用于视觉定位自监督表征学习的差异注意力学习
Learning with Difference Attention for Visually Grounded Self-supervised Representations
AI总结：
针对自监督学习在多目标图像视觉定位上的不足，提出视觉差异注意力（VDA）揭示现有方法缺陷，并设计可微差异注意力（DiDA）损失，大幅提升模型视觉定位能力。
摘要：
自监督学习的近期工作在单目标图像上取得了显著成果，但在复杂多目标图像上表现不佳，其糟糕的视觉定位能力证明了这一点。为具体说明这一点，我们提出了视觉差异注意力（VDA），通过将图像与其显著区域被掩码的版本进行比较，以无监督方式计算视觉注意力图。我们将VDA应用于最先进的SSL方法以生成注意力图，结果表明它们无法准确突出图像中的所有显著区域，这意味着它们无法为分割等下游任务学习到强大的表征。受这些局限性启发，我们将VDA转化为可微操作，并提出了新的学习目标——可微差异注意力（DiDA）损失，这使SSL模型的视觉定位到图像显著区域的能力获得了大幅提升。
英文摘要：
Recent works in self-supervised learning have shown impressive results on single-object images, but they struggle to perform well on complex multi-object images as evidenced by their poor visual grounding. To demonstrate this concretely, we propose visual difference attention (VDA) to compute visual attention maps in an unsupervised fashion by comparing an image with its salient-regions-masked-out version. We use VDA to derive attention maps for state-of-the art SSL methods and show they do not highlight all salient regions in an image accurately, suggesting their inability to learn strong representations for downstream tasks like segmentation. Motivated by these limitations, we cast VDA as a differentiable operation and propose a new learning objective, Differentiable Difference Attention (DiDA) loss, which leads to substantial improvements in an SSL model's visually grounding to an image's salient regions.
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