187 citations · 249 across the 14 of their papers we have counts for
13 papers · 1 filter
IRSTD-Agent: Agentic Infrared Small Target Detection via Zoom-Guided Interaction Learning
Jiawen Xi, Yu Zhang, Tianyi Zhao +3
Infrared small-target detection plays an important role in maritime monitoring and aerial surveillance. Although multimodal large language models (MLLMs) offer promising capabiliti…
UniNDM: A Unified Noise-driven Detection and Mitigation Framework Against Sexual Content in Text-to-Image Generation
Yao Huang, Yitong Sun, Huanran Chen +8
Despite the impressive generative capabilities of text-to-image diffusion models, they remain vulnerable to implicit sexual prompts, where subtle cues disguised as benign terms or…
Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling
Shiji Zhao, Shukun Xiong, Maoxun Yuan +6
In complex environments, infrared object detection exhibits broad applicability and stability across diverse scenarios. However, infrared object detection is vulnerable to both com…
Breaking Self-Attention Failure: Rethinking Query Initialization for Infrared Small Target Detection
Yuteng Liu, Duanni Meng, Yimian Dai +3
Infrared small target detection (IRSTD) faces significant challenges due to low signal-to-noise ratios, extremely small target sizes, and complex cluttered backgrounds. Although DE…
RGBT-GroundBench: Visual Grounding Beyond RGB in Complex Real-World Scenarios
Tianyi Zhao, Jiawen Xi, Linhui Xiao +4
Visual grounding (VG) localizes target objects in an image from natural-language expressions. In real-world perception, RGB cues often degrade under low illumination and adverse we…
Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective
Maoxun Yuan, Duanni Meng, Ziteng Xi +4
Infrared small target detection and segmentation (IRSTDS) is a critical yet challenging task in defense and civilian applications, owing to the dim, shapeless appearance of targets…
MoAPT: Mixture of Adversarial Prompt Tuning for Vision-Language Models
Shiji Zhao, Qihui Zhu, Shukun Xiong +7
Large pre-trained Vision Language Models (VLMs) demonstrate excellent generalization capabilities but remain highly susceptible to adversarial examples, posing potential security r…
Rethinking Multi-Modal Object Detection from the Perspective of Mono-Modality Feature Learning
Tianyi Zhao, Boyang Liu, Yanglei Gao +3
Multi-Modal Object Detection (MMOD), due to its stronger adaptability to various complex environments, has been widely applied in various applications. Extensive research is dedica…
UniRGB-IR: A Unified Framework for Visible-Infrared Semantic Tasks via Adapter Tuning
Maoxun Yuan, Bo Cui, Tianyi Zhao +4
Semantic analysis on visible (RGB) and infrared (IR) images has gained significant attention due to their enhanced accuracy and robustness under challenging conditions including lo…
Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection
Tianyi Zhao, Maoxun Yuan, Feng Jiang +2
In recent years, object detection utilizing both visible (RGB) and thermal infrared (IR) imagery has garnered extensive attention and has been widely implemented across a diverse a…
Learning to Pan-sharpening with Memories of Spatial Details
Maoxun Yuan, Tianyi Zhao, Bo Li +1
Pan-sharpening, as one of the most commonly used techniques in remote sensing systems, aims to inject spatial details from panchromatic images into multispectral images (MS) to obt…
Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection
Maoxun Yuan, Xingxing Wei
Object detection on visible (RGB) and infrared (IR) images, as an emerging solution to facilitate robust detection for around-the-clock applications, has received extensive attenti…
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection
Maoxun Yuan, Yinyan Wang, Xingxing Wei
Integrating multispectral data in object detection, especially visible and infrared images, has received great attention in recent years. Since visible (RGB) and infrared (IR) imag…