1 code implementation • 30 Apr 2024 • Jie Hu, Yawen Huang, Yilin Lu, Guoyang Xie, Guannan Jiang, Yefeng Zheng, Zhichao Lu
The AnomalyXFusion framework comprises two distinct yet synergistic modules: the Multi-modal In-Fusion (MIF) module and the Dynamic Dif-Fusion (DDF) module.
1 code implementation • 29 Apr 2024 • Zhuohao Li, Guoyang Xie, Guannan Jiang, Zhichao Lu
Transformer recently emerged as the de facto model for computer vision tasks and has also been successfully applied to shadow removal.
no code implementations • 17 Apr 2024 • Yongdong Luo, Haojia Lin, Xiawu Zheng, Yigeng Jiang, Fei Chao, Jie Hu, Guannan Jiang, Songan Zhang, Rongrong Ji
3D Visual Grounding (3DVG) and 3D Dense Captioning (3DDC) are two crucial tasks in various 3D applications, which require both shared and complementary information in localization and visual-language relationships.
1 code implementation • 30 Nov 2023 • Yiwei Ma, Yijun Fan, Jiayi Ji, Haowei Wang, Xiaoshuai Sun, Guannan Jiang, Annan Shu, Rongrong Ji
Nevertheless, a substantial domain gap exists between 2D images and 3D assets, primarily attributed to variations in camera-related attributes and the exclusive presence of foreground objects.
1 code implementation • 1 Nov 2023 • Minglang Huang, Yiyi Zhou, Gen Luo, Guannan Jiang, Weilin Zhuang, Xiaoshuai Sun
To address this issue, we propose a new learning task for RES called Omni-supervised Referring Expression Segmentation (Omni-RES), which aims to make full use of unlabeled, fully labeled and weakly labeled data, e. g., referring points or grounding boxes, for efficient RES training.
1 code implementation • ICCV 2023 • Jie Hu, Chen Chen, Liujuan Cao, Shengchuan Zhang, Annan Shu, Guannan Jiang, Rongrong Ji
Through extensive experiments conducted on the COCO and Cityscapes datasets, we demonstrate that PAIS is a promising framework for semi-supervised instance segmentation, particularly in cases where labeled data is severely limited.
no code implementations • 4 Aug 2023 • Shuman Fang, Shuai Liu, Jie Li, Guannan Jiang, Xianming Lin, Rongrong Ji
Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects, which plays a curtail role in high-level semantic understanding tasks.
no code implementations • 27 Jun 2023 • Qiong Wu, Shubin Huang, Yiyi Zhou, Pingyang Dai, Annan Shu, Guannan Jiang, Rongrong Ji
Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens.
1 code implementation • ICCV 2023 • You Huang, Hao Yang, Ke Sun, Shengchuan Zhang, Liujuan Cao, Guannan Jiang, Rongrong Ji
Interactive image segmentation enables annotators to efficiently perform pixel-level annotation for segmentation tasks.
1 code implementation • ICCV 2023 • Yiwei Ma, Xiaioqing Zhang, Xiaoshuai Sun, Jiayi Ji, Haowei Wang, Guannan Jiang, Weilin Zhuang, Rongrong Ji
Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a target text.
1 code implementation • 15 Mar 2023 • Jinxiang Lai, Siqian Yang, Wenlong Wu, Tao Wu, Guannan Jiang, Xi Wang, Jun Liu, Bin-Bin Gao, Wei zhang, Yuan Xie, Chengjie Wang
Then we derive two specific attention modules, named SpatialFormer Semantic Attention (SFSA) and SpatialFormer Target Attention (SFTA), to enhance the target object regions while reduce the background distraction.
