Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers (2303.14816v1)

Abstract: Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection. However, they suffer from two major limitations: less effective locality modeling and insufficient feature aggregation in decoders, which are not conducive to camouflaged object detection that explores subtle cues from indistinguishable backgrounds. To address these issues, in this paper, we propose a novel transformer-based Feature Shrinkage Pyramid Network (FSPNet), which aims to hierarchically decode locality-enhanced neighboring transformer features through progressive shrinking for camouflaged object detection. Specifically, we propose a nonlocal token enhancement module (NL-TEM) that employs the non-local mechanism to interact neighboring tokens and explore graph-based high-order relations within tokens to enhance local representations of transformers. Moreover, we design a feature shrinkage decoder (FSD) with adjacent interaction modules (AIM), which progressively aggregates adjacent transformer features through a layer-bylayer shrinkage pyramid to accumulate imperceptible but effective cues as much as possible for object information decoding. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms the existing 24 competitors on three challenging COD benchmark datasets under six widely-used evaluation metrics. Our code is publicly available at https://github.com/ZhouHuang23/FSPNet.

• The paper introduces FSPNet, a transformer-based framework that integrates global context with local feature shrinkage to tackle camouflaged object detection.
• It leverages a Vision Transformer encoder, a non-local token enhancement module, and a feature shrinkage decoder to preserve subtle object details.
• Experiments on CAMO, COD10K, and NC4K benchmarks demonstrate significant improvements over 24 competing methods.

Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers

The paper introduces a transformer-based Feature Shrinkage Pyramid Network (FSPNet) tailored for the task of camouflaged object detection (COD). Vision transformers, well-regarded for their capability to model global contexts, face limitations in encoding locality, which is crucial for detecting camouflaged objects hidden in complex backgrounds. FSPNet addresses this through a unique architecture combining transformers with mechanisms that enhance local feature representation and aggregation.

Key Contributions and Methodology

1. Vision Transformer Encoder: The authors leverage vision transformers (ViTs) as a backbone for encoding global contexts. The ViT processes input images by serializing them into tokens to model intricate dependencies within the data effectively.
2. Non-local Token Enhancement Module (NL-TEM): This module serves to enhance locality modeling within tokens by implementing non-local operations that facilitate feature interaction and graph-based high-order semantic relations within token sequences. NL-TEM processes adjacent tokens to extract essential local cues necessary for distinguishing subtle differences in camouflaged objects.
3. Feature Shrinkage Decoder (FSD): Designed to aggregate features progressively using a layer-by-layer shrinkage pyramid strategy, the FSD hierarchically decodes neighboring localized transformer features. The adjacent interaction modules (AIMs) within the pyramid selectively merge features, reducing the loss of inconspicuous but crucial object details and enhancing object information decoding.

Experimental Results

The paper reports extensive experimentation on three challenging COD benchmark datasets—CAMO, COD10K, and NC4K—demonstrating significant performance enhancement over 24 competing methods. Notably, the model outperforms the best performing current models such as ZoomNet and SINet-v2, indicating robustness in diverse scenarios, including small, large, multiple, occluded, and boundary-uncertain camouflaged objects.

Quantitatively, FSPNet shows improvements in structural similarity ($S_m$), weighted F-measure ($F^{\omega}_{\beta}$), and other salient measures, underscoring its efficacy in precise object localization and segmentation.

Implications and Future Directions

FSPNet enhances the COD domain by compellingly integrating transformers with innovative local exploration mechanisms and a sophisticated decoder design, suggesting potential applications across fields requiring precise segmentation, such as medical image processing and industrial inspection.

The paper opens paths for further transformer-based exploration in COD, emphasizing the importance of combining global feature encoding with local representation enhancement, suggesting future research could hone transformer architectures specifically for complex object detection scenarios and improve real-time processing applications.

In conclusion, while FSPNet marks significant advancements, it lays the groundwork for deeper inquiries into transformer applicability and optimization in similar COD tasks, prompting researchers to explore augmentation strategies that bolster the visual perception of automated systems under challenging conditions.