5 papers
Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models
Dong Chen, Fangyun Wei, Ziyu Wan +18
We introduce Lens, a 3.8B-parameter T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with more than 6B parameters acro…
VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer
Yanning Hou, Peiyuan Li, Zirui Liu +4
Zero-shot anomaly detection (ZSAD) requires detecting and localizing anomalies without access to target-class anomaly samples. Mainstream methods rely on vision-language models (VL…
Real, Fake, or Manipulated? Detecting Machine-Influenced Text
Yitong Wang, Zhongping Zhang, Margherita Piana +3
Large Language Model (LLMs) can be used to write or modify documents, presenting a challenge for understanding the intent behind their use. For example, benign uses may involve usi…
OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning
Yuan Gong, Xionghui Wang, Jie Wu +3
In this paper, we introduce OneReward, a unified reinforcement learning framework that enhances the model's generative capabilities across multiple tasks under different evaluation…
DreamFuse: Adaptive Image Fusion with Diffusion Transformer
Junjia Huang, Pengxiang Yan, Jiyang Liu +5
Image fusion seeks to seamlessly integrate foreground objects with background scenes, producing realistic and harmonious fused images. Unlike existing methods that directly insert…