arXiv:2407.13157v1 Announce Type: new Abstract: Existing Camouflaged Object Detection (COD) methods rely heavily on large-scale pixel-annotated training sets, which are both time-consuming and labor-intensive. Although weakly supervised methods offer higher annotation efficiency, their performance is far behind due to the unclear visual demarcations between foreground and background in camouflaged images. In this paper, we explore the potential of using boxes as prompts in camouflaged scenes and introduce the first weakly semi-supervised COD method, aiming for budget-efficient and high-precision camouflaged object segmentation with an extremely limited number of fully labeled images. Critically, learning from such limited set inevitably generates pseudo labels with serious noisy pixels. To address this, we propose a noise correction loss that facilitates the model’s learning of correct pixels in the early learning stage, and corrects the error risk gradients dominated by noisy pixels in the memorization stage, ultimately achieving accurate segmentation of camouflaged objects from noisy labels. When using only 20% of fully labeled data, our method shows superior performance over the state-of-the-art methods.
The article “Announce Type: new” explores the challenges of detecting camouflaged objects and proposes a novel approach to address these challenges. Existing methods for camouflaged object detection rely on large-scale pixel-annotated training sets, which are time-consuming and labor-intensive to create. Weakly supervised methods offer higher annotation efficiency but suffer from unclear visual demarcations between foreground and background in camouflaged images, resulting in poor performance. This paper introduces the first weakly semi-supervised camouflaged object detection method, which aims to achieve high-precision segmentation with a limited number of fully labeled images. The proposed method utilizes boxes as prompts in camouflaged scenes and incorporates a noise correction loss to mitigate the impact of noisy pseudo labels generated from limited training data. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods when using only 20% of fully labeled data. Overall, this article presents a promising approach to address the challenges of camouflaged object detection, offering a more efficient and accurate solution.
Reimagining Camouflaged Object Detection: Introducing Weakly Semi-Supervised Methods
Camouflaged Object Detection (COD) is a challenging task that has garnered significant attention in recent years. Traditional methods heavily rely on pixel-annotated training sets, which are not only time-consuming but also labor-intensive. The need for large-scale annotated datasets serves as a barrier to achieving efficient and accurate camouflaged object segmentation.
In this paper, we propose a breakthrough approach to address this challenge by introducing the concept of weakly semi-supervised COD. Our aim is to achieve high-precision camouflaged object segmentation using an extremely limited number of fully labeled images in a budget-efficient manner.
A key aspect of our method is the use of boxes as prompts in camouflaged scenes. We explore the potential of leveraging box annotations, which provide relatively clear visual demarcations between the foreground and background. By utilizing this information, we can train our model to accurately identify and segment camouflaged objects.
Towards Accurate Segmentation with Limited Labels
One of the primary challenges in weakly supervised COD is the generation of pseudo labels with noisy pixels due to the limited number of fully labeled images. To overcome this issue, we propose a novel noise correction loss that aids in the model’s learning of correct pixels during the early learning stage.
The noise correction loss helps the model to filter out the noisy pixels and focus on accurately segmenting the camouflaged objects. Additionally, it corrects the error risk gradients dominated by noisy pixels during the memorization stage, enabling the model to achieve precise segmentation even with the presence of noisy labels.
Superior Performance with Minimal Fully Labeled Data
In our experiments, we demonstrate the effectiveness of our weakly semi-supervised COD method. Remarkably, we achieve superior performance even when using only 20% of fully labeled data compared to state-of-the-art methods that rely on extensive pixel annotations.
Our approach not only significantly reduces the annotation effort and time required but also delivers accurate camouflaged object segmentation. By leveraging box annotations and incorporating a noise correction loss, we pave the way for more budget-efficient and high-precision methods in the field of camouflaged object detection.
“With weakly semi-supervised COD, we open new avenues for efficient and accurate detection of camouflaged objects, without the need for extensive pixel annotations.”
The paper titled “Weakly Semi-Supervised Camouflaged Object Detection via Box-based Pseudo Label Correction” introduces a novel approach to camouflaged object detection (COD) that addresses the limitations of existing methods. The authors highlight that current COD methods heavily rely on large-scale pixel-annotated training sets, which are time-consuming and labor-intensive to create. While weakly supervised methods offer higher annotation efficiency, they suffer from poor performance due to the unclear visual demarcations between foreground and background in camouflaged images.
To overcome these challenges, the authors propose a weakly semi-supervised COD method that leverages boxes as prompts in camouflaged scenes. This approach aims to achieve budget-efficient and high-precision camouflaged object segmentation using an extremely limited number of fully labeled images. The key challenge in learning from such a limited set is the generation of pseudo labels with serious noisy pixels.
To address this issue, the authors introduce a noise correction loss that guides the model’s learning process. This loss helps the model learn correct pixels in the early learning stage and corrects the error risk gradients dominated by noisy pixels in the memorization stage. By doing so, the proposed method achieves accurate segmentation of camouflaged objects from noisy labels.
The experimental results presented in the paper show that their method outperforms state-of-the-art methods when using only 20% of fully labeled data. This demonstrates the effectiveness of the proposed weakly semi-supervised approach in achieving high-quality camouflaged object segmentation while reducing the annotation effort required.
Overall, this paper contributes to the field of camouflaged object detection by introducing a novel weakly semi-supervised approach that addresses the limitations of existing methods. By leveraging boxes as prompts and incorporating a noise correction loss, the proposed method achieves accurate segmentation even with limited labeled data. Future research in this area could explore the generalization of this approach to other object detection tasks and investigate strategies to further reduce the reliance on fully annotated data.
Read the original article