no code implementations • 17 Apr 2024 • Qiangang Du, Jinlong Peng, Changan Wang, Xu Chen, Qingdong He, Wenbing Zhu, Mingmin Chi, Yabiao Wang, Chengjie Wang
Next, a shape-aware and a brightness-aware module are designed to improve the capacity for representation learning.
no code implementations • 17 Apr 2024 • Qiangang Du, Jinlong Peng, Xu Chen, Qingdong He, Liren He, Qiang Nie, Wenbing Zhu, Mingmin Chi, Yabiao Wang, Chengjie Wang
In this paper, we propose a multimodal contrastive learning (ChangeCLIP) based on visual-language pre-training for change detection domain generalization.
no code implementations • 19 Mar 2024 • Pengzhi Li, Qiang Nie, Ying Chen, Xi Jiang, Kai Wu, Yuhuan Lin, Yong liu, Jinlong Peng, Chengjie Wang, Feng Zheng
To our knowledge, this is the first tuning-free method that concurrently utilizes text and image guidance for image customization in specific regions.
no code implementations • 19 Mar 2024 • Jianlong Hu, Xu Chen, Zhenye Gan, Jinlong Peng, Shengchuan Zhang, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Liujuan Cao, Rongrong Ji
To address the challenge of real-world anomaly detection, we propose a new framework named Dual Memory bank enhanced representation learning for Anomaly Detection (DMAD).
no code implementations • 18 Mar 2024 • Liren He, Zhengkai Jiang, Jinlong Peng, Liang Liu, Qiangang Du, Xiaobin Hu, Wenbing Zhu, Mingmin Chi, Yabiao Wang, Chengjie Wang
In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of ``learning shortcuts'', wherein the model fails to learn the patterns of normal samples as it should, opting instead for shortcuts such as identity mapping or artificial noise elimination.
no code implementations • 11 Mar 2024 • Qingdong He, Jinlong Peng, Zhengkai Jiang, Xiaobin Hu, Jiangning Zhang, Qiang Nie, Yabiao Wang, Chengjie Wang
On top of that, PointSeg can incorporate with various segmentation models and even surpasses the supervised methods.
no code implementations • 10 Mar 2024 • Xiaobin Hu, Xu Peng, Donghao Luo, Xiaozhong Ji, Jinlong Peng, Zhengkai Jiang, Jiangning Zhang, Taisong Jin, Chengjie Wang, Rongrong Ji
Our DiffuMatting shows several potential applications (e. g., matting-data generator, community-friendly art design and controllable generation).
1 code implementation • 21 Jan 2024 • Qingdong He, Jinlong Peng, Zhengkai Jiang, Kai Wu, Xiaozhong Ji, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Mingang Chen, Yunsheng Wu
3D open-vocabulary scene understanding aims to recognize arbitrary novel categories beyond the base label space.
no code implementations • 15 Dec 2023 • Shizhan Liu, Zhengkai Jiang, Yuxi Li, Jinlong Peng, Yabiao Wang, Weiyao Lin
Active domain adaptation has emerged as a solution to balance the expensive annotation cost and the performance of trained models in semantic segmentation.
1 code implementation • 7 Sep 2023 • Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Liang Liu, Yabiao Wang, Chengjie Wang
To improve the facial representation quality, we use feature map of a pre-trained visual backbone as a supervision item and use a partially pre-trained decoder for mask image modeling.
2 code implementations • 7 Sep 2023 • Teng Hu, Ran Yi, Haokun Zhu, Liang Liu, Jinlong Peng, Yabiao Wang, Chengjie Wang, Lizhuang Ma
To solve the problem, we propose Compositional Neural Painter, a novel stroke-based rendering framework which dynamically predicts the next painting region based on the current canvas, instead of dividing the image plane uniformly into painting regions.
no code implementations • 14 Aug 2023 • Mufeng Yao, Jiaqi Wang, Jinlong Peng, Mingmin Chi, Chao Liu
Given the extracted flow, the flow-guided feature augmentation is designed to augment the object detection feature based on its optical flow, which improves the detection of small objects.
1 code implementation • CVPR 2023 • Yue Wang, Jinlong Peng, Jiangning Zhang, Ran Yi, Yabiao Wang, Chengjie Wang
2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields.
Ranked #3 on RGB+3D Anomaly Detection and Segmentation on MVTEC 3D-AD (using extra training data)
Contrastive Learning RGB+3D Anomaly Detection and Segmentation
no code implementations • 23 Aug 2022 • Boshen Zhang, Yuxi Li, Yuanpeng Tu, Jinlong Peng, Yabiao Wang, Cunlin Wu, Yang Xiao, Cairong Zhao
Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set.
no code implementations • 13 May 2022 • Jinlong Peng, Zekun Luo, Liang Liu, Boshen Zhang, Tao Wang, Yabiao Wang, Ying Tai, Chengjie Wang, Weiyao Lin
Image harmonization aims to generate a more realistic appearance of foreground and background for a composite image.
no code implementations • 8 Feb 2022 • Zhengkai Jiang, Zhangxuan Gu, Jinlong Peng, Hang Zhou, Liang Liu, Yabiao Wang, Ying Tai, Chengjie Wang, Liqing Zhang
In contrast, we present a simple and efficient single-stage VIS framework based on the instance segmentation method CondInst by adding an extra tracking head.
Ranked #36 on Video Instance Segmentation on YouTube-VIS validation
no code implementations • 24 May 2021 • Jinlong Peng, Zhengkai Jiang, Yueyang Gu, Yang Wu, Yabiao Wang, Ying Tai, Chengjie Wang, Weiyao Lin
In addition, we add a localization branch to predict the localization accuracy, so that it can work as the replacement of the regression assistance link during inference.
1 code implementation • NeurIPS 2020 • Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin
In this paper, we address several inadequacies of current video object segmentation pipelines.
no code implementations • 30 Jul 2020 • Jinlong Peng, Yueyang Gu, Yabiao Wang, Chengjie Wang, Jilin Li, Feiyue Huang
Multiple Object Tracking (MOT) is an important task in computer vision.
1 code implementation • ECCV 2020 • Jinlong Peng, Changan Wang, Fangbin Wan, Yang Wu, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Yanwei Fu
Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution.