OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers
Jialun Pei, Tianyang Cheng, Deng-Ping Fan, He Tang, Chuanbo Chen, Luc Van Gool
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 feed-forward 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."
BibTeX
@inproceedings{eccv2022_osformeronestage,
title = {OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers},
author = {Jialun Pei and Tianyang Cheng and Deng-Ping Fan and He Tang and Chuanbo Chen and Luc Van Gool},
booktitle = {ECCV 2022},
year = {2022}
}