Feature Aggregation and Propagation Network for Camouflaged Object Detection

• URL: http://arxiv.org/abs/2212.00990v1
• Date: Fri, 2 Dec 2022 05:54:28 GMT
• Title: Feature Aggregation and Propagation Network for Camouflaged Object Detection
• Authors: Tao Zhou, Yi Zhou, Chen Gong, Jian Yang, Yu Zhang
• Abstract summary: Camouflaged object detection (COD) aims to detect/segment camouflaged objects embedded in the environment. Several COD methods have been developed, but they still suffer from unsatisfactory performance due to intrinsic similarities between foreground objects and background surroundings. We propose a novel Feature Aggregation and propagation Network (FAP-Net) for camouflaged object detection.
• Score: 42.33180748293329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Camouflaged object detection (COD) aims to detect/segment camouflaged objects embedded in the environment, which has attracted increasing attention over the past decades. Although several COD methods have been developed, they still suffer from unsatisfactory performance due to the intrinsic similarities between the foreground objects and background surroundings. In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection. Specifically, we propose a Boundary Guidance Module (BGM) to explicitly model the boundary characteristic, which can provide boundary-enhanced features to boost the COD performance. To capture the scale variations of the camouflaged objects, we propose a Multi-scale Feature Aggregation Module (MFAM) to characterize the multi-scale information from each layer and obtain the aggregated feature representations. Furthermore, we propose a Cross-level Fusion and Propagation Module (CFPM). In the CFPM, the feature fusion part can effectively integrate the features from adjacent layers to exploit the cross-level correlations, and the feature propagation part can transmit valuable context information from the encoder to the decoder network via a gate unit. Finally, we formulate a unified and end-to-end trainable framework where cross-level features can be effectively fused and propagated for capturing rich context information. Extensive experiments on three benchmark camouflaged datasets demonstrate that our FAP-Net outperforms other state-of-the-art COD models. Moreover, our model can be extended to the polyp segmentation task, and the comparison results further validate the effectiveness of the proposed model in segmenting polyps. The source code and results will be released at https://github.com/taozh2017/FAPNet.

Related papers

• Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection [57.883265488038134]
  We propose a hierarchical graph interaction network termed HGINet for camouflaged object detection. The network is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Our experiments demonstrate the superior performance of HGINet compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-27T12:53:25Z)
• CoFiNet: Unveiling Camouflaged Objects with Multi-Scale Finesse [46.79770062391987]
  We introduce a novel method for camouflage object detection, named CoFiNet. Our approach focuses on multi-scale feature fusion and extraction, with special attention to the model's segmentation effectiveness. CoFiNet achieves state-of-the-art performance across all datasets.
  arXiv  Detail & Related papers  (2024-02-03T17:24:55Z)
• Unveiling Camouflage: A Learnable Fourier-based Augmentation for Camouflaged Object Detection and Instance Segmentation [27.41886911999097]
  We propose a learnable augmentation method for camouflaged object detection (COD) and camouflaged instance segmentation (CIS) Our proposed augmentation method boosts the performance of camouflaged object detectors and camouflaged instance segmenters by large margins.
  arXiv  Detail & Related papers  (2023-08-29T22:43:46Z)
• Frequency Perception Network for Camouflaged Object Detection [51.26386921922031]
  We propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain. Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage. Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-17T11:30:46Z)
• DQnet: Cross-Model Detail Querying for Camouflaged Object Detection [54.82390534024954]
  A convolutional neural network (CNN) for camouflaged object detection tends to activate local discriminative regions while ignoring complete object extent. In this paper, we argue that partial activation is caused by the intrinsic characteristics of CNN. In order to obtain feature maps that could activate full object extent, a novel framework termed Cross-Model Detail Querying network (DQnet) is proposed.
  arXiv  Detail & Related papers  (2022-12-16T06:23:58Z)
• Camouflaged Object Detection via Context-aware Cross-level Fusion [10.942917945534678]
  Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. We propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features. C2F-Net is an effective COD model and outperforms state-of-the-art (SOTA) models remarkably.
  arXiv  Detail & Related papers  (2022-07-27T08:34:16Z)
• Towards Accurate Camouflaged Object Detection with Mixture Convolution and Interactive Fusion [45.45231015502287]
  We propose a novel deep learning based COD approach, which integrates the large receptive field and effective feature fusion into a unified framework. Our method detects camouflaged objects with an effective fusion strategy, which aggregates the rich context information from a large receptive field.
  arXiv  Detail & Related papers  (2021-01-14T16:06:08Z)
• Cross-layer Feature Pyramid Network for Salient Object Detection [102.20031050972429]
  We propose a novel Cross-layer Feature Pyramid Network to improve the progressive fusion in salient object detection. The distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information.
  arXiv  Detail & Related papers  (2020-02-25T14:06:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.