Holistic, instance-level scene parsing with deep neural networks - Li, Q (2020)
Humans have long fantasised about the notion of a machine that sees and interprets a scene in ways that simulate the human visual system. As effortless as it appears to us, however, making sense of a scene is highly challenging for computers, due to the unbounded complexity and unlimited variations of a real-world scene. This thesis focuses on solving instance-aware pixel-level scene understanding with deep neural networks, which have potential applications in autonomous navigation, security systems, object counting, medical image analysis, amongst many others. This thesis first proposes an instance-level human parsing network, which is capable of producing category-, instance-, and part-level segmentation of human in a single forward pass. The proposed network is trained end-to-end given detections, and exploits a differentiable conditional random field (CRF) defined over a dynamic number of part instances for every image. It is the first work to perform human parsing at an instance level and can be trivially adapted to parse other objects. At the time of publication, it achieves state-of-the-art performance on public leaderboards.