133 citations · 332 across the 25 of their papers we have counts for
25 papers
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM
Huaxin Zhang, Xiaohao Xu, Xiang Wang +6
Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit biased detection when faced with challenging or unseen events and lack interpretability. To address…
From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking
Xiaohao Xu, Tianyi Zhang, Sibo Wang +6
Embodied agents require robust navigation systems to operate in unstructured environments, making the robustness of Simultaneous Localization and Mapping (SLAM) models critical to…
LogiCode: an LLM-Driven Framework for Logical Anomaly Detection
Yiheng Zhang, Yunkang Cao, Xiaohao Xu +1
This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond traditional focu…
Learning Shared RGB-D Fields: Unified Self-supervised Pre-training for Label-efficient LiDAR-Camera 3D Perception
Xiaohao Xu, Ye Li, Tianyi Zhang +3
Constructing large-scale labeled datasets for multi-modal perception model training in autonomous driving presents significant challenges. This has motivated the development of sel…
GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection
Huaxin Zhang, Xiang Wang, Xiaohao Xu +6
In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite signif…
-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
Xiang Li, Kai Qiu, Jinglu Wang +6
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the…
Is Your LiDAR Placement Optimized for 3D Scene Understanding?
Ye Li, Lingdong Kong, Hanjiang Hu +2
The reliability of driving perception systems under unprecedented conditions is crucial for practical usage. Latest advancements have prompted increasing interest in multi-LiDAR pe…
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning
Xiaohao Xu, Yunkang Cao, Huaxin Zhang +2
Anomaly detection is vital in various industrial scenarios, including the identification of unusual patterns in production lines and the detection of manufacturing defects for qual…
Customizable Perturbation Synthesis for Robust SLAM Benchmarking
Xiaohao Xu, Tianyi Zhang, Sibo Wang +6
Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Sim…
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
Yunkang Cao, Xiaohao Xu, Jiangning Zhang +4
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspect…
Generative Denoise Distillation: Simple Stochastic Noises Induce Efficient Knowledge Transfer for Dense Prediction
Zhaoge Liu, Xiaohao Xu, Yunkang Cao +1
Knowledge distillation is the process of transferring knowledge from a more powerful large model (teacher) to a simpler counterpart (student). Numerous current approaches involve t…
Towards Transferable Multi-modal Perception Representation Learning for Autonomy: NeRF-Supervised Masked AutoEncoder
Xiaohao Xu
This work proposes a unified self-supervised pre-training framework for transferable multi-modal perception representation learning via masked multi-modal reconstruction in Neural…
Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead
Yunkang Cao, Xiaohao Xu, Chen Sun +2
Anomaly detection is a crucial task across different domains and data types. However, existing anomaly detection models are often designed for specific domains and modalities. This…
QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition
Xiang Li, Jinglu Wang, Xiaohao Xu +4
Audiovisual segmentation (AVS) is a challenging task that aims to segment visual objects in videos according to their associated acoustic cues. With multiple sound sources and back…
PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation
Shilin Yan, Xiaohao Xu, Renrui Zhang +4
Panoramic videos contain richer spatial information and have attracted tremendous amounts of attention due to their exceptional experience in some fields such as autonomous driving…
HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation
Huaxin Zhang, Xiang Wang, Xiaohao Xu +3
Point-supervised Temporal Action Localization (PSTAL) is an emerging research direction for label-efficient learning. However, current methods mainly focus on optimizing the networ…
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection
Yunkang Cao, Xiaohao Xu, Chen Sun +3
This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal…
Segment Any Anomaly without Training via Hybrid Prompt Regularization
Yunkang Cao, Xiaohao Xu, Chen Sun +4
We present a novel framework, i.e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundat…
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
Yunkang Cao, Xiaohao Xu, Weiming Shen
Point cloud (PCD) anomaly detection steadily emerges as a promising research area. This study aims to improve PCD anomaly detection performance by combining handcrafted PCD descrip…
Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization
Yunkang Cao, Xiaohao Xu, Zhaoge Liu +1
Most unsupervised image anomaly localization methods suffer from overgeneralization because of the high generalization abilities of convolutional neural networks, leading to unreli…
Optimization of Forcemyography Sensor Placement for Arm Movement Recognition
Xiaohao Xu, Zihao Du, Huaxin Zhang +4
How to design an optimal wearable device for human movement recognition is vital to reliable and accurate human-machine collaboration. Previous works mainly fabricate wearable devi…
Online Video Instance Segmentation via Robust Context Fusion
Xiang Li, Jinglu Wang, Xiaohao Xu +2
Video instance segmentation (VIS) aims at classifying, segmenting and tracking object instances in video sequences. Recent transformer-based neural networks have demonstrated their…
Towards Robust Video Object Segmentation with Adaptive Object Calibration
Xiaohao Xu, Jinglu Wang, Xiang Ming +1
In the booming video era, video segmentation attracts increasing research attention in the multimedia community. Semi-supervised video object segmentation (VOS) aims at segmenting…
Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus
Xiang Li, Jinglu Wang, Xiaohao Xu +3
Referring Video Object Segmentation (R-VOS) is a challenging task that aims to segment an object in a video based on a linguistic expression. Most existing R-VOS methods have a cri…