36 citations · 79 across the 20 of their papers we have counts for
20 papers
SDDF: Specificity-Driven Dynamic Focusing for Open-Vocabulary Camouflaged Object Detection
Jiaming Liang, Yifeng Zhan, Chunlin Liu +6
Open-vocabulary object detection (OVOD) aims to detect known and unknown objects in the open world by leveraging text prompts. Benefiting from the emergence of large-scale vision--…
P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked Autoencoders
Xuechao Chen, Ying Chen, Jialin Li +5
3D pre-training is crucial to 3D perception tasks. Nevertheless, limited by the difficulties in collecting clean and complete 3D data, 3D pre-training has persistently faced data s…
Decision Boundary-aware Knowledge Consolidation Generates Better Instance-Incremental Learner
Qiang Nie, Weifu Fu, Yuhuan Lin +5
Instance-incremental learning (IIL) focuses on learning continually with data of the same classes. Compared to class-incremental learning (CIL), the IIL is seldom explored because…
SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection
Qiangang Du, Jinlong Peng, Xu Chen +4
In recent years, change detection and anomaly detection models based on CNN and transformer have achieved remarkable success across various datasets based on paired data. However,…
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Xi Jiang, Ying Chen, Qiang Nie +6
Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experim…
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
Xi Jiang, Ying Chen, Qiang Nie +4
In the context of high usability in single-class anomaly detection models, recent academic research has become concerned about the more complex multi-class anomaly detection. Altho…
Tuning-Free Image Customization with Image and Text Guidance
Pengzhi Li, Qiang Nie, Ying Chen +7
Despite significant advancements in image customization with diffusion models, current methods still have several limitations: 1) unintended changes in non-target areas when regene…
HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching
Ying Chen, Yong Liu, Kai Wu +5
Deep learning-based image matching methods play a crucial role in computer vision, yet they often suffer from substantial computational demands. To tackle this challenge, we presen…
LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking
Jialin Li, Qiang Nie, Weifu Fu +4
Deep learning models, particularly those based on transformers, often employ numerous stacked structures, which possess identical architectures and perform similar functions. While…
PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models
Qingdong He, Jinlong Peng, Zhengkai Jiang +2
Recent success of vision foundation models have shown promising performance for the 2D perception tasks. However, it is difficult to train a 3D foundation network directly due to t…
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
Jiaqi Liu, Kai Wu, Qiang Nie +6
Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible. H…
Beyond Prototypes: Semantic Anchor Regularization for Better Representation Learning
Yanqi Ge, Qiang Nie, Ye Huang +5
One of the ultimate goals of representation learning is to achieve compactness within a class and well-separability between classes. Many outstanding metric-based and prototype-bas…
Can the Query-based Object Detector Be Designed with Fewer Stages?
Jialin Li, Weifu Fu, Yuhuan Lin +2
Query-based object detectors have made significant advancements since the publication of DETR. However, most existing methods still rely on multi-stage encoders and decoders, or a…
IIDM: Inter and Intra-domain Mixing for Semi-supervised Domain Adaptation in Semantic Segmentation
Weifu Fu, Qiang Nie, Jialin Li +6
Despite recent advances in semantic segmentation, an inevitable challenge is the performance degradation caused by the domain shift in real applications. Current dominant approach…
Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes
Donghao Zhou, Jialin Li, Jinpeng Li +7
Large-scale well-annotated datasets are of great importance for training an effective object detector. However, obtaining accurate bounding box annotations is laborious and demandi…
NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
Jianlin Liu, Qiang Nie, Yong Liu +1
We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer. A conditional neural radiance field…
HopFIR: Hop-wise GraphFormer with Intragroup Joint Refinement for 3D Human Pose Estimation
Kai Zhai, Qiang Nie, Bo Ouyang +2
2D-to-3D human pose lifting is fundamental for 3D human pose estimation (HPE), for which graph convolutional networks (GCNs) have proven inherently suitable for modeling the human…
Rethinking Dimensionality Reduction in Grid-based 3D Object Detection
Dihe Huang, Ying Chen, Yikang Ding +7
Bird's eye view (BEV) is widely adopted by most of the current point cloud detectors due to the applicability of well-explored 2D detection techniques. However, existing methods ob…
Lifting 2D Human Pose to 3D with Domain Adapted 3D Body Concept
Qiang Nie, Ziwei Liu, Yunhui Liu
Lifting the 2D human pose to the 3D pose is an important yet challenging task. Existing 3D pose estimation suffers from 1) the inherent ambiguity between the 2D and 3D data, and 2)…
Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement
Qiang Nie, Ziwei Liu, Yunhui Liu
Learning a good 3D human pose representation is important for human pose related tasks, e.g. human 3D pose estimation and action recognition. Within all these problems, preserving…