Acquiring sufficient ground-truth supervision to train deep visual models has
been a bottleneck over the years due to the data-hungry nature of deep
learning. This is exacerbated in some structured prediction tasks, such as
semantic segmentation, which requires pixel-level annotations. This work
addresses weakly supervised semantic segmentation (WSSS), with the goal of
bridging the gap between image-level annotations and pixel-level segmentation.
We formulate WSSS as a novel group-wise learning task that explicitly models
semantic dependencies in a group of images to estimate more reliable pseudo
ground-truths, which can be used for training more accurate segmentation
models. In particular, we devise a graph neural network (GNN) for group-wise
semantic mining, wherein input images are represented as graph nodes, and the
underlying relations between a pair of images are characterized by an efficient
co-attention mechanism. Moreover, in order to prevent the model from paying
excessive attention to common semantics only, we further propose a graph
dropout layer, encouraging the model to learn more accurate and complete object
responses. The whole network is end-to-end trainable by iterative message
passing, which propagates interaction cues over the images to progressively
improve the performance. We conduct experiments on the popular PASCAL VOC 2012
and COCO benchmarks, and our model yields state-of-the-art performance. Our
code is available at: this https URL

本研究提出一种基于图神经网络的弱监督语义分割方法，将图像表示为图的节点，使用组内注意力机制建立图中的关系，通过迭代信息传递的方式学习更准确、更完整的物体响应，并在多个基准数据集上取得了最先进的性能。

面向弱监督语义分割的分组语义挖掘

Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation

Solving grounded language tasks often requires reasoning about relationships
between objects in the context of a given task. For example, to answer the
question "What color is the mug on the plate?" we must check the color of the
specific mug that satisfies the "on" relationship with respect to the plate.
Recent work has proposed various methods capable of complex relational
reasoning. However, most of their power is in the inference structure, while
the scene is represented with simple local appearance features. In this paper,
we take an alternate approach and build contextualized representations for
objects in a visual scene to support relational reasoning. We propose a general
framework of Language-Conditioned Graph Networks (LCGN), where each node
represents an object, and is described by a context-aware representation from
related objects through iterative message passing conditioned on the textual
input. E.g., conditioning on the "on" relationship to the plate, the object
"mug" gathers messages from the object "plate" to update its representation to
"mug on the plate", which can be easily consumed by a simple classifier for
answer prediction. We experimentally show that our LCGN approach effectively
supports relational reasoning and improves performance across several tasks and
datasets. Our code is available at this http URL

本文提出了一种基于场景上下文化表征的图网络模型，通过文本输入的迭代信息传递来构建物体的语境化表达，以支持关系推理，实验结果表明该方法有效地提高了多项任务和数据集的表现。