21 citations · 23 across the 8 of their papers we have counts for
7 papers · 1 filter
IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection
Zewen Li, Zitong Yu, Qilang Ye +3
The robust causal capability of Multimodal Large Language Models (MLLMs) hold the potential of detecting defective objects in Industrial Anomaly Detection (IAD). However, most trad…
Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification
Wei Zhuo, Runjie Luo, Wufeng Xue +1
Few-shot Learning (FSL), which endeavors to develop the generalization ability for recognizing novel classes using only a few images, faces significant challenges due to data scarc…
UINO-FSS: Unifying Representation Learning and Few-shot Segmentation via Hierarchical Distillation and Mamba-HyperCorrelation
Wei Zhuo, Zhiyue Tang, Wufeng Xue +3
Few-shot semantic segmentation has attracted growing interest for its ability to generalize to novel object categories using only a few annotated samples. To address data scarcity,…
EchoONE: Segmenting Multiple echocardiography Planes in One Model
Jiongtong Hu, Wei Zhuo, Jun Cheng +3
In clinical practice of echocardiography examinations, multiple planes containing the heart structures of different view are usually required in screening, diagnosis and treatment…
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation
Weizhao He, Yang Zhang, Wei Zhuo +4
Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their traini…
Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation
Songhe Deng, Wei Zhuo, Jinheng Xie +1
Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only imag…
ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
Lihe Yang, Wei Zhuo, Lei Qi +2
Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage unlabeled data. In this work, we first construct a strong baseline of self-training (n…