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cs.CV2026

Denoising-Enhanced Coarse-to-Fine Infrared Small Target Detection with Attention Prior-Guided Knowledge Distillation

Houzhang Fang, Ruixuan Huang, Qiuhuan Chen +3

Infrared small target detection (IRSTD) in high-resolution images is crucial for many practical applications, such as surveillance of unmanned aerial vehicles (UAVs) and UAV-based…

cs.CV2026

ST-Gen4D: Embedding 4D Spatiotemporal Cognition into World Model for 4D Generation

Haonan Wang, Hanyu Zhou, Tao Gu +1

Generative models have achieved success in producing apparently coherent 2D videos, but remain challenging in the physical world due to lack of 4D spatiotemporal scale. Typically,…

cs.CV2026

High-Quality and Efficient Turbulence Mitigation with Events

Xiaoran Zhang, Jian Ding, Yuxing Duan +4

Turbulence mitigation (TM) is highly ill-posed due to the stochastic nature of atmospheric turbulence. Most methods rely on multiple frames recorded by conventional cameras to capt…

cs.CV2026

NEC-Diff: Noise-Robust Event-RAW Complementary Diffusion for Seeing Motion in Extreme Darkness

Haoyue Liu, Jinghan Xu, Luxin Feng +4

High-quality imaging of dynamic scenes in extremely low-light conditions is highly challenging. Photon scarcity induces severe noise and texture loss, causing significant image deg…

cs.CV2026

Cog2Gen3D: Sculpturing 3D Semantic-Geometric Cognition for 3D Generation

Haonan Wang, Hanyu Zhou, Haoyue Liu +2

Generative models have achieved success in producing semantically plausible 2D images, but it remains challenging in 3D generation due to the absence of spatial geometry constraint…

cs.CV2026

Adapting Depth Anything to Adverse Imaging Conditions with Events

Shihan Peng, Yuyang Xiong, Hanyu Zhou +5

Robust depth estimation under dynamic and adverse lighting conditions is essential for robotic systems. Currently, depth foundation models, such as Depth Anything, achieve great su…