55 citations · 121 across the 20 of their papers we have counts for
6 papers · 1 filter
CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation
Yiming Cui, Cheng Han, Dongfang Liu
The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims to track and seg…
ClusterFormer: Clustering As A Universal Visual Learner
James C. Liang, Yiming Cui, Qifan Wang +3
This paper presents CLUSTERFORMER, a universal vision model that is based on the CLUSTERing paradigm with TransFORMER. It comprises two novel designs: 1. recurrent cross-attention…
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
Yiming Cui, Linjie Yang, Haichao Yu
Transformer-based detection and segmentation methods use a list of learned detection queries to retrieve information from the transformer network and learn to predict the location…
E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning
Cheng Han, Qifan Wang, Yiming Cui +4
As the size of transformer-based models continues to grow, fine-tuning these large-scale pretrained vision models for new tasks has become increasingly parameter-intensive. Paramet…
Cloud-RAIN: Point Cloud Analysis with Reflectional Invariance
Yiming Cui, Lecheng Ruan, Hang-Cheng Dong +4
The networks for point cloud tasks are expected to be invariant when the point clouds are affinely transformed such as rotation and reflection. So far, relative to the rotational i…
FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors
Yiming Cui, Linjie Yang
Video object detection needs to solve feature degradation situations that rarely happen in the image domain. One solution is to use the temporal information and fuse the features f…