Referring expression grounding aims at locating certain objects or persons in
an image with a referring expression, where the key challenge is to comprehend
and align various types of information from visual and textual domain, such as
visual attributes, location and interactions with surrounding regions. Although
the attention mechanism has been successfully applied for cross-modal
alignments, previous attention models focus on only the most dominant features
of both modalities, and neglect the fact that there could be multiple
comprehensive textual-visual correspondences between images and referring
expressions. To tackle this issue, we design a novel cross-modal
attention-guided erasing approach, where we discard the most dominant
information from either textual or visual domains to generate difficult
training samples online, and to drive the model to discover complementary
textual-visual correspondences. Extensive experiments demonstrate the
effectiveness of our proposed method, which achieves state-of-the-art
performance on three referring expression grounding datasets.

本文提出了一种新的跨模态注意力制导抹除方法，以处理图像和指示表达之间的多种维度的视觉和文本信息，这种方法取得了三个指示表达基准数据集的最先进性能。

跨模态引导擦除的指代表达空间改进

Improving Referring Expression Grounding with Cross-modal  Attention-guided Erasing

Recognizing objects from simultaneously sensed photometric (RGB) and depth
channels is a fundamental yet practical problem in many machine vision
applications such as robot grasping and autonomous driving. In this paper, we
address this problem by developing a Cross-Modal Attentional Context (CMAC)
learning framework, which enables the full exploitation of the context
information from both RGB and depth data. Compared to existing RGB-D object
detection frameworks, our approach has several appealing properties. First, it
consists of an attention-based global context model for exploiting adaptive
contextual information and incorporating this information into a region-based
CNN (e.g., Fast RCNN) framework to achieve improved object detection
performance. Second, our CMAC framework further contains a fine-grained object
part attention module to harness multiple discriminative object parts inside
each possible object region for superior local feature representation. While
greatly improving the accuracy of RGB-D object detection, the effective
cross-modal information fusion as well as attentional context modeling in our
proposed model provide an interpretable visualization scheme. Experimental
results demonstrate that the proposed method significantly improves upon the
state of the art on all public benchmarks.

本文提出了一种基于跨模态关注上下文学习框架的方法，通过充分开发 RGB 和深度数据的上下文信息来识别物体。实验证明，该方法在所有公共基准测试中均显著提高了 RGB-D 物体检测的准确性，并提供了可解释的可视化方案。