3 citations · 3 across the 4 of their papers we have counts for
9 papers
ChatterBox: Multi-round Multimodal Referring and Grounding
Yunjie Tian, Tianren Ma, Lingxi Xie +6
In this study, we establish a baseline for a new task named multimodal multi-round referring and grounding (MRG), opening up a promising direction for instance-level multimodal dia…
Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models
Xin He, Longhui Wei, Lingxi Xie +1
Multimodal Large Language Models (MLLMs) are experiencing rapid growth, yielding a plethora of noteworthy contributions in recent months. The prevailing trend involves adopting dat…
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Yuhui Xu, Lingxi Xie, Xiaotao Gu +6
Recently years have witnessed a rapid development of large language models (LLMs). Despite the strong ability in many language-understanding tasks, the heavy computational burden l…
Pipeline MoE: A Flexible MoE Implementation with Pipeline Parallelism
Xin Chen, Hengheng Zhang, Xiaotao Gu +3
The Mixture of Experts (MoE) model becomes an important choice of large language models nowadays because of its scalability with sublinear computational complexity for training and…
Focus on Your Target: A Dual Teacher-Student Framework for Domain-adaptive Semantic Segmentation
Xinyue Huo, Lingxi Xie, Wengang Zhou +2
We study unsupervised domain adaptation (UDA) for semantic segmentation. Currently, a popular UDA framework lies in self-training which endows the model with two-fold abilities: (i…
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
Maosen Li, Siheng Chen, Zijing Zhang +3
Graph convolutional network based methods that model the body-joints' relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these meth…