OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers (2207.02255v3)

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.

• The paper introduces a transformer-based one-stage framework that enhances camouflaged instance segmentation through a novel integration of location-sensing and coarse-to-fine feature fusion.
• It employs a Location-Sensing Transformer that captures both location and instance-aware features, enabling faster convergence even with limited training data.
• The approach achieves a 41% average precision on the COD10K test set, outperforming popular models like Mask R-CNN and SOLOv2 with minimal computational overhead.

Overview of "OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers"

The paper "OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers" introduces a novel approach to camouflaged instance segmentation (CIS) using a transformer-based model, named OSFormer. The framework is a significant contribution, proposing a one-stage strategy grounded on the principles of transformer architecture to address the challenges associated with detecting and segmenting camouflaged objects. These objects present a substantial difficulty due to their intrinsic ability to blend into complex backgrounds, a characteristic derived from biological strategies like background matching and disruptive coloration.

Key Components and Innovations

OSFormer is constructed around two main components: the Location-Sensing Transformer (LST) and the Coarse-to-Fine Fusion (CFF) mechanism.

1. Location-Sensing Transformer (LST): The LST is pivotal in OSFormer, designed to capture both location and instance-aware features through the use of location-guided queries. This design bypasses the limitations of zero-initialized queries as seen in traditional DETR models, leading to faster convergence and improved detection performance even with a limited data set of approximately 3,040 training samples.
2. Coarse-to-Fine Fusion (CFF): CFF merges context information from the LST encoder with features from a CNN backbone. This fusion helps in the efficient blending of local and global features, which is crucial for the prediction of camouflaged instances. The integration of a Reverse Edge Attention (REA) module within CFF further refines edge detection, a critical aspect when dealing with camouflaged instances.

Numerical Results and Performance

The performance of OSFormer is evaluated against 11 popular instance segmentation models. The results demonstrate a significant improvement, achieving an AP of 41% on the COD10K test set, surpassing existing Mask R-CNN and SOLOv2 variants by a notable margin. This performance is achieved with minimal computational demands, indicating a high degree of efficiency suitable for real-world applications.

Implications and Future Directions

The implications of OSFormer extend into various sectors where camouflage detection is critical, such as wildlife monitoring, medical imaging (e.g., polyp and lung infection segmentation), and defense. The methodology presented sets a precedent for employing transformer-based architectures in similar contexts of instance segmentation where nuanced object detection is required.

Looking forward, this work paves the way for further exploration into more efficient transformer designs capable of a generalized approach to a variety of segmentation tasks. Enhancements might focus on refining aspects of the attention mechanisms and improving the integration with convolutional networks to reduce computational overhead further.

Furthermore, the prospects of extending this research to other downstream tasks with limited data availability could accelerate the development of autonomous systems with enhanced visual perception capabilities.