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Camouflaged Object Segmentation with a Single Task-generic Prompt

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 0
평가표: 3
보존된 평가 기록: 8

The previous fully-supervised and weakly-supervised camouflaged object segmentation tasks required a significant amount of annotated data for supervised training to enable models to segment camouflaged objects effectively. However, models like the Segment Anything Model (SAM), which falls under the category of Promptable Segmentation models, can achieve excellent segmentation performance on unseen images with just an instance-specific visual prompt. Nevertheless, for complex scenarios like camouflaged objects, SAM may not perform well even with an instance-specific prompt. Furthermore, the question arises: Is an instance-specific prompt necessary? In more realistic scenarios, where only a task-generic task description is provided as a universally applicable text prompt, how can we improve segmentation across various datasets under the camouflaged object segmentation task?

데이터셋

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COD10K

Sensory ecologists have found that this s background matching camouflage strategy works by deceiving the visual perceptual system of the observer. Naturally, addressing concealed object detection (COD) requires a significant amount of visual perception knowledge. Understanding COD has not only scientific value in itself, but it also important for applications in many fundamental fields, such as computer vision (e.g., for search-and-rescue work, or rare species discovery), medicine (e.g., polyp segmentation, lung infection segmentation), agriculture (e.g., locust detection to prevent invasion), and art (e.g., recreational art). The high intrinsic similarities between the targets and non-targets make COD far more challenging than traditional object segmentation/detection. Although it has gained increased attention recently, studies on COD still remain scarce, mainly due to the lack of a sufficiently large dataset and a standard benchmark like Pascal-VOC, ImageNet, MS-COCO, ADE20K, and DAVIS. To build the large-scale COD dataset, we build the COD10K, which contains 10,000 images (5,066 camouflaged, 3,000 background, 1,934 noncamouflaged), divided into 10 super-classes, and 78 sub-classes (69 camouflaged, nine non-camouflaged) which are collected from multiple photography websites.

CAMO

Camouflaged Object (CAMO) dataset specifically designed for the task of camouflaged object segmentation. We focus on two categories, i.e., naturally camouflaged objects and artificially camouflaged objects, which usually correspond to animals and humans in the real world, respectively. Camouflaged object images consists of 1250 images (1000 images for the training set and 250 images for the testing set). Non-camouflaged object images are collected from the MS-COCO dataset (1000 images for the training set and 250 images for the testing set). CAMO has objectness mask ground-truth.

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