RGB-Thermal images leverage complementary optical and thermal modalities to identify objects. While achieving superior performance, the reliance on multimodal fusion inherently limits inference efficiency and adaptability to harsh RGB-failure environments. In this work, we propose a multimodal decomposed distillation framework to develop robust thermal-only detectors by transferring knowledge from multimodal teachers. Unlike conventional one-to-one distillation, we decouple the tasks of simultaneously mimicking RGB-T teacher representations and preserving thermal-specific student feature integrity into dual branches to avoid intrinsic semantic conflicts. Specifically, we present channel-adaptive prompt learning for cross-modal decomposition and a frequency-guided dynamic module for decomposed knowledge integration. The dual-branch architecture employs asymmetric training objectives to ensure effective cross-modal knowledge transfer while preserving the integrity of thermal information. Furthermore, to exploit finer-grained instance knowledge across both feature and prediction levels, we introduce a customized instance alignment distillation to enhance the local discriminability in feature pyramids, and propose an uncertainty-aware logit distillation to compensate for ambiguous predictions in detection heads. Experiments on three datasets validate the effectiveness of our framework in boosting thermal-based detectors. Code is released at https://github.com/lyf0801/DecomKD.

Recently, anchor-based detectors can achieve decent performance in multimodal remote sensing scenarios, whereas their anchor-free counterparts fail to reach comparable results. To remedy this problem, we first comprehensively investigate the misalignment issues in multimodal features and detection heads, and present a dual-perspective alignment learning (DPAL) framework for multimodal remote sensing object detection. Particularly, we design a cross-modal alignment module (CMAM), which utilizes the multiscale dilation strategy and differentiable alignment function with channel-wise modulation for cross-modal feature integration. Additionally, to cope with the misalignment problem in regression and classification heads, we propose a task-head alignment module (THAM). It presents a novel pseudo-anchor mechanism, introduces a semi-fixed offset generation strategy to capture task-variant sampling coordinates, and ultimately deploys an offset knowledge transfer mechanism with deformable alignment for anchor-free detection heads. Extensive experiments on four multimodal object detection datasets show impressive results of the proposed DPAL framework. The project code is released at https://github.com/lyf0801/DPAL.

Existing salient object detection methods for optical remote sensing images achieve superior results with high-resolution inputs but exhibit significant degradation in low-resolution conditions. To bridge this resolution discrepancy, we propose a structured cross-resolution knowledge distillation (SCRKD) framework designed for severely low-resolution inputs. It leverages high-resolution models as teachers to guide low-resolution students through three synergistic distillation mechanisms: 1) multiview correlation distillation (MVCD); 2) multiscale feature distillation (MSFD); and 3) decoupled saliency distillation (DSD). In addition, we present cascaded SCRKD that progressively refines structured knowledge in a multistage manner, achieving further performance boosts. Experiments on three datasets indicate that SCRKD surpasses 13 state-of-the-art methods across various cross-resolution settings. Besides, our framework based on three distinct baselines validates its model-agnostic nature. This work provides an efficient solution for low-resolution salient object detection. Code is available at:https://github.com/lyf0801/SCRKD

Recently, fake news detection (FND) is an essential task in the field of social network analysis, and multimodal detection methods that combine text and image have been significantly explored in the last five years. However, the physical features of images that can be clearly shown in the frequency level are often ignored, and thus cross-modal feature extraction and interaction still remain a great challenge when the frequency domain is introduced for multimodal FND. To address this issue, we propose a frequency-aware cross-modal interaction network (FCINet) for multimodal FND in this article. First, a triple-branch encoder with robust feature extraction capacity is proposed to explore the representation of frequency, spatial, and text domains, separately. Then, we design a parallel cross-modal interaction strategy to fully exploit the interdependencies among them to facilitate multimodal FND. Finally, a combined loss function including deep auxiliary supervision and event classification is introduced to improve the generalization ability for multitask training. Extensive experiments and visual analysis on two public real-world multimodal fake news datasets show that the presented FCINet obtains excellent performance and exceeds numerous state-of-the-art methods.

