Thoroughly utilizing scale-aware context features to accurately segment entire salient regions remains a significant challenge in salient object detection (SOD) for optical remote sensing images (RSIs). In this letter, we propose a novel dynamic context coordination network (DCC-Net), which is capable of highlighting complete salient regions by exploiting feature coordination across different network levels. DCC-Net is composed of three components: a feature encoder, a bilinear purification (BP) module, and a dynamic context coordination (DCC) module. First, we adopt a standard feature encoder to extract multiscale features. Second, a BP module is proposed to establish a global correlation between horizontal and vertical directions and purify saliency representations. Finally, a DCC module is designed to facilitate context coordination from low to high resolutions by conditioning the convolution kernels dynamic on adjacent context. Comparison experiments are performed on the ORSSD, EORSSD, and ORSI-4199 datasets, demonstrating the superiority of our proposed method against other state-of-the-art methods and verifying the effectiveness of the DCC.

Current state-of-the-art methods for salient object detection in optical remote sensing images (RSI-SOD) primarily relies on individual image context to detect salient objects. However, the potential of cross-image contexts remains largely unexplored in existing works, which can provide valuable auxiliary and complementary information for discriminating object representations in RSIs. In this paper, we investigate the utilization of cross-image contextual information for RSI-SOD. We propose a novel memory-based context propagation network (MCP-Net) to harness dataset-level contextual information. MCP-Net incorporates a cross-image dual memory module (CDM) to store dataset-level information and utilize it to generate contextual information for the current image. CDM effectively captures intra-scene variations by leveraging both foreground and background memory banks, resulting in improved object representations. Additionally, we enhance the representations by leveraging scale-aware context information within individual images. To preserve RSI details before memory modules, we introduce a shared attention-guided fusion module (SAF) to align the adjacent network-level features. Extensive evaluation results demonstrate the superior performance of our proposed method compared to state-of-the-art methods on three public benchmarks. These results affirm that the inclusion of cross-image contexts can significantly benefit salient object detection in remote sensing images.

Recognizing and organizing text in correct reading order plays a crucial role in historical document analysis and preservation. While existing methods have shown promising performance, they often struggle with challenges such as diverse layouts, low image quality, style variations, and distortions. This is primarily due to the lack of consideration for these issues in the current benchmarks, which hinders the development and evaluation of historical document analysis and recognition (HDAR) methods in complex real-world scenarios. To address this gap, this paper introduces a complex multi-style Chinese historical document analysis benchmark, named M5HisDoc. The M5 indicates five properties of style, ie., Multiple layouts, Multiple document types, Multiple calligraphy styles, Multiple backgrounds, and Multiple challenges. The M5HisDoc dataset consists of two subsets, M5HisDoc-R (Regular) and M5HisDoc-H (Hard). The M5HisDoc-R subset comprises 4,000 historical document images. To ensure high-quality annotations, we meticulously perform manual annotation and triple-checking. To replicate real-world conditions for historical document analysis applications, we incorporate image rotation, distortion, and resolution reduction into M5HisDoc-R subset to form a new challenging subset named M5HisDoc-H, which contains the same number of images as M5HisDoc-R. The dataset exhibits diverse styles, significant scale variations, dense texts, and an extensive character set. We conduct benchmarking experiments on five tasks: text line detection, text line recognition, character detection, character recognition, and reading order prediction. We also conduct cross-validation with other benchmarks. Experimental results demonstrate that the M5HisDoc dataset can offer new challenges and great opportunities for future research in this field, thereby providing deep insights into the solution for HDAR. The dataset is available at https://github.com/HCIILAB/M5HisDoc.