no code implementations • 2 Mar 2024 • Xinyi Yu, Ling Yan, PengTao Jiang, Hao Chen, Bo Li, Lin Yuanbo Wu, Linlin Ou
This innovative approach empowers the network to simultaneously predict masks and depth, enhancing its ability to capture nuanced depth-related information during the instance segmentation process.
no code implementations • 12 Feb 2024 • Yiqi Zhao, Xinyi Yu, Jyotirmoy V. Deshmukh, Lars Lindemann
Motivated by the advances in conformal prediction (CP), we propose conformal predictive programming (CPP), an approach to solve chance constrained optimization (CCO) problems, i. e., optimization problems with nonlinear constraint functions affected by arbitrary random parameters.
no code implementations • 7 Dec 2023 • Xinyi Yu, Yiqi Zhao, Xiang Yin, Lars Lindemann
We propose a predictive control synthesis framework that guarantees, with high probability, the satisfaction of signal temporal logic (STL) tasks that are defined over the system and uncontrollable stochastic agents.
1 code implementation • 27 Nov 2023 • Chuwei Wang, Xinyi Yu, Jianing Zhao, Lars Lindemann, Xiang Yin
Existing works on online monitoring usually assume that the monitor can acquire system information periodically at each time instant.
1 code implementation • 22 Oct 2023 • Xinyi Yu, Guanbin Li, Wei Lou, SiQi Liu, Xiang Wan, Yan Chen, Haofeng Li
Therefore, augmenting a dataset with only a few labeled images to improve the segmentation performance is of significant research and application value.
1 code implementation • 22 Oct 2023 • Wei Lou, Xinyi Yu, Chenyu Liu, Xiang Wan, Guanbin Li, SiQi Liu, Haofeng Li
Afterward, we train a separate segmentation model for each category using the images in the corresponding category.
no code implementations • 10 Oct 2023 • Dingran Yuan, Xinyi Yu, ShaoYuan Li, Xiang Yin
In order to tackle the overtaking task in such challenging scenarios, we introduce a novel integrated framework tailored for vehicle overtaking maneuvers.
no code implementations • 18 Sep 2023 • Xinyi Yu, Liqin Lu, Jintao Rong, Guangkai Xu, Linlin Ou
3D scene reconstruction from 2D images has been a long-standing task.
no code implementations • 24 Jul 2023 • Xinyi Yu, Xiang Yin, Lars Lindemann
Given an ATR bound, we compute a sequence of control inputs so that the specification is satisfied by the system as long as each sub-trajectory is shifted not more than the ATR bound.
1 code implementation • ICCV 2023 • Mingyang Zhang, Xinyi Yu, Haodong Zhao, Linlin Ou
To address the problem of uniform sampling, we propose ShiftNAS, a method that can adjust the sampling probability based on the complexity of subnets.
no code implementations • 4 Jun 2023 • Jintao Rong, Hao Chen, Tianxiao Chen, Linlin Ou, Xinyi Yu, Yifan Liu
Prompt learning has become a popular approach for adapting large vision-language models, such as CLIP, to downstream tasks.
no code implementations • 28 May 2023 • Mingyang Zhang, Hao Chen, Chunhua Shen, Zhen Yang, Linlin Ou, Xinyi Yu, Bohan Zhuang
This is due to their utilization of unstructured pruning on LPMs, impeding the merging of LoRA weights, or their dependence on the gradients of pre-trained weights to guide pruning, which can impose significant memory overhead.
no code implementations • 19 Apr 2023 • Yang Yang, Weijie Ma, Hao Chen, Linlin Ou, Xinyi Yu
The combination of LiDAR and camera modalities is proven to be necessary and typical for 3D object detection according to recent studies.
