1 citations · 1 across the 4 of their papers we have counts for
4 papers
Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL
Sudong Wang, Weiquan Huang, Xiaomin Yu +9
The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiab…
DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers
Li Ren, Chen Chen, Liqiang Wang +1
Visual Prompt Tuning (VPT) has become a promising solution for Parameter-Efficient Fine-Tuning (PEFT) approach for Vision Transformer (ViT) models by partially fine-tuning learnabl…
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
Li Ren, Chen Chen, Liqiang Wang +1
Deep Metric Learning (DML) has long attracted the attention of the machine learning community as a key objective. Existing solutions concentrate on fine-tuning the pre-trained mode…
Towards Improved Proxy-based Deep Metric Learning via Data-Augmented Domain Adaptation
Li Ren, Chen Chen, Liqiang Wang +1
Deep Metric Learning (DML) plays an important role in modern computer vision research, where we learn a distance metric for a set of image representations. Recent DML techniques ut…