no code implementations • ECCV 2020 • Guo-Sen Xie, Li Liu, Fan Zhu, Fang Zhao, Zheng Zhang, Yazhou Yao, Jie Qin, Ling Shao
To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.
no code implementations • ECCV 2020 • Fang Zhao, Shengcai Liao, Guo-Sen Xie, Jian Zhao, Kaihao Zhang, Ling Shao
On the other hand, mutual instance selection further selects reliable and informative instances for training according to the peer-confidence and relationship disagreement of the networks.
no code implementations • 17 Apr 2025 • Weijia Li, Guanglei Chu, Jiong Chen, Guo-Sen Xie, Caifeng Shan, Fang Zhao
To address these limitations, we introduce a new task, Reasoning Logical Anomaly Detection (RLAD), which extends traditional anomaly detection by incorporating logical reasoning.
1 code implementation • 31 Dec 2024 • Shi-Feng Peng, Guolei Sun, Yong Li, Hongsong Wang, Guo-Sen Xie
In contrast, the large-scale visual model SAM, pre-trained on tens of millions of images from various domains and classes, possesses excellent generalizability.
1 code implementation • 23 Dec 2024 • Fenfang Tao, Guo-Sen Xie, Fang Zhao, Xiangbo Shu
Specifically, a kernel-aware hierarchical graph is built by taking the different layer features focusing on anomalous regions of different sizes as nodes, meanwhile, the relationships between arbitrary pairs of nodes stand for the edges of the graph.
1 code implementation • 23 Dec 2024 • Jiaqi Ma, Guo-Sen Xie, Fang Zhao, Zechao Li
Therefore, in this paper, we utilize the more challenging image-level annotations and propose an adaptive frequency-aware network (AFANet) for weakly-supervised few-shot semantic segmentation (WFSS).
1 code implementation • 18 Nov 2024 • Bowen Duan, Shiming Chen, Yufei Guo, Guo-Sen Xie, Weiping Ding, Yisong Wang
However, methods trained under this paradigm often struggle to learn robust embedding space because they align the two modalities in an isolated manner among classes, which ignore the crucial class relationship during the alignment process.
1 code implementation • 27 Sep 2024 • Yuli Zhou, Guolei Sun, Yawei Li, Guo-Sen Xie, Luca Benini, Ender Konukoglu
This study presents a comprehensive study on SAM2's ability in VCOS.
no code implementations • 4 May 2024 • Meiqi Cao, Rui Yan, Xiangbo Shu, Guangzhao Dai, Yazhou Yao, Guo-Sen Xie
Therefore, we propose a novel Adapt-Focused bi-Propagating Prototype learning (AdaFPP) framework to jointly recognize individual, group, and global activities in panoramic activity scenes by learning an adapt-focused detector and multi-granularity prototypes as the pretext tasks in an end-to-end way.
no code implementations • 3 May 2024 • Hongyu Qu, Rui Yan, Xiangbo Shu, Hailiang Gao, Peng Huang, Guo-Sen Xie
Recent few-shot action recognition (FSAR) methods typically perform semantic matching on learned discriminative features to achieve promising performance.
no code implementations • 28 Mar 2024 • Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, Guo-Sen Xie
In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues.
1 code implementation • 16 Jul 2023 • Yin Tang, Tao Chen, Xiruo Jiang, Yazhou Yao, Guo-Sen Xie, Heng-Tao Shen
Existing methods have demonstrated that the domain agent-based attention mechanism is effective in FSVOS by learning the correlation between support images and query frames.
1 code implementation • 8 Apr 2023 • Binqian Xu, Xiangbo Shu, Jiachao Zhang, Rui Yan, Guo-Sen Xie
In particular, we propose a novel Attack-Augmentation Mixing-Contrastive skeletal representation learning (A$^2$MC) to contrast hard positive features and hard negative features for learning more robust skeleton representations.
no code implementations • CVPR 2023 • Fang Zhao, Zekun Li, Shaoli Huang, Junwu Weng, Tianfei Zhou, Guo-Sen Xie, Jue Wang, Ying Shan
Once the anchor transformations are found, per-vertex nonlinear displacements of the garment template can be regressed in a canonical space, which reduces the complexity of deformation space learning.
