In recent years, the rapid progress of deep learning has driven notable advancements in satellite video tracking, a critical task for applications such as environmental monitoring, disaster management, and defense. Despite these strides, existing approaches remain constrained by their inability to handle dynamic challenges, such as target appearance variations, complex motion patterns, and occlusions. Traditional methods often suffer from static template matching or overly complex update mechanisms, compromising their robustness and practicality in real-world scenarios. To address these limitations, we propose a paradigm shift in satellite video tracking by integrating historical trajectory knowledge with visual features. This fusion enhances the tracker's perceptual understanding of targets over time, enabling more adaptive and resilient tracking. By aligning spatial, temporal, and cross-modal information, our approach effectively bridges the gap between fragmented observations and coherent tracking performance, even under challenging conditions like small target detection and cluttered backgrounds. Extensive experiments conducted on multiple satellite video tracking benchmarks demonstrate the superiority of our method, with HTTrack achieving success rates of 51.5% on SV248S, 52.9% on SatSOT, and 32.6% on VISO, significantly outperforming state-of-the-art trackers and marking a step forward in achieving robust, accurate, and scalable satellite video tracking.

RGB-T tracking is increasingly deployed in safety-critical applications such as autonomous driving, surveillance, and rescue robotics, where tracking reliability is essential under adverse conditions. Although the fusion of RGB and thermal infrared (TIR) modalities offers improved robustness in low-light and occluded scenes, recent findings show that RGB-T trackers remain highly susceptible to subtle input perturbations, human-imperceptible modifications that exploit cross-modal inconsistencies to mislead tracking outputs. In real-world scenarios, such perturbations can arise from sensor spoofing, infrared camouflage, or physical-world attacks, posing serious risks to operational safety. To address this, we propose SFPT, a Semantic Feature Purification framework that enhances RGB-T tracking at the representation level. Rather than filtering corrupted inputs at the pixel level, SFPT introduces task-specific semantic anchors into the feature space to reinforce perturbation-invariant cues. These anchors are derived from descriptive language, interact with visual features to purify representations. To further suppress modality-specific interference, we design an Adaptive Perturbation-Guided Cross-Modal Fusion (APG-CMF) module, which leverages language and visual signals to estimate reliability and dynamically reweight cross-modal features, ensuring robust fusion under perturbation conditions. Extensive experiments under diverse perturbation conditions validate the effectiveness of our approach. Notably, SFPT maintains performance comparable to clean settings even when subjected to perturbations of strength 1/255 and 4/255, demonstrating strong resilience to real-world interference.

Foundational vision-language models (VLMs) like CLIP are redefining the vision domain with their exceptional generalization capabilities. Prompt-based learning methods adapt pre-trained VLMs to video action recognition tasks using task-specific learnable text tokens. However, these tokens often struggle to generalize to unseen categories, as they tend to forget general textual knowledge. To address this, we construct knowledge prompts composed of handcrafted and descriptive prompts and introduce a novel knowledge-guided context mapping to enhance the generalization of learnable prompts to unseen categories. This approach mitigates the forgetting of fundamental knowledge by reducing the discrepancy between learnable prompts and knowledge prompts while simultaneously allowing the prompts to extract rich contextual knowledge from LLM data. Then, incorporating the knowledge-guided context mapping into the contrastive loss enables zero-shot transfer of prompts to new categories and data, providing discriminative prompts for both seen and unseen tasks. In addition, we propose an advanced temporal aggregation method that refines uniform mean pooling by incorporating frame-level textual relevance scoring. Extensive evaluations on multiple benchmarks demonstrate that learning to prompt with refining text knowledge is an effective quick-tuning method, achieving superior sample generalization performance without increasing training parameters.

