arXiv subjects

Xikun Hu

Publications and source records attributed to Xikun Hu.

3 recordsLinked to original sources

arXiv2026

Adv-Camou: An Emissivity-based Physical Dynamic Adversarial Patch for Infrared Attack

With the widespread application of infrared object detection technologies in military domains, intelligent infrared adversarial camouflage capabilities have become critical. Existing physical attack methods predominantly employ static thermal suppression strategies, suffering from limited environmental adaptability, insufficient attack robustness, and inadequate visual concealment. This study proposes Adv-Camou, an emissivity-based physical dynamic adversarial patch that achieves paradigm-shifting advancements from static to dynamic attacks through algorithm-material co-innovation. First, a multi-level loss function based on feature confusion mechanisms is designed to deceive detectors by attenuating target saliency and enforcing background feature alignment. Second, adaptive physical transformations and differentiable binarization optimization are integrated to address physical robustness and realizability constraints. A 6*4 array of reversible electrochromic emissivity device is developed and tested across a range of background scenarios, enabling scene-adaptive dynamic camouflage through independent unit control. Experiments demonstrate that this method achieves 95.59% attack success rate (ASR), reducing average precision (AP) by 60.51%. Physical validations show that static adversarial patches attain 76.55% average ASR under multi-view, multi-range, and multi-spectral conditions, while dynamic patches enhance ASR up to 91.55% in complex environments (wilderness/urban) through adaptive switching. The seamless digital-to-physical transfer via tunable emissivity devices transcends the limitations of traditional static camouflage by establishing a dynamic infrared regulation capability. This advancement enhances the battlefield survivability of military assets and demonstrates transformative potential for adaptive camouflage in practical combat scenarios.

eess.IV↗

arXiv2026

UniEvo-RS: Omni-Prompt Unified Remote Sensing Segmentation with Representative Exemplar-Driven Prototype Evolution

Prompt-driven vision-language models (VLMs) hold immense promise for accelerating dense remote sensing (RS) annotation, but static models suffer from severe performance degradation when deployed on novel scenes, unseen categories, or visually confusing backgrounds. Moreover, existing unified paradigms primarily rely on intra-image specific prompts, lacking flexible task routing to adapt to multi-intent operational workflows. In practical batch mapping, annotators typically refine a small set of representative samples before processing large datasets. Motivated by this practice, we propose UniEvo-RS, an omni-prompt unified RS segmentation framework equipped with representative exemplar-driven prototype evolution. First, we construct a multi-instruction prompt dataset that unifies text-driven and visual-driven prompts within a single architecture, establishing a dynamic task-routing mechanism for highly diverse RS annotation scenarios. Second, we introduce a representative feedback-driven, training-free prototype evolution mechanism. By contrasting manual annotations with initial predictions on exemplars, UniEvo-RS distills prediction errors into positive and negative prototypes. These prototypes enhance LLM query recall and suppress spatial background noise under a fixed-budget clustering memory. Extensive experiments show that UniEvo-RS unifies diverse prompting tasks, achieving state-of-the-art performance across most settings. Crucially, with minimal interaction on a few exemplars, it enables training-free, progressive accuracy enhancement on unseen categories during batch annotation.

cs.CV↗

arXiv2026

BMCR: Adaptive Backbone Module Composition via Reinforcement Learning for Remote Sensing Object Detection

In remote sensing object detection, Convolutional Neural Networks (CNNs) excel at capturing local details while Vision Transformers (ViTs) are better at global context modeling. However, existing detectors typically rely on a single fixed backbone or a manually designed hybrid architecture, and thus fail to adaptively exploit these complementary strengths across inputs of diverse complexity. To address this limitation, we propose Backbone Module Composition via Reinforcement Learning (BMCR). BMCR dynamically assembles input-adaptive inference paths from reusable modules decomposed from off-the-shelf CNN and ViT backbones. To enable such cross-family composition, we first construct an extensible module toolbox. Specifically, we decompose representative CNN and ViT backbones into reusable functional modules and encapsulate each module with explicit structural, semantic, and computational metadata for compatibility-aware assembly. To bridge the gap between grid-based CNN features and token-based ViT representations, we design a lightweight Optimal Transport (OT) based transition interface that ensures distribution-aware alignment while respecting spatial consistency. The backbone composition process is then formulated as a sequential decision problem, in which a policy network progressively selects task-relevant modules according to intermediate multi-scale observations. To stabilize the joint optimization of reusable modules and the routing policy, we further develop an Adaptive Module Cooperative Optimization (AMCO) strategy that coordinates module updating, routing exploration, and reward assignment during training. On DOTA-v1.0, DOTA-v1.5 and DIOR-R, BMCR achieves 79.31\%, 73.41\% and 71.86\% mAP, respectively, surpassing strong static and dynamic baselines by up to 2.5 points while maintaining competitive efficiency.

cs.CV↗