Datao Tang — accepted AI-conference papers

2026

ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Senisng

Zhenghui Zhao, Chen Wu, Xiangyong Cao, Di Wang, Hongruixuan Chen, Datao Tang +2

CVPR 2026

Spatiotemporal image generation is a highly meaningful task, which can generate future scenes conditioned on given observations. However, existing change generation methods can only handle event-driven changes (e.g., new buildings) and fail to model cross-temporal variations (e.g., seasonal shifts).

Cited by 0 Source ↗code ↗Scholar ↗

2026

From Scale to Speed: Adaptive Test-Time Scaling for Image Editing

Xiangyan Qu, Zhenlong Yuan, Jing Tang, Rui Chen, Datao Tang, Meng Yu +6

CVPR 2026

Image Chain-of-Thought (Image-CoT) is a test-time scaling paradigm that improves image generation by extending inference time. Most Image-CoT methods focus on text-to-image (T2I) generation. Unlike T2I generation, image editing is goal-directed: the solution space is constrained by the source image

Cited by 0 Source ↗Scholar ↗