We present the RSSOD-Bench dataset for salient object detection (SOD) in optical remote sensing imagery. While SOD has achieved success in natural scene images with deep learning, research in SOD for remote sensing imagery (RSSOD) is still in its early stages. Existing RSSOD datasets have limitations in terms of scale, and scene categories, which make them misaligned with real-world applications. To address these shortcomings, we construct the RSSOD-Bench dataset, which contains images from four different cities in the USA1. The dataset provides annotations for various salient object categories, such as buildings, lakes, rivers, highways, bridges, aircraft, ships, athletic fields, and more. The salient objects in RSSOD-Bench exhibit large-scale variations, cluttered backgrounds, and different seasons. Unlike existing datasets, RSSOD-Bench offers uniform distribution across scene categories. We benchmark 23 different state-of-the-art approaches from both the computer vision and remote sensing communities. Experimental results demonstrate that more research efforts are required for the RSSOD task.

Recently, fully convolutional networks (FCNs) have contributed significantly to salient object detection in optical remote sensing images (RSIs). However, owing to the limited receptive fields of FCNs, accurate and integral detection of salient objects in RSIs with complex edges and irregular topology is still challenging. Moreover, suffering from the low contrast and complicated background of RSIs, existing models often occur ambiguous or uncertain recognition. To remedy the above problems, we propose a novel hybrid modeling approach, i.e., uncertainty-aware graph reasoning with global collaborative learning (UG2L) framework. Specifically, we propose a graph reasoning pipeline to model the intricate relations among RSI patches instead of pixels, and introduce an efficient graph reasoning block (GRB) to build graph representations. On top of it, a global context block (GCB) with a linear attention mechanism is proposed to explore the multiscale and global context collaboratively. Finally, we design a simple yet effective uncertainty-aware loss (UAL) to enhance the model’s reliability for better prediction of saliency or non-saliency. Experimental and visual results on three datasets show the superiority of the proposed UG2L. Code is available at https://github.com/lyf0801/UG2L.

Current state-of-the-art remote sensing salient object detectors always require high-resolution spatial context to ensure excellent performance, which incurs enormous computation costs and hinders real-time efficiency. In this work, we propose a universal super-resolution assisted learning (SRAL) framework to boost performance and accelerate the inference efficiency of existing approaches. To this end, we propose to reduce the spatial resolution of the input remote sensing images (RSIs), which is model-agnostic, and can be applied to existing algorithms without extra computation cost. Specifically, a transposed saliency detection decoder (TSDD) is designed to upsample interim features progressively. On top of it, an auxiliary super-resolution decoder (ASRD) is proposed to build a multitask learning (MTL) framework to investigate an efficient complementary paradigm of saliency detection and super-resolution. Furthermore, a novel task-fusion guidance module (TFGM) is proposed to effectively distill domain knowledge from the super-resolution auxiliary task to the salient object detection task in optical RSIs. The presented ASRD and TFGM can be omitted in the inference phase without any extra computational budget. Extensive experiments on three datasets show that the presented SRAL with 224×224 input is superior to more than 20 algorithms. Moreover, it can be successfully generalized to existing typical networks with significant accuracy improvements in a parameter-free manner. Codes and models are available at https://github.com/lyf0801/SRAL.

Existing remote sensing image salient object detection (RSI-SOD) methods widely perform object-level semantic understanding with pixel-level supervision, but ignore the image-level scene information. As a fundamental attribute of RSIs, the scene has a complex intrinsic correlation with salient objects, which may bring hints to improve saliency detection performance. However, existing RSI-SOD datasets lack both pixel- and image-level labels, and it is non-trivial to effectively transfer the scene domain knowledge for more accurate saliency localization. To address these challenges, we first annotate the image-level scene labels of three RSI-SOD datasets inspired by remote sensing scene classification. On top of it, we present a novel scene-guided dual-stream network (SDNet), which can perform cross-task knowledge distillation from the scene classification to facilitate accurate saliency detection. Specifically, a scene knowledge transfer module (SKTM) and a conditional dynamic guidance module (CDGM) are designed for extracting saliency key area as spatial attention from the scene subnet and guiding the saliency subnet to generate scene-enhanced saliency features, respectively. Finally, an object contour awareness module (OCAM) is introduced to enable the model to focus more on irregular spatial details of salient objects from the complicated background. Extensive experiments reveal that our SDNet outperforms over 20 state-of-the-art algorithms on three datasets. Moreover, we prove that the proposed framework is model-agnostic, and its extension to six baselines can bring significant performance benefits. Code will be available at https://github.com/lyf0801/SDNet.

