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cs.CV2026

Grounding with Confidence: Controllable Generative Video Temporal Grounding

Jinhao Chen, Benlei Cui, Ruijian Jia +7

Video temporal grounding supports applications such as video search, content review, and automated editing by localizing events described in natural language. Yet existing generati…

cs.CV2026

MetaVideoAgent: Automated Video-Agent Evolution for Long-Form Video Understanding

Benlei Cui, Ruize Wang, Junjie Li +9

Long-form video understanding requires locating sparse, question-relevant evidence in long, multimodal videos. Real-world video distributions differ in modality-specific informatio…

cs.CV2026

Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning

Hongxing Li, Xiufeng Huang, Dingming Li +11

Fine-grained visual reasoning remains challenging for vision-language models, especially when small but critical visual cues are buried in high-resolution images. Existing approach…

cs.CV2026

Yuvion VL: A Multimodal Foundation Model for Adversarial Content and AI Safety

Shikai Qiu, Xiaowen Xu, Benlei Cui +55

General-purpose models often struggle to reliably identify and understand real-world multimodal risks, largely due to the inherent multimodal adversarial nature of content and AI s…

cs.CV2026

simpleposter: A simple baseline for product poster generation

Benlei Cui, Fangao Zeng, Weitao Jiang +6

Product poster generation poses distinct challenges beyond general poster design, requiring both faithful preservation of product appearance and precise control over dense, multi-l…

cs.CV2026

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts

Haolei Xu, Haiwen Hong, Hongxing Li +7

Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon termed Seeing but Not Thinking:…

cs.CV2026

TC-Padé: Trajectory-Consistent Padé Approximation for Diffusion Acceleration

Benlei Cui, Shaoxuan He, Bukun Huang +8

Despite achieving state-of-the-art generation quality, diffusion models are hindered by the substantial computational burden of their iterative sampling process. While feature cach…

cs.CV2026

Diffusion Probe: Generated Image Result Prediction Using CNN Probes

Benlei Cui, Bukun Huang, Zhizeng Ye +7

Text-to-image (T2I) diffusion models lack an efficient mechanism for early quality assessment, leading to costly trial-and-error in multi-generation scenarios such as prompt iterat…

cs.CV2023

One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications

Mengyao Lyu, Yuhong Yang, Haiwen Hong +6

The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing…

cs.CV2022

Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition

Yunqing Hu, Xuan Jin, Yin Zhang +5

Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. In this paper, we take pure Vision Transformer (ViT) as the research base…

cs.CV2021

DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework

Haiwen Hong, Xuan Jin, Yin Zhang +4

In multimodal tasks, we find that the importance of text and image modal information is different for different input cases, and for this motivation, we propose a high-performance…