Title: Push and Pull:  Competing Feature-Prototype Interactions  Improve Semi-supervised Semantic Segmentation. Abstract: This paper challenges semi-supervised segmentation with a rethink on the feature-prototype interaction in the classification head. Specifically, we view each weight vector in the classification head as the prototype of a semantic category. The basic practice in the softmax classifier is to pull a feature towards its positive prototype (i.e., the prototype of its class), as well as to push it away from its negative prototypes. In this paper, we focus on the interaction between the feature and its negative prototypes, which is always “pushing” to make them dissimilar. While the pushing-away interaction is necessary, this paper reveals a new mechanism that the contrary interaction of pulling close negative prototypes is also beneficial. We have two insights for this counter-intuitive interaction: 1) some pseudo negative prototypes might actually be positive so that the pulling interaction can help resisting the pseudo-label noises, and 2) some true negative prototypes might contain contextual information that is beneficial. Therefore, we integrate these two competing interactions into a Push-and-Pull Learning (PPL) method. On the one hand, PPL introduces the novel pulling-close interaction between features and negative prototypes with a feature-to-prototype attention. On the other hand, PPL reinforces the original pushing-away interaction with a multi-prototype contrastive learning. While PPL is very simple, experiments show that it substantially improves semi-supervised segmentation and sets a new state of the art.