1 code implementation • 15 Nov 2022 • Xinyi Yu, Chuwei Wang, Dingran Yuan, ShaoYuan Li, Xiang Yin
However, instead of applying MPC directly for the entire task horizon, we decompose the STL formula into several sub-formulae with disjoint time horizons, and shrinking horizon MPC is applied for each short-horizon sub-formula iteratively.
1 code implementation • 26 Sep 2022 • Xinyi Yu, Weijie Dong, Xiang Yin, ShaoYuan Li
To this end, effective approaches for the computation of feasible sets of STL formulae are provided.
no code implementations • 30 Jun 2022 • Jiangping Lu, Xinyi Yu, Mi Lin, Linlin Ou
Thus, the Gaussian Angle Loss (GA Loss) is presented to solve this problem by adding a corrected loss for square targets.
no code implementations • 4 May 2022 • Xinyi Yu, Jianan Hu, Yuehai Fan, Wancai Zheng, Linlin Ou
Firstly, based on subgraph network, the history information of all agents is aggregated before encoding interactions through a graph neural network, so as to improve the ability of the robot to anticipate the future scenarios implicitly.
no code implementations • 18 Apr 2022 • Michael Gleicher, Xinyi Yu, YuHeng Chen
Machine learning practitioners must perform model selection, calibration, discretization, performance assessment, tuning, and fairness assessment.
no code implementations • 13 Apr 2022 • Xinyi Yu, Xiaowei Wang, Jintao Rong, Mingyang Zhang, Linlin Ou
However, the performance of architecture is limited by the type of operations and prior knowledge.
no code implementations • 30 Mar 2022 • Xinyi Yu, Weijie Dong, Xiang Yin, ShaoYuan Li
We show that, by explicitly utilizing the model information of the dynamic system, the proposed online monitoring algorithm can falsify or certify of the specification in advance compared with existing algorithms, where no model information is used.
no code implementations • 8 Jan 2022 • Xinyi Yu, Weiqi He, Xuecheng Qian, Yang Yang, Linlin Ou
Accurate rail location is a crucial part in the railway support driving system for safety monitoring.
no code implementations • 31 Dec 2021 • Xinyi Yu, Ling Yan, Yang Yang, Libo Zhou, Linlin Ou
In this paper, we propose a conditional generative data-free knowledge distillation (CGDD) framework for training lightweight networks without any training data.
Conditional Image Generation Data-free Knowledge Distillation +1
no code implementations • 12 Oct 2021 • Jingtao Rong, Xinyi Yu, Mingyang Zhang, Linlin Ou
In this paper, an across-task neural architecture search (AT-NAS) is proposed to address the problem through combining gradient-based meta-learning with EA-based NAS to learn over the distribution of tasks.
no code implementations • 21 Sep 2021 • Xinyi Yu, Mi Lin, Jiangping Lu, Linlin Ou
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.
1 code implementation • 8 Sep 2021 • Mingyang Zhang, Xinyi Yu, Jingtao Rong, Linlin Ou
However, it is still challenging to search for efficient networks due to the gap between the searched constraint and real inference time exists.
no code implementations • 11 Jun 2021 • Weichen Chen, Xinyi Yu, Linlin Ou
A specific view-attribute is composed by the extracted attribute feature and four view scores which are predicted by view predictor as the confidences for attribute from different views.
no code implementations • 10 Nov 2020 • Mingyang Zhang, Xinyi Yu, Jingtao Rong, Linlin Ou
To overcome the unfull training, a stage-wise pruning(SWP) method is proposed, which splits a deep supernet into several stage-wise supernets to reduce the candidate number and utilize inplace distillation to supervise the stage training.
no code implementations • 16 Apr 2020 • Michael Gleicher, Aditya Barve, Xinyi Yu, Florian Heimerl
The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets.
no code implementations • 22 Nov 2019 • Mingyang Zhang, Xinyi Yu, Jingtao Rong, Linlin Ou
Different from previous work, we take the node features from a well-trained graph aggregator instead of the hand-craft features, as the states in reinforcement learning.