activity
20182022
most citedClass-wise Dynamic Graph Convolution for Semantic Segmentation

16 citations · 50 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV20223 cited

OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models

Jinze Bai, Rui Men, Hao Yang +15

Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alterna…

cs.CV202211 cited

Pretrained Diffusion Models for Unified Human Motion Synthesis

Jianxin Ma, Shuai Bai, Chang Zhou

Generative modeling of human motion has broad applications in computer animation, virtual reality, and robotics. Conventional approaches develop separate models for different motio…

cs.CV20223 cited

M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing

Zhikang Li, Huiling Zhou, Shuai Bai +3

The fashion industry has diverse applications in multi-modal image generation and editing. It aims to create a desired high-fidelity image with the multi-modal conditional signal a…

cs.CV20213 cited

Connecting Language and Vision for Natural Language-Based Vehicle Retrieval

Shuai Bai, Zhedong Zheng, Xiaohan Wang +5

Vehicle search is one basic task for the efficient traffic management in terms of the AI City. Most existing practices focus on the image-based vehicle matching, including vehicle…

cs.CV202114 cited

Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection

Hanzhe Hu, Shuai Bai, Aoxue Li +2

Conventional deep learning based methods for object detection require a large amount of bounding box annotations for training, which is expensive to obtain such high quality annota…

cs.CV202016 cited

Class-wise Dynamic Graph Convolution for Semantic Segmentation

Hanzhe Hu, Deyi Ji, Weihao Gan +3

Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-a…