5 papers
Spectral Evolution-Guided Token Pruning in Multimodal Large Language Models
Bin Chen, Yuxiang Cai, Yadan Luo +3
Reducing visual token redundancy is critical for accelerating Multimodal Large Language Models (MLLMs) without degrading cross-modal reasoning performance. Existing token pruning m…
OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios
Xinyi Li, Zhen Fang, Yongxin Deng +12
Hallucination detection is essential for the reliable deployment of large language models (LLMs). However, existing evaluations face two core challenges: inconsistent inference con…
Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Models
Yuanwei Hu, Bo Peng, Yadan Luo +3
Out-of-distribution (OOD) detection has emerged as a popular technique to enhance the reliability of machine learning models by identifying unexpected inputs from unknown classes.…
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection
Bo Peng, Yadan Luo, Yonggang Zhang +2
Post-hoc out-of-distribution (OOD) detection has garnered intensive attention in reliable machine learning. Many efforts have been dedicated to deriving score functions based on lo…
Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents
Zhizhen Zhang, Lei Zhu, Zhen Fang +2
Pre-training vision-language representations on human action videos has emerged as a promising approach to reduce reliance on large-scale expert demonstrations for training embodie…