5 accepted papers
Different from supervised semantic segmentation task, semi-supervised semantic segmentation (SSSS) aims to alleviate the burden of time-consuming pixel-wise manual labeling. Although existing methods have achieved the promising performance with a small amount of labeled images, they still suffer fro…
Feature Pyramid Network (FPN) plays a critical role and is indispensable for object detection methods. In recent years, attention mechanism has been utilized to improve FPN due to its excellent performance. Existing attention-based FPN methods generally work with a complex structure, resulting in an…
Attention mechanisms are widely adopted in existing scene parsing methods due to their excellent performance, especially spatial self-attention. However, spatial self-attention suffers from high computational complexity, which limits the practical applications of the scene parsing methods on mobile…
In real-world scenarios, data generally follows a long-tail distribution. Although a large number of works on long-tail object detection have emerged, they still suffer from an insufficient discriminative learning for tail categories. To alleviate this problem, we devise a novel Class-guided Triple…
Recently, a large number of channel attention blocks are proposed to boost the representational power of deep convolutional neural networks (CNNs). These approaches commonly learn the relationship between global contexts and attention activations by using fully-connected layers or linear transformat…