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20242026
most citedERNIE 5.0 Technical Report

2 citations · 2 across the 13 of their papers we have counts for

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cs.CV2026

Test-Time Curriculum for Open-Set AIGC Detection

Yiqian Zhang, Zheyuan Gu, Xiangzhao Hao +8

AI-generated image detectors deployed in open-world environments inevitably face distribution shifts as new and stronger generative models continue to emerge. Although existing met…

cs.CV2026

Memento: Reconstruct to Remember for Consistent Long Video Generation

Xuan Wei, Longbin Ji, Guan Wang +5

Long-form video generation requires recurring subjects to remain consistent across various shots, viewpoints, motions, and scene transitions. Existing temporal decomposition method…

cs.CV2026

Native Audio-Visual Alignment for Generation

Longbin Ji, Guan Wang, Xuan Wei +6

Joint audio-video generation aims to synthesize temporally synchronized and semantically coherent visual-acoustic content. However, existing open-source methods mainly rely on eith…

cs.CV2026

Blink: Dynamic Visual Token Resolution for Enhanced Multimodal Understanding

Yuchen Feng, Zhenyu Zhang, Naibin Gu +8

Multimodal large language models (MLLMs) have achieved remarkable progress on various vision-language tasks, yet their visual perception remains limited. Humans, in comparison, per…

cs.CV2026

CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models

Xiangzhao Hao, Zefeng Zhang, Zhenyu Zhang +6

Image degradation from blur, noise, compression, and poor illumination severely undermines multimodal understanding in real-world settings. Unified multimodal models that combine u…

cs.CV2026

Learning to Generate via Understanding: Understanding-Driven Intrinsic Rewarding for Unified Multimodal Models

Jiadong Pan, Liang Li, Yuxin Peng +6

Recently, unified multimodal models (UMMs) have made remarkable progress in integrating visual understanding and generation, demonstrating strong potential for complex text-to-imag…