1 code implementation • 25 Mar 2024 • Ye Li, Lingdong Kong, Hanjiang Hu, Xiaohao Xu, Xiaonan Huang
The robustness of driving perception systems under unprecedented conditions is crucial for safety-critical usages.
1 code implementation • 17 Mar 2024 • Xiaohao Xu, Yunkang Cao, Yongqi Chen, Weiming Shen, Xiaonan Huang
In addition, we unify the input representation of multi-modality into a 2D image format, enabling multi-modal anomaly detection and reasoning.
1 code implementation • 10 Mar 2024 • Huaxin Zhang, Xiang Wang, Xiaohao Xu, Xiaonan Huang, Chuchu Han, Yuehuan Wang, Changxin Gao, Shanjun Zhang, Nong Sang
In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling.
2 code implementations • 7 Mar 2024 • Xiang Li, Kai Qiu, Jinglu Wang, Xiaohao Xu, Rita Singh, Kashu Yamazak, Hao Chen, Xiaonan Huang, Bhiksha Raj
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the environment where intelligent systems perceive.
1 code implementation • 12 Feb 2024 • Xiaohao Xu, Tianyi Zhang, Sibo Wang, Xiang Li, Yongqi Chen, Ye Li, Bhiksha Raj, Matthew Johnson-Roberson, Xiaonan Huang
To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations.
no code implementations • 29 Jan 2024 • Yunkang Cao, Xiaohao Xu, Jiangning Zhang, Yuqi Cheng, Xiaonan Huang, Guansong Pang, Weiming Shen
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e. g., industrial defect inspection, and medical lesion detection.
1 code implementation • 5 Nov 2023 • Yunkang Cao, Xiaohao Xu, Chen Sun, Xiaonan Huang, Weiming Shen
This study explores the use of GPT-4V(ision), a powerful visual-linguistic model, to address anomaly detection tasks in a generic manner.
no code implementations • 13 Sep 2022 • Kun Wang, William R. Johnson III, Shiyang Lu, Xiaonan Huang, Joran Booth, Rebecca Kramer-Bottiglio, Mridul Aanjaneya, Kostas Bekris
This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot.
no code implementations • 29 May 2022 • Shiyang Lu, William R. Johnson III, Kun Wang, Xiaonan Huang, Joran Booth, Rebecca Kramer-Bottiglio, Kostas Bekris
To ensure that the pose estimates of rigid elements are physically feasible, i. e., they are not resulting in collisions between rods or with the environment, physical constraints are introduced during the optimization.
no code implementations • 19 Nov 2020 • Zhixiong Yue, Baijiong Lin, Xiaonan Huang, Yu Zhang
Although NAS methods can find network architectures with the state-of-the-art performance, the adversarial robustness and resource constraint are often ignored in NAS.
1 code implementation • 12 Feb 2020 • Pengxin Guo, Chang Deng, Linjie Xu, Xiaonan Huang, Yu Zhang
The proposed feature augmentation strategy can be used in many deep multi-task learning models.