21 citations · 85 across the 12 of their papers we have counts for
12 papers
An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training
Zitian Chen, Mingyu Ding, Yikang Shen +4
We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently. Despite considerable progress in multi-task learning, most effor…
Doubly Robust Self-Training
Banghua Zhu, Mingyu Ding, Philip Jacobson +4
Self-training is an important technique for solving semi-supervised learning problems. It leverages unlabeled data by generating pseudo-labels and combining them with a limited lab…
VDT: General-purpose Video Diffusion Transformers via Mask Modeling
Haoyu Lu, Guoxing Yang, Nanyi Fei +4
This work introduces Video Diffusion Transformer (VDT), which pioneers the use of transformers in diffusion-based video generation. It features transformer blocks with modularized…
Planning with Large Language Models for Code Generation
Shun Zhang, Zhenfang Chen, Yikang Shen +3
Existing large language model-based code generation pipelines typically use beam search or sampling algorithms during the decoding process. Although the programs they generate achi…
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
Haoyu Lu, Yuqi Huo, Guoxing Yang +4
Large-scale vision-language pre-trained models have shown promising transferability to various downstream tasks. As the size of these foundation models and the number of downstream…
Understanding Self-Supervised Pretraining with Part-Aware Representation Learning
Jie Zhu, Jiyang Qi, Mingyu Ding +6
In this paper, we are interested in understanding self-supervised pretraining through studying the capability that self-supervised representation pretraining methods learn part-awa…
LGDN: Language-Guided Denoising Network for Video-Language Modeling
Haoyu Lu, Mingyu Ding, Nanyi Fei +2
Video-language modeling has attracted much attention with the rapid growth of web videos. Most existing methods assume that the video frames and text description are semantically c…
Multimodal foundation models are better simulators of the human brain
Haoyu Lu, Qiongyi Zhou, Nanyi Fei +8
Multimodal learning, especially large-scale multimodal pre-training, has developed rapidly over the past few years and led to the greatest advances in artificial intelligence (AI).…
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer
Yao Mu, Shoufa Chen, Mingyu Ding +3
Transformer has achieved great successes in learning vision and language representation, which is general across various downstream tasks. In visual control, learning transferable…
Context Autoencoder for Self-Supervised Representation Learning
Xiaokang Chen, Mingyu Ding, Xiaodi Wang +7
We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. We pretrain an encoder by making predictions in…
HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers
Mingyu Ding, Xiaochen Lian, Linjie Yang +4
High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored…
Learning Versatile Neural Architectures by Propagating Network Codes
Mingyu Ding, Yuqi Huo, Haoyu Lu +5
This work explores how to design a single neural network capable of adapting to multiple heterogeneous vision tasks, such as image segmentation, 3D detection, and video recognition…