55 citations · 121 across the 20 of their papers we have counts for
21 papers
A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning
Changyu Liu, James Chenhao Liang, Wenhao Yang +6
Diffusion models have significantly reshaped the field of generative artificial intelligence and are now increasingly explored for their capacity in discriminative representation l…
All You Need is One: Capsule Prompt Tuning with a Single Vector
Yiyang Liu, James C. Liang, Heng Fan +7
Prompt-based learning has emerged as a parameter-efficient finetuning (PEFT) approach to facilitate Large Language Model (LLM) adaptation to downstream tasks by conditioning genera…
MPT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
Taowen Wang, Yiyang Liu, James Chenhao Liang +11
Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains, with increasing emphasis on enhancing their zero-shot generalization cap…
ProMotion: Prototypes As Motion Learners
Yawen Lu, Dongfang Liu, Qifan Wang +6
In this work, we introduce ProMotion, a unified prototypical framework engineered to model fundamental motion tasks. ProMotion offers a range of compelling attributes that set it a…
SSGA-Net: Stepwise Spatial Global-local Aggregation Networks for for Autonomous Driving
Yiming Cui, Cheng Han, Dongfang Liu
Visual-based perception is the key module for autonomous driving. Among those visual perception tasks, video object detection is a primary yet challenging one because of feature de…
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
Cheng Han, Qifan Wang, Yiming Cui +4
As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained attention due to its su…
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…
DFA: Dynamic Feature Aggregation for Efficient Video Object Detection
Yiming Cui
Video object detection is a fundamental yet challenging task in computer vision. One practical solution is to take advantage of temporal information from the video and apply featur…
Dynamic Proposals for Efficient Object Detection
Yiming Cui, Linjie Yang, Ding Liu
Object detection is a basic computer vision task to loccalize and categorize objects in a given image. Most state-of-the-art detection methods utilize a fixed number of proposals a…
GL-RG: Global-Local Representation Granularity for Video Captioning
Liqi Yan, Qifan Wang, Yiming Cui +4
Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description. To date, state-of-the-art methods inadequately mo…
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception
Yiming Cui, Zhiwen Cao, Yixin Xie +5
Multi-object tracking and segmentation (MOTS) is a critical task for autonomous driving applications. The existing MOTS studies face two critical challenges: 1) the published datas…
TF-Blender: Temporal Feature Blender for Video Object Detection
Yiming Cui, Liqi Yan, Zhiwen Cao +1
Video objection detection is a challenging task because isolated video frames may encounter appearance deterioration, which introduces great confusion for detection. One of the pop…
SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation
Dongfang Liu, Yiming Cui, Wenbo Tan +1
Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, le…
Hierarchical Attention Fusion for Geo-Localization
Liqi Yan, Yiming Cui, Yingjie Chen +1
Geo-localization is a critical task in computer vision. In this work, we cast the geo-localization as a 2D image retrieval task. Current state-of-the-art methods for 2D geo-localiz…
DenserNet: Weakly Supervised Visual Localization Using Multi-scale Feature Aggregation
Dongfang Liu, Yiming Cui, Liqi Yan +3
In this work, we introduce a Denser Feature Network (DenserNet) for visual localization. Our work provides three principal contributions. First, we develop a convolutional neural n…
Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze
Dongfang Liu, Yiming Cui, Xiaolei Guo +3
Accurate localization is a foundational capacity, required for autonomous vehicles to accomplish other tasks such as navigation or path planning. It is a common practice for vehicl…