OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers

• URL: http://arxiv.org/abs/2207.02255v1
• Date: Tue, 5 Jul 2022 18:32:21 GMT
• Title: OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers
• Abstract summary: OSFormer is a one-stage transformer framework for camouflaged instance segmentation (CIS) We design a location-sensing transformer (LST) to obtain the location label and instance-aware parameters. We develop a coarse-to-fine fusion (CFF) to merge diverse context information from the LST encoder and CNN backbone.
• Score: 79.09486658392626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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