10 citations · 21 across the 13 of their papers we have counts for
13 papers
ViPO: Visual Preference Optimization at Scale
Ming Li, Jie Wu, Justin Cui +3
While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference d…
Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization
Xinxin Liu, Ming Li, Zonglin Lyu +2
Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets provide only single, holisti…
Frame Interpolation with Consecutive Brownian Bridge Diffusion
Zonglin Lyu, Ming Li, Jianbo Jiao +1
Recent work in Video Frame Interpolation (VFI) tries to formulate VFI as a diffusion-based conditional image generation problem, synthesizing the intermediate frame given a random…
ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback
Ming Li, Taojiannan Yang, Huafeng Kuang +4
To enhance the controllability of text-to-image diffusion models, existing efforts like ControlNet incorporated image-based conditional controls. In this paper, we reveal that exis…
IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose Alignment
Letian Zhang, Ming Li, Chen Chen +1
Neural radiance fields (NeRF) is a promising approach for generating photorealistic images and representing complex scenes. However, when processing data sequentially, it can suffe…
LucidDreaming: Controllable Object-Centric 3D Generation
Zhaoning Wang, Ming Li, Chen Chen
With the recent development of generative models, Text-to-3D generations have also seen significant growth, opening a door for creating video-game 3D assets from a more general pub…
DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection
Manlin Zhang, Jie Wu, Yuxi Ren +7
Data is the cornerstone of deep learning. This paper reveals that the recently developed Diffusion Model is a scalable data engine for object detection. Existing methods for scalin…
DLIP: Distilling Language-Image Pre-training
Huafeng Kuang, Jie Wu, Xiawu Zheng +5
Vision-Language Pre-training (VLP) shows remarkable progress with the assistance of extremely heavy parameters, which challenges deployment in real applications. Knowledge distilla…
AlignDet: Aligning Pre-training and Fine-tuning in Object Detection
Ming Li, Jie Wu, Xionghui Wang +6
The paradigm of large-scale pre-training followed by downstream fine-tuning has been widely employed in various object detection algorithms. In this paper, we reveal discrepancies…
First Place Solution to the CVPR'2023 AQTC Challenge: A Function-Interaction Centric Approach with Spatiotemporal Visual-Language Alignment
Tom Tongjia Chen, Hongshan Yu, Zhengeng Yang +6
Affordance-Centric Question-driven Task Completion (AQTC) has been proposed to acquire knowledge from videos to furnish users with comprehensive and systematic instructions. Howeve…
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation
Jie Qin, Jie Wu, Pengxiang Yan +8
Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary categories of text-based descriptions, which popularizes the segmentation system to more gen…
Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic Segmentation
Jie Qin, Jie Wu, Ming Li +3
Albeit with varying degrees of progress in the field of Semi-Supervised Semantic Segmentation, most of its recent successes are involved in unwieldy models and the lightweight solu…
Parallel Pre-trained Transformers (PPT) for Synthetic Data-based Instance Segmentation
Ming Li, Jie Wu, Jinhang Cai +6
Recently, Synthetic data-based Instance Segmentation has become an exceedingly favorable optimization paradigm since it leverages simulation rendering and physics to generate high-…