Cover Image

CELLO: Causal Evaluation of Large Vision-Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Meiqi, Peng, Bo, Zhang, Yan, Lu, Chaochao
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
by: Chen, Meiqi, et al.
Published: (2024)

CauSight: Learning to Supersense for Visual Causal Discovery
by: Zhang, Yize, et al.
Published: (2025)

Interpreting Low-level Vision Models with Causal Effect Maps
by: Hu, Jinfan, et al.
Published: (2024)

Think-Reflect-Revise: A Policy-Guided Reflective Framework for Safety Alignment in Large Vision Language Models
by: Weng, Fenghua, et al.
Published: (2025)

Mitigating Hallucinations in Large Vision-Language Models via Causal Route Gating
by: Cheng, Zhe, et al.
Published: (2026)

ADAM: An Embodied Causal Agent in Open-World Environments
by: Yu, Shu, et al.
Published: (2024)

Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases
by: Chen, Kai, et al.
Published: (2024)

Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models
by: Lu, Jiaying, et al.
Published: (2023)

ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation
by: Peng, Bo, et al.
Published: (2023)

REVAL: A Comprehension Evaluation on Reliability and Values of Large Vision-Language Models
by: Zhang, Jie, et al.
Published: (2025)

Delving into Spectral Clustering with Vision-Language Representations
by: Peng, Bo, et al.
Published: (2026)

Are We on the Right Way for Evaluating Large Vision-Language Models?
by: Chen, Lin, et al.
Published: (2024)

OmniEarth: A Benchmark for Evaluating Vision-Language Models in Geospatial Tasks
by: Fu, Ronghao, et al.
Published: (2026)

MCA-LLaVA: Manhattan Causal Attention for Reducing Hallucination in Large Vision-Language Models
by: Zhao, Qiyan, et al.
Published: (2025)

Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
by: Chen, Jiawei, et al.
Published: (2024)

Evaluating Attribute Comprehension in Large Vision-Language Models
by: Zhang, Haiwen, et al.
Published: (2024)

MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
by: Ying, Kaining, et al.
Published: (2024)

RelationVLM: Making Large Vision-Language Models Understand Visual Relations
by: Huang, Zhipeng, et al.
Published: (2024)

Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models
by: Yan, Bei, et al.
Published: (2024)

Language-Guided Token Compression with Reinforcement Learning in Large Vision-Language Models
by: Cao, Sihan, et al.
Published: (2026)

MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
by: Meng, Fanqing, et al.
Published: (2024)

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models
by: Wang, Zeqing, et al.
Published: (2025)

IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models
by: Yang, Jiabing, et al.
Published: (2025)

Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation
by: Wang, Zehao, et al.
Published: (2024)

How Texts Help? A Fine-grained Evaluation to Reveal the Role of Language in Vision-Language Tracking
by: Li, Xuchen, et al.
Published: (2024)

AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models
by: Wu, Yuhang, et al.
Published: (2024)

VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
by: Qiu, Haoyi, et al.
Published: (2024)

MM-MoralBench: A MultiModal Moral Evaluation Benchmark for Large Vision-Language Models
by: Yan, Bei, et al.
Published: (2024)

Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation
by: Yu, Hong-Tao, et al.
Published: (2025)

FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models
by: Jing, Liqiang, et al.
Published: (2023)

Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models
by: Zhang, Jinrui, et al.
Published: (2024)

DDFAV: Remote Sensing Large Vision Language Models Dataset and Evaluation Benchmark
by: Li, Haodong, et al.
Published: (2024)

Learning Invariant Causal Mechanism from Vision-Language Models
by: Song, Zeen, et al.
Published: (2024)

Debiased Negative Mining Improves Out-of-distribution Detection with Pre-trained Vision-Language Models
by: Peng, Bo, et al.
Published: (2026)

Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models
by: Wu, Mingrui, et al.
Published: (2024)

Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Large Models
by: Ju, Chen, et al.
Published: (2024)

Causal Tracing of Object Representations in Large Vision Language Models: Mechanistic Interpretability and Hallucination Mitigation
by: Li, Qiming, et al.
Published: (2025)

SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation
by: Du, Hao, et al.
Published: (2025)

Large Vision-Language Models as Emotion Recognizers in Context Awareness
by: Lei, Yuxuan, et al.
Published: (2024)

Instruction-Following Evaluation of Large Vision-Language Models
by: Shiono, Daiki, et al.
Published: (2025)