most citedSIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection

2 citations · 4 across the 12 of their papers we have counts for

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cs.CV2025

AngularFuse: A Closer Look at Angle-based Perception for Spatial-Sensitive Multi-Modality Image Fusion

Xiaopeng Liu, Yupei Lin, Sen Zhang +3

Visible-infrared image fusion is crucial in key applications such as autonomous driving and nighttime surveillance. Its main goal is to integrate multimodal information to produce…

cs.CV2025

Ivan-ISTD: Rethinking Cross-domain Heteroscedastic Noise Perturbations in Infrared Small Target Detection

Yuehui Li, Yahao Lu, Haoyuan Wu +3

In the multimedia domain, Infrared Small Target Detection (ISTD) plays a important role in drone-based multi-modality sensing. To address the dual challenges of cross-domain shift…

cs.CV2025

DroneSR: Rethinking Few-shot Thermal Image Super-Resolution from Drone-based Perspective

Zhipeng Weng, Xiaopeng Liu, Ce Liu +3

Although large scale models achieve significant improvements in performance, the overfitting challenge still frequently undermines their generalization ability. In super resolution…

cs.CV2025

DFVO: Learning Darkness-free Visible and Infrared Image Disentanglement and Fusion All at Once

Qi Zhou, Yukai Shi, Xiaojun Yang +4

Visible and infrared image fusion is one of the most crucial tasks in the field of image fusion, aiming to generate fused images with clear structural information and high-quality…

cs.CV2025

Rethinking Generalizable Infrared Small Target Detection: A Real-scene Benchmark and Cross-view Representation Learning

Yahao Lu, Yuehui Li, Xingyuan Guo +3

Infrared small target detection (ISTD) is highly sensitive to sensor type, observation conditions, and the intrinsic properties of the target. These factors can introduce substanti…

cs.CV2025

Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation

Tianshui Chen, Jianman Lin, Zhijing Yang +3

Speech-preserving facial expression manipulation (SPFEM) aims to modify a talking head to display a specific reference emotion while preserving the mouth animation of source spoken…