most citedQuo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20241 cited

Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight

Xi Ding, Lei Wang

Video anomaly detection (VAD) has witnessed significant advancements through the integration of large language models (LLMs) and vision-language models (VLMs), addressing critical…

cs.CV2024

Attention-driven GUI Grounding: Leveraging Pretrained Multimodal Large Language Models without Fine-Tuning

Hai-Ming Xu, Qi Chen, Lei Wang +1

Recent advancements in Multimodal Large Language Models (MLLMs) have generated significant interest in their ability to autonomously interact with and interpret Graphical User Inte…

cs.CL2024

CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds

Lei Wang, Jianxun Lian, Yi Huang +5

Role-playing is a crucial capability of Large Language Models (LLMs), enabling a wide range of practical applications, including intelligent non-player characters, digital twins, a…

astro-ph.IM2024

UPdec-Webb: A Dataset for Coaddition of JWST NIRCam Images

Lei Wang, Huanyuan Shan, Lin Nie +19

We present the application of the image coaddition algorithm, Up-sampling and PSF Deconvolution Coaddition (UPDC), for stacking multiple exposure images captured by the James Webb…

physics.flu-dyn2024

Phase-field based lattice Boltzmann method for containerless freezing

Jiangxu Huang, Lei Wang, Zhenhua Chai +1

In this paper, a lattice Boltzmann model is proposed to simulate solid-liquid phase change phenomena in multiphase systems. The model couples the thermal properties of the solidifi…

cs.CV2024

Gradient-Aware Logit Adjustment Loss for Long-tailed Classifier

Fan Zhang, Wei Qin, Weijieying Ren +3

In the real-world setting, data often follows a long-tailed distribution, where head classes contain significantly more training samples than tail classes. Consequently, models tra…