4 citations · 8 across the 15 of their papers we have counts for
15 papers
PET-DINO: Unifying Visual Cues into Grounding DINO with Prompt-Enriched Training
Weifu Fu, Jinyang Li, Bin-Bin Gao +6
Open-Set Object Detection (OSOD) enables recognition of novel categories beyond fixed classes but faces challenges in aligning text representations with complex visual concepts and…
Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detection
Yuanting Fan, Jun Liu, Xiaochen Chen +5
Few-shot anomaly detection (FSAD) methods identify anomalous regions with few known normal samples. Most existing methods rely on the generalization ability of pre-trained vision-l…
DRL: Discriminative Representation Learning with Parallel Adapters for Class Incremental Learning
Jiawei Zhan, Jun Liu, Jinlong Peng +4
With the excellent representation capabilities of Pre-Trained Models (PTMs), remarkable progress has been made in non-rehearsal Class-Incremental Learning (CIL) research. However,…
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
Bin-Bin Gao, Yue Zhou, Jiangtao Yan +7
Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studie…
CAR: Controllable Autoregressive Modeling for Visual Generation
Ziyu Yao, Jialin Li, Yifeng Zhou +6
Controllable generation, which enables fine-grained control over generated outputs, has emerged as a critical focus in visual generative models. Currently, there are two primary te…
MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
Xi Jiang, Jian Li, Hanqiu Deng +6
In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to renew the paradigms in practical applications due to their robust language…
Anno-incomplete Multi-dataset Detection
Yiran Xu, Haoxiang Zhong, Kai Wu +5
Object detectors have shown outstanding performance on various public datasets. However, annotating a new dataset for a new task is usually unavoidable in real, since 1) a single e…
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…
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…
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…