Remote Sensing Image-Text Retrieval (RSITR) is pivotal for knowledge services
and data mining in the remote sensing (RS) domain. Considering the multi-scale
representations in image content and text vocabulary can enable the models to
learn richer representations and enhance retrieval. Current multi-scale RSITR
approaches typically align multi-scale fused image features with text features,
but overlook aligning image-text pairs at distinct scales separately. This
oversight restricts their ability to learn joint representations suitable for
effective retrieval. We introduce a novel Multi-Scale Alignment (MSA) method to
overcome this limitation. Our method comprises three key innovations: (1)
Multi-scale Cross-Modal Alignment Transformer (MSCMAT), which computes
cross-attention between single-scale image features and localized text
features, integrating global textual context to derive a matching score matrix
within a mini-batch, (2) a multi-scale cross-modal semantic alignment loss that
enforces semantic alignment across scales, and (3) a cross-scale multi-modal
semantic consistency loss that uses the matching matrix from the largest scale
to guide alignment at smaller scales. We evaluated our method across multiple
datasets, demonstrating its efficacy with various visual backbones and
establishing its superiority over existing state-of-the-art methods. The GitHub
URL for our project is: this https URL

遥感图像文本检索领域的研究论文，介绍了一种新的多尺度对齐方法，使用多尺度图像特征和本地化文本特征进行交叉对齐，并引入语义对齐损失与跨尺度多模态语义一致性损失，通过在多个数据集上的评估证明了其优越性。

超越融合：一种用于遥感图像 - 文本检索的多尺度对齐方法

Transcending Fusion: A Multi-Scale Alignment Method for Remote Sensing  Image-Text Retrieval

Audio-visual segmentation is a challenging task that aims to predict
pixel-level masks for sound sources in a video. Previous work applied a
comprehensive manually designed architecture with countless pixel-wise accurate
masks as supervision. However, these pixel-level masks are expensive and not
available in all cases. In this work, we aim to simplify the supervision as the
instance-level annotation, i.e., weakly-supervised audio-visual segmentation.
We present a novel Weakly-Supervised Audio-Visual Segmentation framework,
namely WS-AVS, that can learn multi-scale audio-visual alignment with
multi-scale multiple-instance contrastive learning for audio-visual
segmentation. Extensive experiments on AVSBench demonstrate the effectiveness
of our WS-AVS in the weakly-supervised audio-visual segmentation of
single-source and multi-source scenarios.

本文介绍了一种名为 WS-AVS 的弱监督音视频分割框架，通过多尺度多实例对比学习实现了多尺度音视频对齐和音视频分割，在单一源和多源情景下有效地进行了弱监督音视频分割。