6 papers
Why Reinforcement Fine-Tuning Enables MLLMs Preserve Prior Knowledge Better: A Data Perspective
Zhihao Zhang, Qiaole Dong, Qi Zhang +12
Post-training algorithms such as Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT) are widely used to adapt (multimodal) large language models to downstream tasks. W…
Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
Mingqi Wu, Zhihao Zhang, Qiaole Dong +11
Reasoning in large language models has long been a central research focus, and recent studies employing reinforcement learning (RL) have introduced diverse methods that yield subst…
Enhancing Video Inpainting with Aligned Frame Interval Guidance
Ming Xie, Junqiu Yu, Qiaole Dong +2
Recent image-to-video (I2V) based video inpainting methods have made significant strides by leveraging single-image priors and modeling temporal consistency across masked frames. N…
PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching
Yun Wang, Junjie Hu, Qiaole Dong +4
Temporally consistent depth estimation from stereo video is critical for real-world applications such as augmented reality, where inconsistent depth estimation disrupts the immersi…
Online Dense Point Tracking with Streaming Memory
Qiaole Dong, Yanwei Fu
Dense point tracking is a challenging task requiring the continuous tracking of every point in the initial frame throughout a substantial portion of a video, even in the presence o…
Repositioning the Subject within Image
Yikai Wang, Chenjie Cao, Ke Fan +4
Current image manipulation primarily centers on static manipulation, such as replacing specific regions within an image or altering its overall style. In this paper, we introduce a…