Over the past few years, a significant progress has been made in deep
convolutional neural networks (CNNs)-based image recognition. This is mainly
due to the strong ability of such networks in mining discriminative object pose
and parts information from texture and shape. This is often inappropriate for
fine-grained visual classification (FGVC) since it exhibits high intra-class
and low inter-class variances due to occlusions, deformation, illuminations,
etc. Thus, an expressive feature representation describing global structural
information is a key to characterize an object/ scene. To this end, we propose
a method that effectively captures subtle changes by aggregating context-aware
features from most relevant image-regions and their importance in
discriminating fine-grained categories avoiding the bounding-box and/or
distinguishable part annotations. Our approach is inspired by the recent
advancement in self-attention and graph neural networks (GNNs) approaches to
include a simple yet effective relation-aware feature transformation and its
refinement using a context-aware attention mechanism to boost the
discriminability of the transformed feature in an end-to-end learning process.
Our model is evaluated on eight benchmark datasets consisting of fine-grained
objects and human-object interactions. It outperforms the state-of-the-art
approaches by a significant margin in recognition accuracy.

本文介绍一种有效的方法，通过聚合来自最相关的图像区域的上下文感知特征及其在区分细粒度类别中的重要性来捕获细微变化，避免边框和 / 或可见部分注释，并通过自我关注和图形神经网络的最新进展来包括一种简单而有效的关系感知特征变换及其在端到端学习过程中的性能评估。评估了我们的模型，发现其在八个细粒度对象和人类对象交互数据集上的识别准确性显著优于现有技术。

SR-GNN: 面向细粒度图像分类的空间关系感知图神经网络

SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization

Few-shot object detection (FSOD) aims to detect never-seen objects using few
examples. This field sees recent improvement owing to the meta-learning
techniques by learning how to match between the query image and few-shot class
examples, such that the learned model can generalize to few-shot novel classes.
However, currently, most of the meta-learning-based methods perform pairwise
matching between query image regions (usually proposals) and novel classes
separately, therefore failing to take into account multiple relationships among
them. In this paper, we propose a novel FSOD model using heterogeneous graph
convolutional networks. Through efficient message passing among all the
proposal and class nodes with three different types of edges, we could obtain
context-aware proposal features and query-adaptive, multiclass-enhanced
prototype representations for each class, which could help promote the pairwise
matching and improve final FSOD accuracy. Extensive experimental results show
that our proposed model, denoted as QA-FewDet, outperforms the current
state-of-the-art approaches on the PASCAL VOC and MSCOCO FSOD benchmarks under
different shots and evaluation metrics.

本文提出了一种使用异构图卷积网络的新颖 FSOD 模型，通过三种不同类型的边在所有提案和类节点之间进行有效的消息传递，从而获得上下文感知提案特征和查询自适应的多类增强原型表示，这有助于促进成对匹配并提高 FSOD 准确性，在 PASCAL VOC 和 MSCOCO FSOD 基准测试中，我们的模型 QA-FewDet 在不同的拍摄数和评估指标下优于现有的最先进方法。