论文标题
合成数据监督显着对象检测
Synthetic Data Supervised Salient Object Detection
论文作者
论文摘要
尽管深层对象检测（SOD）取得了显着的进展，但深层SOD模型非常渴望数据，需要大规模像素的注释才能提供如此有希望的结果。在本文中，我们提出了一种新颖但有效的方法，用于SOD，即造成的Sodgan，该方法可以生成无限的高质量图像面罩对，只需要少数标记的数据，这些合成的对可以替代人类标记的DUTS-TR来训练任何现成的SOD模型。它的贡献是三倍。 1）我们提出的扩散嵌入网络可以解决流式不匹配，并且对于潜在代码生成而言是可以解决的，可以更好地与Imagenet潜在空间匹配。 2）首次，我们提出的几个镜头显着性蒙版生成器可以通过几个标记的数据合成无限的精确图像同步显着性掩码。 3）我们提出的质量意识歧视器可以从嘈杂的合成数据库中选择高质量合成的图像面罩对，从而提高了合成数据的质量。我们的Sodgan首次通过直接从生成模型生成的合成数据来解决SOD，这为SOD打开了新的研究范式。广泛的实验结果表明，经过合成数据训练的显着性模型可以实现$ 98.4 \％$ f-f-MEASET在DUTS-TR上训练的显着性模型。此外，我们的方法在半/弱监督的方法中实现了新的SOTA性能，甚至超过了几种完全监督的SOTA方法。代码可从https://github.com/wuzhenyubuaa/sodgan获得
Although deep salient object detection (SOD) has achieved remarkable progress, deep SOD models are extremely data-hungry, requiring large-scale pixel-wise annotations to deliver such promising results. In this paper, we propose a novel yet effective method for SOD, coined SODGAN, which can generate infinite high-quality image-mask pairs requiring only a few labeled data, and these synthesized pairs can replace the human-labeled DUTS-TR to train any off-the-shelf SOD model. Its contribution is three-fold. 1) Our proposed diffusion embedding network can address the manifold mismatch and is tractable for the latent code generation, better matching with the ImageNet latent space. 2) For the first time, our proposed few-shot saliency mask generator can synthesize infinite accurate image synchronized saliency masks with a few labeled data. 3) Our proposed quality-aware discriminator can select highquality synthesized image-mask pairs from noisy synthetic data pool, improving the quality of synthetic data. For the first time, our SODGAN tackles SOD with synthetic data directly generated from the generative model, which opens up a new research paradigm for SOD. Extensive experimental results show that the saliency model trained on synthetic data can achieve $98.4\%$ F-measure of the saliency model trained on the DUTS-TR. Moreover, our approach achieves a new SOTA performance in semi/weakly-supervised methods, and even outperforms several fully-supervised SOTA methods. Code is available at https://github.com/wuzhenyubuaa/SODGAN
