5 citations · 5 across the 4 of their papers we have counts for
5 papers · 1 filter
SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs
Zi-Hao Bo, Yaqian Li, Anzhou Hou +6
Mixture-of-Experts (MoE) has become a prevalent backbone for large vision-language models (VLMs), yet how modality-specific signals should guide expert routing remains under-explor…
ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection
Haowei Zhu, Tianxiang Pan, Rui Qin +2
The scale and quality of datasets are crucial for training robust perception models. However, obtaining large-scale annotated data is both costly and time-consuming. Generative mod…
Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning
Haowei Zhu, Fangyuan Zhang, Rui Qin +3
As the scale of vision models continues to grow, Visual Prompt Tuning (VPT) has emerged as a parameter-efficient transfer learning technique, noted for its superior performance com…
Low-Confidence Samples Mining for Semi-supervised Object Detection
Guandu Liu, Fangyuan Zhang, Tianxiang Pan +1
Reliable pseudo-labels from unlabeled data play a key role in semi-supervised object detection (SSOD). However, the state-of-the-art SSOD methods all rely on pseudo-labels with hig…
Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Training
Fangyuan Zhang, Tianxiang Pan, Bin Wang
This study delves into semi-supervised object detection (SSOD) to improve detector performance with additional unlabeled data. State-of-the-art SSOD performance has been achieved r…