ADGNet introduces an Asymmetric Dual-text Guided Network for infrared small target detection, addressing challenges of pixel-level methods and multimodal approaches that lack regional guidance. It employs an Asymmetric Dual-text Prompt (ADP) with an abstract target prompt and a detailed background prompt, and an Asymmetric Dual-Branch Interaction (ADBI) module to guide visual features separately, followed by an Adaptive Feature Aggregation (AFA) module for dynamic fusion. The authors also create an Asymmetric Image-Text Infrared (AITIR) dataset with asymmetric text annotations for three public datasets, and show that ADGNet outperforms 21 state‑of‑the‑art methods.
By Tongtong Wang, Mingzhu Xu, Chenglong Yu, Jing Wang, Xiaohui Lin, Weili Guan
The paper introduces DOD-SA, a framework for infrared-visible object detection that uses only single-modality annotations. It employs a Collaborative Teacher-Student Network with a single-modality branch and a dual-modality decoupled branch to transfer knowledge across modalities, and a Progressive and Self‑Tuning Training Strategy to refine pseudo‑labels. A Pseudo Label Assigner is also designed to align labels between modalities during training.
By Hang Jin, Chenqiang Gao, Junjie Guo, Fangcen Liu, Qinyao Chang, Kanghui Tian, Deyu Meng
arXiv:2607. 04603v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) aims to identify long distance small targets from complex infrared backgrounds, and is a fundamental task in remote sensing.
By Tianfang Zhang, Fengyi Wu, Lei Li, Chang Liu, Zhenming Peng, Huaping Zhang, Xiangyang Ji
Controllable infrared-visible image fusion aims to integrate complementary thermal and structural information with flexible region-aware modulation, producing fused images that adapt to diverse user requirements and downstream tasks. However, existing methods typically rely on predefined discrete control conditions, leading to a sparse space that fails to support fine-grained modulation demands.
arXiv:2608.20870v1 Announce Type: new
Abstract: Infrared small target detection is still challenging in remote sensing imagery, because the targets are extremely small, exhibit weak local contrast, a...
By Rui Liu, Jing Nie, Ying Fu
Infrared small target detection (IRSTD) remains challenging due to tiny target size, low signal-to-noise ratio, severe foreground-background imbalance, and blurred boundaries in complex scenes. Existing methods usually rely on post-activation probability-domain supervision for discrimination, where weak targets and strong clutter may produce saturated and close probabilities, limiting weak-target discrimination.
arXiv:2608. 05771v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) has achieved substantial progress under domain-consistent evaluation, yet detector performance often degrades markedly when generalizing to unseen infrared domains.
By Aohua Li, Jin Kuang, Yubing Lu, Pingping Liu
Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.
arXiv:2603.02767v4 Announce Type: replace-cross
Abstract: Image--text contrastive pretraining has become a dominant paradigm for visual representation learning, yet existing methods often yield repre...
By Hanpeng Liu, Zidan Wang, Shuoxi Zhang, Zonglin Zhao, Zihao Bo, Rinyoichi Takezoe, Kaiwen Long, Yaqian Li, Kun He
arXiv:2607. 03143v1 Announce Type: cross Abstract: Vision-language alignment powers open-vocabulary recognition, retrieval, and LVLM grounding, yet natural captions are often underspecified, making similarity brittle and overly confident under paraphrase and omitted details.
By Chengzhen Yu, Canran Xiao, Siyuan Ma, Yang Liu
C2FXNet is a unified object detection framework designed for adverse weather conditions. It uses a dual-level guidance mechanism: a Multi-step Reasoning Router (MRR) for coarse scene reasoning and a Fine Scene Refinement (FSR) module for fine-grained semantic adjustment. A Scene-aware Mixture-of-Experts (SMoE) dynamically combines scene-specific experts, enabling robust detection across foggy, dark, and clear scenes without scene-specific training.
By Tianle Fang, Zhenbing Liu, Chong Yin, Bolun Li, Haoxiang Lu
arXiv:2608.28517v1 Announce Type: new
Abstract: Cross-modal image translation in remote sensing must preserve source-observed content while matching the target-domain distribution. Existing methods j...
By Keyan Hu, Mingtao Wang, Ziyu Zhou, Tiandong Shi, Haifeng Li, Ji Qi, Chao Tao