1 citations · 2 across the 7 of their papers we have counts for
7 papers
Tutor-Student Reinforcement Learning: A Dynamic Curriculum for Robust Deepfake Detection
Zhanhe Lei, Zhongyuan Wang, Jikang Cheng +5
Standard supervised training for deepfake detection treats all samples with uniform importance, which can be suboptimal for learning robust and generalizable features. In this work…
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…
RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories
Huiyang Shao, Xin Xia, Yuhong Yang +3
Diffusion models have achieved remarkable success across various domains. However, their slow generation speed remains a critical challenge. Existing acceleration methods, while ai…
Quantized Prompt for Efficient Generalization of Vision-Language Models
Tianxiang Hao, Xiaohan Ding, Juexiao Feng +3
In the past few years, large-scale pre-trained vision-language models like CLIP have achieved tremendous success in various fields. Naturally, how to transfer the rich knowledge in…
Debiased Novel Category Discovering and Localization
Juexiao Feng, Yuhong Yang, Yanchun Xie +6
In recent years, object detection in deep learning has experienced rapid development. However, most existing object detection models perform well only on closed-set datasets, ignor…
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…
When Face Recognition Meets Occlusion: A New Benchmark
Baojin Huang, Zhongyuan Wang, Guangcheng Wang +5
The existing face recognition datasets usually lack occlusion samples, which hinders the development of face recognition. Especially during the COVID-19 coronavirus epidemic, weari…