1 code implementation • 18 Jul 2022 • Gensheng Pei, Fumin Shen, Yazhou Yao, Guo-Sen Xie, Zhenmin Tang, Jinhui Tang
Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS).
1 code implementation • 20 Jun 2022 • Tao Chen, Yazhou Yao, Lei Zhang, Qiong Wang, Guo-Sen Xie, Fumin Shen
Specifically, we propose a saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes.
no code implementations • CVPR 2022 • Jie Liu, Yanqi Bao, Guo-Sen Xie, Huan Xiong, Jan-Jakob Sonke, Efstratios Gavves
Specifically, in DPCN, a dynamic convolution module (DCM) is firstly proposed to generate dynamic kernels from support foreground, then information interaction is achieved by convolution operations over query features using these kernels.
Ranked #36 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
2 code implementations • CVPR 2022 • Shiming Chen, Ziming Hong, Guo-Sen Xie, Wenhan Yang, Qinmu Peng, Kai Wang, Jian Zhao, Xinge You
Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e. g., attribute semantics) between visual and attribute features.
1 code implementation • 16 Dec 2021 • Shiming Chen, Ziming Hong, Wenjin Hou, Guo-Sen Xie, Yibing Song, Jian Zhao, Xinge You, Shuicheng Yan, Ling Shao
Analogously, VAT uses the similar feature augmentation encoder to refine the visual features, which are further applied in visual$\rightarrow$attribute decoder to learn visual-based attribute features.
1 code implementation • 3 Dec 2021 • Shiming Chen, Ziming Hong, Yang Liu, Guo-Sen Xie, Baigui Sun, Hao Li, Qinmu Peng, Ke Lu, Xinge You
Although some attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected.
2 code implementations • NeurIPS 2021 • Shiming Chen, Guo-Sen Xie, Yang Liu, Qinmu Peng, Baigui Sun, Hao Li, Xinge You, Ling Shao
Specifically, HSVA aligns the semantic and visual domains by adopting a hierarchical two-step adaptation, i. e., structure adaptation and distribution adaptation.
1 code implementation • CVPR 2021 • Guo-Sen Xie, Jie Liu, Huan Xiong, Ling Shao
However, they fail to fully leverage the high-order appearance relationships between multi-scale features among the support-query image pairs, thus leading to an inaccurate localization of the query objects.
no code implementations • ICCV 2021 • Guo-Sen Xie, Huan Xiong, Jie Liu, Yazhou Yao, Ling Shao
Specifically, we first generate N pairs (key and value) of multi-resolution query features guided by the support feature and its mask.
1 code implementation • 11 Nov 2019 • Shiming Chen, Peng Zhang, Guo-Sen Xie, Qinmu Peng, Zehong Cao, Wei Yuan, Xinge You
Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge.
no code implementations • CVPR 2019 • Guo-Sen Xie, Li Liu, Xiaobo Jin, Fan Zhu, Zheng Zhang, Jie Qin, Yazhou Yao, Ling Shao
Zero-shot learning (ZSL) aims to classify images from unseen categories, by merely utilizing seen class images as the training data.
no code implementations • 3 Apr 2019 • Zheng Zhang, Guo-Sen Xie, Yang Li, Sheng Li, Zi Huang
Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications.
no code implementations • 29 Jan 2016 • Guo-Sen Xie, Xu-Yao Zhang, Shuicheng Yan, Cheng-Lin Liu
Learned from a large-scale training dataset, CNN features are much more discriminative and accurate than the hand-crafted features.
no code implementations • ICCV 2015 • Guo-Sen Xie, Xu-Yao Zhang, Xiangbo Shu, Shuicheng Yan, Cheng-Lin Liu
Feature pooling is an important strategy to achieve high performance in image classification.