We present OSFormer, the first one-stage transformer framework for camouflaged instance segmentation (CIS). OSFormer is based on two key designs. First, we design a location-sensing transformer (LST) to obtain the location label and instance-aware parameters by introducing the location-guided queries and the blend-convolution feedforward network. Second, we develop a coarse-to-fine fusion (CFF) to merge diverse context information from the LST encoder and CNN backbone. Coupling these two components enables OSFormer to efficiently blend local features and long-range context dependencies for predicting camouflaged instances. Compared with two-stage frameworks, our OSFormer reaches 41% AP and achieves good convergence efficiency without requiring enormous training data, i.e., only 3,040 samples under 60 epochs. Code link: https://github.com/PJLallen/OSFormer.

OSFormer是第一个使用一种阶段变压器框架来进行伪装实例分割（CIS）的研究，包括关键设计的位置感知变压器（LST）和粗到细的融合（CFF）来融合来自LST编码器和CNN主干的多样化上下文信息，在不需要大量训练数据（仅60个时期下的3,040个样本）的情况下，OSFormer达到了41％AP，并实现了良好的收敛效率。

OSFormer: 基于Transformers的单阶段伪装实例分割