58 citations · 107 across the 22 of their papers we have counts for
22 papers
When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators
Jintao Rong, Xin Xie, Xinyi Yu +4
Training-free motion customization imposes motion patterns from reference videos onto video generators through test-time computation. Most existing methods target full diffusion mo…
UP-SLAM: Adaptively Structured Gaussian SLAM with Uncertainty Prediction in Dynamic Environments
Wancai Zheng, Linlin Ou, Jiajie He +3
Recent 3D Gaussian Splatting (3DGS) techniques for Visual Simultaneous Localization and Mapping (SLAM) have significantly progressed in tracking and high-fidelity mapping. However,…
Channel Merging: Preserving Specialization for Merged Experts
Mingyang Zhang, Jing Liu, Ganggui Ding +3
Lately, the practice of utilizing task-specific fine-tuning has been implemented to improve the performance of large language models (LLM) in subsequent tasks. Through the integrat…
GSORB-SLAM: Gaussian Splatting SLAM benefits from ORB features and Transmittance information
Wancai Zheng, Xinyi Yu, Jintao Rong +3
The emergence of 3D Gaussian Splatting (3DGS) has recently ignited a renewed wave of research in dense visual SLAM. However, existing approaches encounter challenges, including sen…
Boosting Box-supervised Instance Segmentation with Pseudo Depth
Xinyi Yu, Ling Yan, Pengtao Jiang +4
The realm of Weakly Supervised Instance Segmentation (WSIS) under box supervision has garnered substantial attention, showcasing remarkable advancements in recent years. However, t…
Improving Neural Indoor Surface Reconstruction with Mask-Guided Adaptive Consistency Constraints
Xinyi Yu, Liqin Lu, Jintao Rong +2
3D scene reconstruction from 2D images has been a long-standing task. Instead of estimating per-frame depth maps and fusing them in 3D, recent research leverages the neural implici…
ShiftNAS: Improving One-shot NAS via Probability Shift
Mingyang Zhang, Xinyi Yu, Haodong Zhao +1
One-shot Neural architecture search (One-shot NAS) has been proposed as a time-efficient approach to obtain optimal subnet architectures and weights under different complexity case…
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
Jintao Rong, Hao Chen, Linlin Ou +3
The Contrastive Language-Image Pretraining (CLIP) model has been widely used in various downstream vision tasks. The few-shot learning paradigm has been widely adopted to augment i…
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
Mingyang Zhang, Hao Chen, Chunhua Shen +4
Large Language Models (LLMs), such as LLaMA and T5, have shown exceptional performance across various tasks through fine-tuning. Although low-rank adaption (LoRA) has emerged to ch…
CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection
Yang Yang, Weijie Ma, Hao Chen +2
The combination of LiDAR and camera modalities is proven to be necessary and typical for 3D object detection according to recent studies. Existing fusion strategies tend to overly…
MKIoU Loss: Towards Accurate Oriented Object Detection in Aerial Images
Xinyi Yu, Jiangping Lu, Mi Lin +1
Oriented bounding box regression is crucial for oriented object detection. However, regression-based methods often suffer from boundary problems and the inconsistency between loss…
Multi-subgoal Robot Navigation in Crowds with History Information and Interactions
Xinyi Yu, Jianan Hu, Yuehai Fan +2
Robot navigation in dynamic environments shared with humans is an important but challenging task, which suffers from performance deterioration as the crowd grows. In this paper, mu…
Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy
Xinyi Yu, Xiaowei Wang, Jintao Rong +2
Structural re-parameterization (Rep) methods has achieved significant performance improvement on traditional convolutional network. Most current Rep methods rely on prior knowledge…
Real-time Rail Recognition Based on 3D Point Clouds
Xinyi Yu, Weiqi He, Xuecheng Qian +2
Accurate rail location is a crucial part in the railway support driving system for safety monitoring. LiDAR can obtain point clouds that carry 3D information for the railway enviro…
Conditional Generative Data-free Knowledge Distillation
Xinyi Yu, Ling Yan, Yang Yang +2
Knowledge distillation has made remarkable achievements in model compression. However, most existing methods require the original training data, which is usually unavailable due to…
A Self-adaptive LSAC-PID Approach based on Lyapunov Reward Shaping for Mobile Robots
Xinyi Yu, Siyu Xu, Yuehai Fan +1
To solve the coupling problem of control loops and the adaptive parameter tuning problem in the multi-input multi-output (MIMO) PID control system, a self-adaptive LSAC-PID algorit…
Across-Task Neural Architecture Search via Meta Learning
Jingtao Rong, Xinyi Yu, Mingyang Zhang +1
Adequate labeled data and expensive compute resources are the prerequisites for the success of neural architecture search(NAS). It is challenging to apply NAS in meta-learning scen…
Oriented Object Detection in Aerial Images Based on Area Ratio of Parallelogram
Xinyi Yu, Mi Lin, Jiangping Lu +1
Oriented object detection is a challenging task in aerial images since the objects in aerial images are displayed in arbitrary directions and are frequently densely packed. The mai…
RepNAS: Searching for Efficient Re-parameterizing Blocks
Mingyang Zhang, Xinyi Yu, Jingtao Rong +1
In the past years, significant improvements in the field of neural architecture search(NAS) have been made. However, it is still challenging to search for efficient networks due to…
A Self-adaptive SAC-PID Control Approach based on Reinforcement Learning for Mobile Robots
Xinyi Yu, Yuehai Fan, Siyu Xu +1
Proportional-integral-derivative (PID) control is the most widely used in industrial control, robot control and other fields. However, traditional PID control is not competent when…
Effective Model Compression via Stage-wise Pruning
Mingyang Zhang, Xinyi Yu, Jingtao Rong +1
Automated Machine Learning(Auto-ML) pruning methods aim at searching a pruning strategy automatically to reduce the computational complexity of deep Convolutional Neural Networks(d…
Graph Pruning for Model Compression
Mingyang Zhang, Xinyi Yu, Jingtao Rong +1
Previous AutoML pruning works utilized individual layer features to automatically prune filters. We analyze the correlation for two layers from the different blocks which have a sh…