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20242026
most citedMagnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function

1 citations · 1 across the 6 of their papers we have counts for

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cs.CV2025

AlignCAT: Visual-Linguistic Alignment of Category and Attribute for Weakly Supervised Visual Grounding

Yidan Wang, Chenyi Zhuang, Wutao Liu +2

Weakly supervised visual grounding (VG) aims to locate objects in images based on text descriptions. Despite significant progress, existing methods lack strong cross-modal reasonin…

cs.CV2025

Uni4D: A Unified Self-Supervised Learning Framework for Point Cloud Videos

Zhi Zuo, Chenyi Zhuang, Pan Gao +3

Self-supervised representation learning for point cloud videos remains a challenging problem with two key limitations: (1) existing methods rely on explicit knowledge to learn moti…

cs.CV2025

PiCo: Enhancing Text-Image Alignment with Improved Noise Selection and Precise Mask Control in Diffusion Models

Chang Xie, Chenyi Zhuang, Pan Gao

Advanced diffusion models have made notable progress in text-to-image compositional generation. However, it is still a challenge for existing models to achieve text-image alignment…