47 citations · 79 across the 9 of their papers we have counts for
9 papers
Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities
Yiyuan Zhang, Xiaohan Ding, Kaixiong Gong +3
We propose to improve transformers of a specific modality with irrelevant data from other modalities, e.g., improve an ImageNet model with audio or point cloud datasets. We would l…
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors
Lihe Ding, Shaocong Dong, Zhanpeng Huang +5
Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-…
OneLLM: One Framework to Align All Modalities with Language
Jiaming Han, Kaixiong Gong, Yiyuan Zhang +6
Multimodal large language models (MLLMs) have gained significant attention due to their strong multimodal understanding capability. However, existing works rely heavily on modality…
Towards Unified and Effective Domain Generalization
Yiyuan Zhang, Kaixiong Gong, Xiaohan Ding +4
We propose , a novel and fied framework for omain eneralization that is capable of significantly enhancing the out-of-distribu…
Meta-Transformer: A Unified Framework for Multimodal Learning
Yiyuan Zhang, Kaixiong Gong, Kaipeng Zhang +4
Multimodal learning aims to build models that can process and relate information from multiple modalities. Despite years of development in this field, it still remains challenging…
Improving Transferability for Domain Adaptive Detection Transformers
Kaixiong Gong, Shuang Li, Shugang Li +3
DETR-style detectors stand out amongst in-domain scenarios, but their properties in domain shift settings are under-explored. This paper aims to build a simple but effective baseli…
Pareto Domain Adaptation
Fangrui Lv, Jian Liang, Kaixiong Gong +5
Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source. To achieve…
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
Shuang Li, Kaixiong Gong, Chi Harold Liu +3
Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes…
Transferable Semantic Augmentation for Domain Adaptation
Shuang Li, Mixue Xie, Kaixiong Gong +3
Domain adaptation has been widely explored by transferring the knowledge from a label-rich source domain to a related but unlabeled target domain. Most existing domain adaptation a…