14 citations · 76 across the 14 of their papers we have counts for
14 papers
Synth4Seg -- Learning Defect Data Synthesis for Defect Segmentation using Bi-level Optimization
Shancong Mou, Raviteja Vemulapalli, Shiyu Li +8
Defect segmentation is crucial for quality control in advanced manufacturing, yet data scarcity poses challenges for state-of-the-art supervised deep learning. Synthetic defect dat…
A Survey of Multimodal Large Language Model from A Data-centric Perspective
Tianyi Bai, Hao Liang, Binwang Wan +12
Multimodal large language models (MLLMs) enhance the capabilities of standard large language models by integrating and processing data from multiple modalities, including text, vis…
ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models
Jingyuan Zhu, Shiyu Li, Yuxuan Liu +4
Modern diffusion-based image generative models have made significant progress and become promising to enrich training data for the object detection task. However, the generation qu…
VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
Haoping Bai, Shancong Mou, Tatiana Likhomanenko +6
Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often…
Efficient ConvBN Blocks for Transfer Learning and Beyond
Kaichao You, Guo Qin, Anchang Bao +4
Convolution-BatchNorm (ConvBN) blocks are integral components in various computer vision tasks and other domains. A ConvBN block can operate in three modes: Train, Eval, and Deploy…
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection
Shancong Mou, Xiaoyi Gu, Meng Cao +4
Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generat…
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection
Xuan Zhang, Shiyu Li, Xi Li +3
Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework ha…
Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey
Shancong Mou, Meng Cao, Zhendong Hong +3
Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process. However, the severe imbalanced data, especially the…
Information Gain Propagation: a new way to Graph Active Learning with Soft Labels
Wentao Zhang, Yexin Wang, Zhenbang You +5
Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires consider…
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation
Haoping Bai, Meng Cao, Ping Huang +1
As the adoption of deep learning techniques in industrial applications grows with increasing speed and scale, successful deployment of deep learning models often hinges on the avai…
RIM: Reliable Influence-based Active Learning on Graphs
Wentao Zhang, Yexin Wang, Zhenbang You +5
Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to…
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation
Tsung-Han Wu, Yueh-Cheng Liu, Yu-Kai Huang +4
Despite the success of deep learning on supervised point cloud semantic segmentation, obtaining large-scale point-by-point manual annotations is still a significant challenge. To r…
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer
Haoping Bai, Meng Cao, Ping Huang +1
As the applications of deep learning models on edge devices increase at an accelerating pace, fast adaptation to various scenarios with varying resource constraints has become a cr…
Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning
Jia-Fong Yeh, Hsin-Ying Lee, Bing-Chen Tsai +3
In recent years, few-shot learning problems have received a lot of attention. While methods in most previous works were trained and tested on datasets in one single domain, cross-d…