1 code implementation • 16 Feb 2023 • Gen Luo, Minglang Huang, Yiyi Zhou, Xiaoshuai Sun, Guannan Jiang, Zhiyu Wang, Rongrong Ji
Experimental results show the superior performance and efficiency of RepAdapter than the state-of-the-art PETL methods.
no code implementations • CVPR 2023 • Lei Jin, Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Guannan Jiang, Annan Shu, Rongrong Ji
Based on RefCLIP, we further propose the first model-agnostic weakly supervised training scheme for existing REC models, where RefCLIP acts as a mature teacher to generate pseudo-labels for teaching common REC models.
no code implementations • CVPR 2023 • Tenghao Cai, Zhizhong Zhang, Xin Tan, Yanyun Qu, Guannan Jiang, Chengjie Wang, Yuan Xie
As a result, our dynamic inference network is trained independently of baseline and provides a flexible, efficient solution to distinguish between tasks.
no code implementations • ICCV 2023 • Xudong Tian, Zhizhong Zhang, Xin Tan, Jun Liu, Chengjie Wang, Yanyun Qu, Guannan Jiang, Yuan Xie
Continual Learning (CL) is the constant development of complex behaviors by building upon previously acquired skills.
no code implementations • CVPR 2023 • Jiamu Sun, Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Guannan Jiang, Zhiyu Wang, Rongrong Ji
In this paper, we present the first attempt of semi-supervised learning for REC and propose a strong baseline method called RefTeacher.
no code implementations • ICCV 2023 • Zhiwei Chen, Jinren Ding, Liujuan Cao, Yunhang Shen, Shengchuan Zhang, Guannan Jiang, Rongrong Ji
Weakly supervised object localization (WSOL) aims to localize objects based on only image-level labels as supervision.
2 code implementations • ICCV 2023 • Zhihao Gu, Liang Liu, Xu Chen, Ran Yi, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Annan Shu, Guannan Jiang, Lizhuang Ma
Specifically, we first propose a normality recall memory (NR Memory) to strengthen the normality of student-generated features by recalling the stored normal information.
Ranked #11 on Anomaly Detection on MVTec AD
no code implementations • 23 Nov 2022 • Jiawei Zhan, Jun Liu, Wei Tang, Guannan Jiang, Xi Wang, Bin-Bin Gao, Tianliang Zhang, Wenlong Wu, Wei zhang, Chengjie Wang, Yuan Xie
This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning.
no code implementations • 2 Nov 2022 • Jinxiang Lai, Siqian Yang, Guannan Jiang, Xi Wang, Yuxi Li, Zihui Jia, Xiaochen Chen, Jun Liu, Bin-Bin Gao, Wei zhang, Yuan Xie, Chengjie Wang
In this paper, for the first time, we investigate the contributions of different distance metrics, and propose an adaptive fusion scheme, bringing significant improvements in few-shot classification.
1 code implementation • Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022) 2022 • Bin-Bin Gao, Xiaochen Chen, Zhongyi Huang, Congchong Nie, Jun Liu, Jinxiang Lai, Guannan Jiang, Xi Wang, Chengjie Wang
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances.
Ranked #3 on Few-Shot Object Detection on MS-COCO (1-shot)
1 code implementation • CVPR 2022 • Fan Yang, Kai Wu, Shuyi Zhang, Guannan Jiang, Yong liu, Feng Zheng, Wei zhang, Chengjie Wang, Long Zeng
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization.
1 code implementation • CVPR 2022 • Shaohua Guo, Liang Liu, Zhenye Gan, Yabiao Wang, Wuhao Zhang, Chengjie Wang, Guannan Jiang, Wei zhang, Ran Yi, Lizhuang Ma, Ke Xu
The huge burden of computation and memory are two obstacles in ultra-high resolution image segmentation.
no code implementations • 10 Dec 2021 • Zhiwei Chen, Changan Wang, Yabiao Wang, Guannan Jiang, Yunhang Shen, Ying Tai, Chengjie Wang, Wei zhang, Liujuan Cao
In this paper, we propose a novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies.
1 code implementation • 16 Sep 2021 • Yuexiao Ma, Taisong Jin, Xiawu Zheng, Yan Wang, Huixia Li, Yongjian Wu, Guannan Jiang, Wei zhang, Rongrong Ji
Instead of solving a problem of the original integer programming, we propose to optimize a proxy metric, the concept of network orthogonality, which is highly correlated with the loss of the integer programming but also easy to optimize with linear programming.