RGB-based object tracking is a fundamental task in computer vision, aiming to identify, locate, and continuously track objects of interest across sequential video frames. Despite the significant advancements in the performance of traditional RGB trackers, they still face challenges in maintaining accuracy and robustness in the presence of complex backgrounds, occlusions, and rapid movements. To tackle these challenges, combining visual auxiliary modalities has gained significant attention. Beyond this, integrating natural language information offers additional advantages by providing high-level semantic context, enhancing robustness, and clarifying target priorities, further elevating tracker performance. This work proposes theAdaptiveMulti-modalVisual Tracking with Dynamic Semantic Prompts (AMVTrack) tracker, which efficiently incorporates image descriptions and avoids text dependency during tracking to improve flexibility and adaptability. AMVTrack significantly reduces computational resource consumption by freezing the parameters of the image encoder, text encoder, and Box Head and only optimizing a few learnable prompt parameters. Additionally, we introduce the Adaptive Dynamic Semantic Prompt Generator (ADSPG), which dynamically generates semantic prompts based on visual features, and theVisual-LanguageFusionAdaptation (V-L FA) method, which integrates multi-modal features to ensure consistency and complementarity of information. Additionally, we partition the Image Encoder to conduct an in-depth investigation into the relationship between the importance of features across different depth and width regions. Experimental results demonstrate that AMVTrack achieves significant performance improvements on multiple benchmark datasets, proving its effectiveness and robustness in complex scenarios.

Satellite video tracking presents significant challenges due to unpredictable target variations, environmental disturbances, and occlusions. Existing approaches either rely on auxiliary modalities or require full fine-tuning of foundation models, resulting in excessive parameter sensitivity and poor generalization. Meanwhile, conventional prompt-based tuning only updates parameters at a single location, limiting its ability to adapt to complex appearance changes. To address these limitations, we propose Adaptive Visual Prompting for Effective Satellite Video Tracking (AVPTrack). Unlike conventional prompts, introduced Super Prompts dynamically refine the original template at multiple distinct positions. This multi-location adaptation allows for fine-grained representation learning, enabling the tracker to better capture target variations and resist environmental disturbances. Additionally, Dynamic Templates are introduced to mitigate tracking failures in highly challenging scenarios, such as occlusions and background clutter, ensuring robust target localization. Furthermore, the Template Selection Adapter (TSA) selects the most relevant templates in real-time, enhancing tracking efficiency. These components are optimized during training while keeping other parameters frozen, ensuring parameter efficiency. We also investigate the relationship between fine-tuning proportions and learning rates to optimize model performance. Extensive evaluations on the SV248S, SatSOT, and VISO datasets demonstrate the superior adaptability and robustness of AVPTrack compared to existing methods.

Multi-modal visual tracking leverages complementary sensor information to enhance robustness under challenging conditions. However, the security of multi-modal tracking systems remains largely unexplored. Existing attacks primarily target single-modal trackers or independently disrupt each modality, failing to exploit the inherent feature interactions and fusion mechanisms that define multi-modal tracking. As a result, these methods exhibit limited attack effectiveness and fail to assess multi-modal tracking systems' vulnerabilities accurately. Understanding these security risks is crucial, as adversarial threats could lead to severe failures in safety-critical applications. To address these challenges, a feature-aware adversarial attack, termed FA3T is proposed. It is designed to explicitly disrupt feature extraction and cross-modal alignment, thereby weakening the fusion process that multi-modal trackers rely on. To achieve this, a Frequency-Spatial Feature Separation (FSFS) module is constructed to perturb feature representations at multiple levels, weakening the modality-complementary advantages of multi-modal tracking. Furthermore, a Target Confusion Attack (TCA) module is devised to manipulate the target-background-template relationships, making it increasingly difficult for the tracker to distinguish the true target, significantly impairing tracking performance. Extensive experiments on five benchmark datasets (i.e., LasHeR, RGBT234, DepthTrack, VOT-RGBD2022, VisEvent) across three different modalities (RGB-T, RGB-D, and RGB-E) demonstrate that our attack substantially degrades state-of-the-art multi-modal trackers, exposing their susceptibility to adversarial threats.