Inspired by two basic mechanisms in animal visual systems, we introduce a feature transform technique that imposes invariance properties in the training of deep neural networks. The resulting algorithm requires less parameter tuning, trains well with an initial learning rate 1.0, and easily generalizes to different tasks. We enforce scale invariance with local statistics in the data to align similar samples at diverse scales. To accelerate convergence, we enforce a GL(n)-invariance property with global statistics extracted from a batch such that the gradient descent solution should remain invariant under basis change. Profiling analysis shows our proposed modifications takes 5% of the computations of the underlying convolution layer. Tested on convolutional networks and transformer networks, our proposed technique requires fewer iterations to train, surpasses all baselines by a large margin, seamlessly works on both small and large batch size training, and applies to different computer vision and language tasks.

Geospatial object detection is an essential task in remote sensing community. One-stage methods based on deep learning have faster running speed but cannot reach higher detection accuracy than two-stage methods. In this paper, to achieve excellent speed/accuracy trade-off for geospatial object detection, a single-shot balanced detector is presented. First, a balanced feature pyramid network (BFPN) is designed, which can balance semantic information and spatial information between high-level and shallow-level features adaptively. Second, we propose a task-interactive head (TIH). It can reduce the task misalignment between classification and regression. Extensive experiments show that the improved detector obtains significant detection accuracy with considerable speed on two benchmark datasets.

Benefiting from the development of convolutional neural networks (CNNs), many excellent algorithms for object detection have been presented. Remote sensing object detection (RSOD) is a challenging task mainly due to: 1) complicated background of remote sensing images (RSIs) and 2) extremely imbalanced scale and sparsity distribution of remote sensing objects. Existing methods cannot effectively solve these problems with excellent detection accuracy and rapid speed. To address these issues, we propose an adaptive balanced network (ABNet) in this article. First, we design an enhanced effective channel attention (EECA) mechanism to improve the feature representation ability of the backbone, which can alleviate the obstacles of complex background on foreground objects. Then, to combine multiscale features adaptively in different channels and spatial positions, an adaptive feature pyramid network (AFPN) is designed to capture more discriminative features. Furthermore, considering that the original FPN ignores rich deep-level features, a context enhancement module (CEM) is proposed to exploit abundant semantic information for multiscale object detection. Experimental results on three public datasets demonstrate that our approach exhibits superior performance over baseline by only introducing less than 1.5M extra parameters.

Recently, salient object detection in optical remote sensing images (RSI-SOD) has attracted great attention. Benefiting from the success of deep learning and the inspiration of natural SOD task, RSI-SOD has achieved fast progress over the past two years. However, existing methods usually suffer from the intrinsic problems of optical RSIs, 1) cluttered background; 2) scale variation of salient objects; 3) complicated edges and irregular topology. To remedy these problems, we propose a hybrid feature aligned network (HFANet) jointly modeling boundary learning to detect salient objects effectively. Specifically, we design a hybrid encoder by unifying two components to capture global context for mitigating the disturbance of complex background. Then, to detect multiscale salient objects effectively, we propose a Gated Fold-ASPP (GF-ASPP) to extract abundant context in the deep semantic features. Furthermore, an adjacent feature aligned module (AFAM) is presented for integrating adjacent features with unparameterized alignment strategy. Finally, we propose a novel interactive guidance loss (IGLoss) to combine saliency and edge detection, which can adaptively perform mutual supervision of the two sub-tasks to facilitate detection of salient objects with blurred edges and irregular topology. Adequate experimental results on three optical RSI-SOD datasets reveal that the presented approach exceeds 18 state-of-the-art ones. All codes and detection results are available athttps://github.com/lyf0801/HFANet.

This paper provides an overview of developments on termination conditions in evolutionary algorithms (EAs). It seeks to give a representative picture of the termination conditions in EAs over the past decades, segment the contributions of termination conditions into progress indicators and termination criteria. With respect to progress indicators, we consider a variety of indicators, in particular in convergence indicators and diversity indicators. With respect to termination criteria, this paper reviews recent research on threshold strategy, statistical inference, i.e., Kalman filters, as well as Fuzzy methods, and other methods. Key developments on termination conditions over decades include: (i) methods of judging the algorithm's search behavior based on statistics, and (ii) methods of detecting the termination based on different distance formulations.