이미지 분할
각 픽셀이 어느 물체에 속하는지 지정한다
이 페이지의 본문은 영어로 제공됩니다. 제목과 요약은 한국어로 번역되었습니다.
이 능력이 뜻하는 것
Takes an image and outputs a mask the size of the input, labelling each pixel with its class or its instance. It is finer than detection because it gives outlines rather than boxes. Semantic segmentation only distinguishes classes (all of them are people), while instance segmentation also separates individuals (A and B are different people); the two are often lumped together as segmentation.
기술적으로 구현하는 방법
The classic design is an encoder–decoder: the encoder downsamples to extract semantics, the decoder upsamples to restore resolution, and skip connections carry high-resolution detail from shallow layers, with U-Net and fully convolutional networks as landmarks. Instance segmentation often detects first and predicts a mask inside each box (the Mask R-CNN line), or uses a promptable segment-anything model cued by clicks or boxes to isolate arbitrary objects.
대표 제품
4Gemini
2023네이티브 멀티모달에 초장문 문맥을 다루는 범용 모델
Qwen
2023여러 규모와 멀티모달 버전을 아우르는 오픈웨이트 모델 계열
SenseAvatar
2022한 장의 인물 사진과 음성으로 입모양이 맞는 디지털 휴먼 영상을 생성한다
GPT-4o
2024네이티브 멀티모달 범용 모델. 텍스트·이미지·오디오를 한 창구에서 다룬다
관련 기관
대표적 용도
- Organ and lesion delineation in medical imaging
- Land-cover classification in remote sensing
- Drivable area and obstacles for driving
- Selection masks for image editing
성능을 평가하는 방법
- IoU / mIoU
- Intersection over union of masks, averaged over classes
- Dice coefficient
- Common in medical imaging, more sensitive than IoU for small targets
- Boundary F-score
- Judges only contour fit rather than large correct interiors
경계와 난점
- Thin structures and boundaries — hair, wires, vessel tips — break or lose their thinness
- Adjacent or overlapping instances of the same class merge into one blob
- Masks are inaccurate on transparent, reflective or low-contrast materials