Remote sensing image change detection plays a critical role in applications like video surveillance and geographic information systems. However, existing binary and semantic change detection methods often rely solely on visual information, neglecting language information, which limits interpretability and the ability to provide specific change details. This work proposes the Change Knowledge-Guided Vision-Language Remote Sensing Change Detection (CKCD) method to address these limitations. By introducing change knowledge as language information, CKCD enhances semantic understanding and change detail representation. A Cross-Modal Affinity (CMA) module is designed to effectively fuse visual and textual features, improving information complementarity and fusion coherence. CKCD further enhances data utilization efficiency by merging change area detection and change category information into a single output through endto- end learning. This design reduces redundant data representations and simplifies the detection process, leading to a more compact and efficient use of the input data without requiring additional branches or multiple output heads. Experimental results demonstrate consistent performance improvements over traditional methods across multiple change detection datasets.

Pre-trained vision-language(V-L) models such as CLIP have demonstrated impressive Zero-Shot performance in many downstream tasks. Since adopting contrastive video-text pairs methods like CLIP to video tasks is limited by its high cost and scale, recent approaches focus on efficiently transferring the image-based CLIP to the video domain. A major finding is that fine-tuning the pre-trained model to achieve strong fully supervised performance leads to low zero shot, few shot, and base to novel generalization. Instead, freezing the backbone network to maintain generalization ability weakens fully supervised performance. Otherwise, no single prompt tuning branch consistently performs optimally. In this work, we proposed a multimodal prompt learning scheme that balances supervised and generalized performance. Our prompting approach contains three sections: 1) Independent prompt on both the vision and text branches to learn the language and visual contexts. 2) Inter-modal prompt mapping to ensure mutual synergy. 3) Reducing the discrepancy between the hand-crafted prompt (a video of a person doing [CLS]) and the learnable prompt, to alleviate the forgetting about essential video scenarios. Extensive validation of fully supervised, zero-shot, few-shot, base-to-novel generalization settings for video recognition indicates that the proposed approach achieves competitive performance with less commute cost.

Object Tracking in satellite videos is a challenging task due to the small target size, low spatial resolution, limited appearance and texture information, and the potential for background confusion. While current state-of-the-art tracking methods perform well on natural images, they often produce unsatisfactory results when applied to satellite videos. In this paper, we address these challenges by leveraging location prompts and refining the feature extractor and bounding box refinement module. Furthermore, we integrate motion features to effectively handle illumination variations that frequently arise in satellite videos, thereby enhancing the overall robustness of the tracker. Our proposed approach, abbreviated as SVLPNet, has been thoroughly evaluated through extensive experiments conducted on two authentic satellite video datasets. The obtained results unequivocally showcase the promising potential of SVLPNet in facilitating object tracking on satellite videos. The source code and raw results will be released at https://github.com/Wprofessor/SVLPNet.

Despite the extensive research on RGBT object tracking, there are still several challenges and issues in practical applications, such as modality differences, lighting variations and disappearance of the target, and changes in viewpoint. Existing methods mostly address these issues by fusing image features, while neglecting a significant amount of target label information. To address these challenges, this paper introduces text to drive the alignment of visible and infrared image features, transforming features from different modalities into the same feature space and fully using complementary features between different modalities. Furthermore, inspired by the success of prompt learning in various tasks, we utilize prior boxes and language as prompts to further guide the model in tracking the target. Extensive experiments demonstrate that the proposed VLCTrack tracker has excellent potential in RGBT object tracking. Compared to previous methods developed for this purpose, our approach achieves state-of-the-art performance on three benchmark datasets.

Most current change detection methods require a large amount of labeled data to train huge parameters. To break this limitation, this paper proposes a novel semi-supervised learning framework for remote sensing change detection, named a semi-dual change detection network (SDCDNet). The SDCDNet consists of a dual shared network and dual branching networks. The dual shared network is designed to exploit the full potential of the data, and the dual branching network is proposed to differentiate the kinds of annotated data and eliminate the disturbance between different types of data. In addition, the adaptive weighting module (AWM) enhances the features of weak branching, and the mask constraint module (MCM) is proposed to increase the ability of the network to extract foreground features. To solve the complex problem of data labeling, a patch-based weak label construction method is proposed to build super-weak labels. Experiments show that the proposed SDCDNet achieves excellent results on two remote sensing image change detection datasets.