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Grounding by Remembering: Cross-Scene and In-Scene Memory for 3D Functional Affordances
Qirui Wang, Jingyi He, Yining Pan +2
Functional affordance grounding requires more than recognizing an object: an agent must localize the specific region that supports an interaction, such as the handle to pull or the…
MonoSR: Open-Vocabulary Spatial Reasoning from Monocular Images
Qirui Wang, Jingyi He, Yining Pan +3
Spatial reasoning (SR), the ability to infer 3D spatial information from 2D inputs, is essential for real-world applications such as embodied AI and autonomous driving. However, ex…
Thinking Ahead: Foresight Intelligence in MLLMs and World Model
Zhantao Gong, Liaoyuan Fan, Qing Guo +3
In this work, we define Foresight Intelligence as the capability to anticipate and interpret future events-an ability essential for applications such as autonomous driving, yet lar…
Patch-as-Decodable-Token: Towards Unified Multi-Modal Vision Tasks in MLLMs
Yongyi Su, Haojie Zhang, Shijie Li +11
Multimodal large language models (MLLMs) have advanced rapidly in recent years. However, existing approaches for vision tasks often rely on indirect representations, such as genera…
DiffPCN: Latent Diffusion Model Based on Multi-view Depth Images for Point Cloud Completion
Zijun Li, Hongyu Yan, Shijie Li +4
Latent diffusion models (LDMs) have demonstrated remarkable generative capabilities across various low-level vision tasks. However, their potential for point cloud completion remai…
AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
Jingyi Liao, Yongyi Su, Rong-Cheng Tu +6
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities across diverse domains, their application to specialized anomaly detection (AD) remains constrain…
Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagation
Tiankai Chen, Yushu Li, Adam Goodge +4
Out-of-distribution (OOD) detection in 3D point cloud data remains a challenge, particularly in applications where safe and robust perception is critical. While existing OOD detect…
SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation
Adam Goodge, Xun Xu, Bryan Hooi +4
As point cloud data increases in prevalence in a variety of applications, the ability to detect out-of-distribution (OOD) point cloud objects becomes critical for ensuring model sa…
Zero-Shot 3D Visual Grounding from Vision-Language Models
Rong Li, Shijie Li, Lingdong Kong +2
3D Visual Grounding (3DVG) seeks to locate target objects in 3D scenes using natural language descriptions, enabling downstream applications such as augmented reality and robotics.…
Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift
Jingyi Liao, Xun Xu, Yongyi Su +4
Anomaly detection plays a crucial role in quality control for industrial applications. However, ensuring robustness under unseen domain shifts such as lighting variations or sensor…
Multi-View Industrial Anomaly Detection with Epipolar Constrained Cross-View Fusion
Yifan Liu, Xun Xu, Shijie Li +2
Multi-camera systems provide richer contextual information for industrial anomaly detection. However, traditional methods process each view independently, disregarding the compleme…
Global-Aware Monocular Semantic Scene Completion with State Space Models
Shijie Li, Zhongyao Cheng, Rong Li +4
Monocular Semantic Scene Completion (MonoSSC) reconstructs and interprets 3D environments from a single image, enabling diverse real-world applications. However, existing methods a…
Future-Aware Interaction Network For Motion Forecasting
Shijie Li, Xun Xu, Si Yong Yeo +1
Motion forecasting is a crucial component of autonomous driving systems, enabling the generation of accurate and smooth future trajectories to ensure safe navigation to the destina…
SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding
Rong Li, Shijie Li, Lingdong Kong +2
3D Visual Grounding (3DVG) aims to locate objects in 3D scenes based on textual descriptions, essential for applications like augmented reality and robotics. Traditional 3DVG appro…
Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition
Qiyu Wei, Xun Xu, Zeng Zeng +1
The patterns on wafer maps play a crucial role in helping engineers identify the causes of production issues during semiconductor manufacturing. In order to reduce costs and improv…
Improving Adversarial Robustness for 3D Point Cloud Recognition at Test-Time through Purified Self-Training
Jinpeng Lin, Xulei Yang, Tianrui Li +1
Recognizing 3D point cloud plays a pivotal role in many real-world applications. However, deploying 3D point cloud deep learning model is vulnerable to adversarial attacks. Despite…
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection
Yushu Li, Yongyi Su, Xulei Yang +2
Existing test-time adaptation (TTA) approaches often adapt models with the unlabeled testing data stream. A recent attempt relaxed the assumption by introducing limited human annot…