capability for dense target scenarios. Combined with deep supervision techniques, such a parameter-free design ensures a low training and inference cost, thus realizing a good trade-off between speed and accuracy. Our experiments empirically prove that the one-stage method applied with GSA on the head obtains an average precision (AP) improvement of 0.2% to 1.1% on the CrowdHuman benchmark with negligible additional computational cost. Furthermore, with GSA, the YOLO series achieves an AP of 92.88% CrowdHuman, which is found to have the best AP among all the proposal-based methods, presenting a competitive performance while maintaining real-time inference. The study of the GSA introduces a novel application of objectness information in object detection.