FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learning

• URL: http://arxiv.org/abs/2603.22969v1
• Date: Tue, 24 Mar 2026 09:05:41 GMT
• Title: FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learning
• Abstract summary: Existing camouflage object detection (COD) methods rely on fully-supervised learning guided by mask annotations.<n>Compared to fully-supervised methods, existing weakly-supervised COD methods exhibit significantly poorer performance.<n>We propose a frequency-aware and contrastive learning-based WSCOD framework in this paper, named FCL-COD.
• Score: 49.58338471202117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing camouflage object detection (COD) methods typically rely on fully-supervised learning guided by mask annotations. However, obtaining mask annotations is time-consuming and labor-intensive. Compared to fully-supervised methods, existing weakly-supervised COD methods exhibit significantly poorer performance. Even for the Segment Anything Model (SAM), there are still challenges in handling weakly-supervised camouflage object detection (WSCOD), such as: a. non-camouflage target responses, b. local responses, c. extreme responses, and d. lack of refined boundary awareness, which leads to unsatisfactory results in camouflage scenes. To alleviate these issues, we propose a frequency-aware and contrastive learning-based WSCOD framework in this paper, named FCL-COD. To mitigate the problem of non-camouflaged object responses, we propose the Frequency-aware Low-rank Adaptation (FoRA) method, which incorporates frequency-aware camouflage scene knowledge into SAM. To overcome the challenges of local and extreme responses, we introduce a gradient-aware contrastive learning approach that effectively delineates precise foreground-background boundaries. Additionally, to address the lack of refined boundary perception, we present a multi-scale frequency-aware representation learning strategy that facilitates the modeling of more refined boundaries. We validate the effectiveness of our approach through extensive empirical experiments on three widely recognized COD benchmarks. The results confirm that our method surpasses both state-of-the-art weakly supervised and even fully supervised techniques.

Related papers

• Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance [49.06684374007753]
  We introduce a new weakly supervised approach for camouflaged object detection to overcome limitations.<n>Specifically, we propose a novel network, MGNet, which tackles edge ambiguity and missed detections.<n>For the weak supervision challenge, we propose BoxSAM, which leverages the Segment Anything Model (SAM) with bounding-box prompts to generate pseudo-labels.
  arXiv  Detail & Related papers  (2026-05-25T03:26:13Z)
• A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection [13.109309606764754]
  We introduce a plug-in detection framework that leverages internal layer-wise inconsistencies within the target model itself.<n>Our method achieves state-of-the-art detection performance with negligible computational overhead.
  arXiv  Detail & Related papers  (2025-05-19T00:48:53Z)
• A Knowledge-guided Adversarial Defense for Resisting Malicious Visual Manipulation [93.28532038721816]
  Malicious applications of visual manipulation have raised serious threats to the security and reputation of users in many fields.<n>We propose a knowledge-guided adversarial defense (KGAD) to actively force malicious manipulation models to output semantically confusing samples.
  arXiv  Detail & Related papers  (2025-04-11T10:18:13Z)
• CGCOD: Class-Guided Camouflaged Object Detection [19.959268087062217]
  We introduce class-guided camouflaged object detection (CGCOD), which extends traditional COD task by incorporating object-specific class knowledge.<n>We propose a multi-stage framework, CGNet, which incorporates a plug-and-play class prompt generator and a simple yet effective class-guided detector.<n>This establishes a new paradigm for COD, bridging the gap between contextual understanding and class-guided detection.
  arXiv  Detail & Related papers  (2024-12-25T19:38:32Z)
• Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection [73.85890512959861]
  We propose a task-agnostic framework to unify Salient Object Detection (SOD) and Camouflaged Object Detection (COD)<n>We design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps.<n> Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings.
  arXiv  Detail & Related papers  (2024-12-22T03:25:43Z)
• SAM-COD: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection [12.915303163650002]
  Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches exhibit significantly inferior performance compared to fully-supervised methods. We propose a unified COD framework, termed SAM-COD, which is capable of supporting arbitrary weakly-supervised labels.
  arXiv  Detail & Related papers  (2024-08-20T11:49:27Z)
• Learning Camouflaged Object Detection from Noisy Pseudo Label [60.9005578956798]
  This paper introduces the first weakly semi-supervised Camouflaged Object Detection (COD) method. It aims for budget-efficient and high-precision camouflaged object segmentation with an extremely limited number of fully labeled images. We propose a noise correction loss that facilitates the model's learning of correct pixels in the early learning stage. When using only 20% of fully labeled data, our method shows superior performance over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-18T04:53:51Z)
• USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model [14.080744645704751]
  Open World Object Detection (OWOD) is a novel and challenging computer vision task. We propose a simple yet effective learning strategy, namely Decoupled Objectness Learning (DOL), which divides the learning of these two boundaries into decoder layers. We also introduce an Auxiliary Supervision Framework (ASF) that uses a pseudo-labeling and a soft-weighting strategies to alleviate the negative impact of noise.
  arXiv  Detail & Related papers  (2023-06-04T06:42:09Z)
• CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models [72.93652777646233]
  Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. We propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models. Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask.
  arXiv  Detail & Related papers  (2023-05-29T07:49:44Z)

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.