2 citations · 2 across the 4 of their papers we have counts for
7 papers
Sparse Reasoning is Enough: Biological-Inspired Framework for Video Anomaly Detection with Large Pre-trained Models
He Huang, Zixuan Hu, Dongxiao Li +2
Video anomaly detection (VAD) plays a vital role in real-world applications such as security surveillance, autonomous driving, and industrial monitoring. Recent advances in large p…
Adaptive Dual Uncertainty Optimization: Boosting Monocular 3D Object Detection under Test-Time Shifts
Zixuan Hu, Dongxiao Li, Xinzhu Ma +4
Accurate monocular 3D object detection (M3OD) is pivotal for safety-critical applications like autonomous driving, yet its reliability deteriorates significantly under real-world d…
LEAD: Exploring Logit Space Evolution for Model Selection
Zixuan Hu, Xiaotong Li, Shixiang Tang +3
The remarkable success of pretrain-then-finetune paradigm has led to a proliferation of available pre-trained models for vision tasks. This surge presents a significant challenge i…
Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI
Sha Zhang, Suorong Yang, Tong Xie +18
Scientific discovery has long been constrained by human limitations in expertise, physical capability, and sleep cycles. The recent rise of AI scientists and automated laboratories…
Beyond Entropy: Region Confidence Proxy for Wild Test-Time Adaptation
Zixuan Hu, Yichun Hu, Xiaotong Li +2
Wild Test-Time Adaptation (WTTA) is proposed to adapt a source model to unseen domains under extreme data scarcity and multiple shifts. Previous approaches mainly focused on sample…
SEVA: Leveraging Single-Step Ensemble of Vicinal Augmentations for Test-Time Adaptation
Zixuan Hu, Yichun Hu, Ling-Yu Duan
Test-Time adaptation (TTA) aims to enhance model robustness against distribution shifts through rapid model adaptation during inference. While existing TTA methods often rely on en…