In contrast to the human vision that mainly depends on the shape for
recognizing the objects, deep image recognition models are widely known to be
biased toward texture. Recently, Meta research team has released the first
foundation model for image segmentation, termed segment anything model (SAM),
which has attracted significant attention. In this work, we understand SAM from
the perspective of texture \textit{v.s.} shape. Different from label-oriented
recognition tasks, the SAM is trained to predict a mask for covering the object
shape based on a promt. With this said, it seems self-evident that the SAM is
biased towards shape. In this work, however, we reveal an interesting finding:
the SAM is strongly biased towards texture-like dense features rather than
shape. This intriguing finding is supported by a novel setup where we
disentangle texture and shape cues and design texture-shape cue conflict for
mask prediction.

对比人类视觉主要依赖形状来识别物体，深度图像识别模型被广泛认为存在对纹理的偏见。最近，元研究团队发布了首个图像分割基础模型，称为 Segment Anything Model（SAM），这引起了很大的关注。在本研究中，我们从纹理和形状的角度理解了 SAM。与以标签为导向的识别任务不同，SAM 被训练为基于提示预测覆盖物体形状的掩码。然而，在本研究中，我们揭示了一个有趣的发现：SAM 对纹理样式的密集特征更具偏向性，而非形状。这一有趣的发现得到了一个新颖的设置的支持，在该设置中我们将纹理和形状提示分离，并构建了纹理 - 形状提示冲突进行掩码预测。