4 citations · 6 across the 8 of their papers we have counts for
9 papers
HTPO: Towards Exploration-Exploitation Balanced Policy Optimization via Hierarchical Token-level Objective Control
Xincheng Yao, Ruoqi Li, Cheng Chen +4
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a pivotal technique for enhancing the reasoning capabilities of Large Language Models (LLMs). However, the de f…
MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models
Xincheng Yao, Zefeng Qian, Chao Shi +2
In the progress of industrial anomaly detection, general anomaly detection (GAD) is an emerging trend and also the ultimate goal. Unlike the conventional single- and multi-class AD…
ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining
Xincheng Yao, Yan Luo, Zefeng Qian +1
The current mainstream and state-of-the-art anomaly detection (AD) methods are substantially established on pretrained feature networks yielded by ImageNet pretraining. However, re…
ResAD++: Towards Class Agnostic Anomaly Detection via Residual Feature Learning
Xincheng Yao, Chao Shi, Muming Zhao +2
This paper explores the problem of class-agnostic anomaly detection (AD), where the objective is to train one class-agnostic AD model that can generalize to detect anomalies in div…
Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition
Zefeng Qian, Xincheng Yao, Yifei Huang +3
Few-shot action recognition (FSAR) aims to classify human actions in videos with only a small number of labeled samples per category. The scarcity of training data has driven recen…
ResAD: A Simple Framework for Class Generalizable Anomaly Detection
Xincheng Yao, Zixin Chen, Chao Gao +2
This paper explores the problem of class-generalizable anomaly detection, where the objective is to train one unified AD model that can generalize to detect anomalies in diverse cl…
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection
Xincheng Yao, Ruoqi Li, Zefeng Qian +2
Unified anomaly detection (AD) is one of the most challenges for anomaly detection, where one unified model is trained with normal samples from multiple classes with the objective…
Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection
Xincheng Yao, Ruoqi Li, Zefeng Qian +2
Humans recognize anomalies through two aspects: larger patch-wise representation discrepancies and weaker patch-to-normal-patch correlations. However, the previous AD methods didn'…
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection
Xincheng Yao, Ruoqi Li, Jing Zhang +2
Most anomaly detection (AD) models are learned using only normal samples in an unsupervised way, which may result in ambiguous decision boundary and insufficient discriminability.…