173 citations · 374 across the 20 of their papers we have counts for
23 papers
Beyond VLM-Based Rewards: Diffusion-Native Latent Reward Modeling
Gongye Liu, Bo Yang, Yida Zhi +8
Preference optimization for diffusion and flow-matching models relies on reward functions that are both discriminatively robust and computationally efficient. Vision-Language Model…
BiVM: Accurate Binarized Neural Network for Efficient Video Matting
Haotong Qin, Xianglong Liu, Xudong Ma +4
Deep neural networks for real-time video matting suffer significant computational limitations on edge devices, hindering their adoption in widespread applications such as online co…
Matching Anything by Segmenting Anything
Siyuan Li, Lei Ke, Martin Danelljan +4
The robust association of the same objects across video frames in complex scenes is crucial for many applications, especially Multiple Object Tracking (MOT). Current methods predom…
DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos
Wen-Hsuan Chu, Lei Ke, Katerina Fragkiadaki
View-predictive generative models provide strong priors for lifting object-centric images and videos into 3D and 4D through rendering and score distillation objectives. A question…
LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning
Junchi Wang, Lei Ke
Understanding human instructions to identify the target objects is vital for perception systems. In recent years, the advancements of Large Language Models (LLMs) have introduced n…
Stable Segment Anything Model
Qi Fan, Xin Tao, Lei Ke +6
The Segment Anything Model (SAM) achieves remarkable promptable segmentation given high-quality prompts which, however, often require good skills to specify. To make SAM robust to…