Recent studies often exploit Graph Convolutional Network (GCN) to model label
dependencies to improve recognition accuracy for multi-label image recognition.
However, constructing a graph by counting the label co-occurrence possibilities
of the training data may degrade model generalizability, especially when there
exist occasional co-occurrence objects in test images. Our goal is to eliminate
such bias and enhance the robustness of the learnt features. To this end, we
propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to
dynamically generate a specific graph for each image. ADD-GCN adopts a Dynamic
Graph Convolutional Network (D-GCN) to model the relation of content-aware
category representations that are generated by a Semantic Attention Module
(SAM). Extensive experiments on public multi-label benchmarks demonstrate the
effectiveness of our method, which achieves mAPs of 85.2%, 96.0%, and 95.5% on
MS-COCO, VOC2007, and VOC2012, respectively, and outperforms current
state-of-the-art methods with a clear margin. All codes can be found at
this https URL

本文提出了一种基于动态图卷积神经网络 (D-GCN) 和语义注意力模块 (SAM) 的注意力驱动动态图卷积网络 (ADD-GCN) 来消除训练数据中标签共现对模型泛化性能的影响，实现图像特征的提取和标签识别任务，并且在公共多标签基准测试中的结果表明该方法的有效性。

基于注意力机制的动态图卷积神经网络用于多标签图像识别

Attention-Driven Dynamic Graph Convolutional Network for Multi-Label  Image Recognition

We propose a weakly-supervised approach for conditional image generation of
complex scenes where a user has fine control over objects appearing in the
scene. We exploit sparse semantic maps to control object shapes and classes, as
well as textual descriptions or attributes to control both local and global
style. In order to condition our model on textual descriptions, we introduce a
semantic attention module whose computational cost is independent of the image
resolution. To further augment the controllability of the scene, we propose a
two-step generation scheme that decomposes background and foreground. The label
maps used to train our model are produced by a large-vocabulary object
detector, which enables access to unlabeled data and provides structured
instance information. In such a setting, we report better FID scores compared
to fully-supervised settings where the model is trained on ground-truth
semantic maps. We also showcase the ability of our model to manipulate a scene
on complex datasets such as COCO and Visual Genome.

我们提出了一种弱监督方法，实现复杂场景下的有条件图像生成，在该方法中，用户可以对场景中出现的对象进行精细控制。我们利用稀疏语义地图来控制对象的形状和类别，以及文本描述或属性来控制局部和全局风格，同时引入语义注意力模块来支持文本描述的条件，该模块的计算成本与图像分辨率无关。为了进一步增强场景的可控性，我们提出了一个两步生成方案来分解背景和前景。我们使用大词汇目标检测器产生的标签地图来训练模型，这使我们能够访问未注释的数据，并提供结构化实例信息。在这样的设置下，我们报告了比全监督设置更好的 FID 得分。我们还展示了我们的模型在 COCO 和 Visual Genome 等复杂数据集上操作场景的能力。

弱监督图像生成中的风格和语义控制

Controlling Style and Semantics in Weakly-Supervised Image Generation

We propose a compact framework with guided attention for multi-label
classification in the fashion domain. Our visual semantic attention model
(VSAM) is supervised by automatic pose extraction creating a discriminative
feature space. VSAM outperforms the state of the art for an in-house dataset
and performs on par with previous works on the DeepFashion dataset, even
without using any landmark annotations. Additionally, we show that our semantic
attention module brings robustness to large quantities of wrong annotations and
provides more interpretable results.

本篇文章基于自动姿态提取，提出了在时尚领域中用于多标签分类的带引导注意力的紧凑框架，通过视觉语义注意力模型（VSAM）进行监督，在不使用地标注注释的情况下在 DeepFashion 数据集上取得了与之前工作相当的性能，同时还证明了我们的语义注意力模块对更多错误注释的容错性和提供更可解释结果的价值。