Resampling Benchmark for Efficient Comprehensive Evaluation of Large Vision-Language Models
Fuente:
arXiv
Guardado en:
| Autores principales: | Suzuki, Teppei, Ozawa, Keisuke |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields
por: Suzuki, Teppei
Publicado: (2023)
por: Suzuki, Teppei
Publicado: (2023)
Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learning
por: Suzuki, Teppei
Publicado: (2024)
por: Suzuki, Teppei
Publicado: (2024)
Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation
por: Yu, Hong-Tao, et al.
Publicado: (2025)
por: Yu, Hong-Tao, et al.
Publicado: (2025)
Evaluating Attribute Comprehension in Large Vision-Language Models
por: Zhang, Haiwen, et al.
Publicado: (2024)
por: Zhang, Haiwen, et al.
Publicado: (2024)
Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling
por: Wu, Tsung-Han, et al.
Publicado: (2025)
por: Wu, Tsung-Han, et al.
Publicado: (2025)
Instruction-Following Evaluation of Large Vision-Language Models
por: Shiono, Daiki, et al.
Publicado: (2025)
por: Shiono, Daiki, et al.
Publicado: (2025)
MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
por: Ying, Kaining, et al.
Publicado: (2024)
por: Ying, Kaining, et al.
Publicado: (2024)
Vision Remember: Recovering Visual Information in Efficient LVLM with Vision Feature Resampling
por: Feng, Ze, et al.
Publicado: (2025)
por: Feng, Ze, et al.
Publicado: (2025)
VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model
por: Wang, Sibo, et al.
Publicado: (2024)
por: Wang, Sibo, et al.
Publicado: (2024)
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
por: Fu, Chaoyou, et al.
Publicado: (2023)
por: Fu, Chaoyou, et al.
Publicado: (2023)
REVAL: A Comprehension Evaluation on Reliability and Values of Large Vision-Language Models
por: Zhang, Jie, et al.
Publicado: (2025)
por: Zhang, Jie, et al.
Publicado: (2025)
Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models
por: Wang, Jin, et al.
Publicado: (2025)
por: Wang, Jin, et al.
Publicado: (2025)
Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning
por: Lu, Fan, et al.
Publicado: (2024)
por: Lu, Fan, et al.
Publicado: (2024)
Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset
por: Chen, Qian, et al.
Publicado: (2026)
por: Chen, Qian, et al.
Publicado: (2026)
DDFAV: Remote Sensing Large Vision Language Models Dataset and Evaluation Benchmark
por: Li, Haodong, et al.
Publicado: (2024)
por: Li, Haodong, et al.
Publicado: (2024)
TinyLVLM-eHub: Towards Comprehensive and Efficient Evaluation for Large Vision-Language Models
por: Shao, Wenqi, et al.
Publicado: (2023)
por: Shao, Wenqi, et al.
Publicado: (2023)
IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models
por: Zhang, Yiming, et al.
Publicado: (2025)
por: Zhang, Yiming, et al.
Publicado: (2025)
VLRMBench: A Comprehensive and Challenging Benchmark for Vision-Language Reward Models
por: Ruan, Jiacheng, et al.
Publicado: (2025)
por: Ruan, Jiacheng, et al.
Publicado: (2025)
XDR-LVLM: An Explainable Vision-Language Large Model for Diabetic Retinopathy Diagnosis
por: Ito, Masato, et al.
Publicado: (2025)
por: Ito, Masato, et al.
Publicado: (2025)
FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models
por: Shu, Dong, et al.
Publicado: (2025)
por: Shu, Dong, et al.
Publicado: (2025)
Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain
por: Luo, Yulin, et al.
Publicado: (2025)
por: Luo, Yulin, et al.
Publicado: (2025)
LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology
por: Qin, Zhenyue, et al.
Publicado: (2025)
por: Qin, Zhenyue, et al.
Publicado: (2025)
ProactiveVideoQA: A Comprehensive Benchmark Evaluating Proactive Interactions in Video Large Language Models
por: Wang, Yueqian, et al.
Publicado: (2025)
por: Wang, Yueqian, et al.
Publicado: (2025)
MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models
por: Xia, Peng, et al.
Publicado: (2024)
por: Xia, Peng, et al.
Publicado: (2024)
EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models
por: Wang, Zekun, et al.
Publicado: (2025)
por: Wang, Zekun, et al.
Publicado: (2025)
MultiVerse: A Multi-Turn Conversation Benchmark for Evaluating Large Vision and Language Models
por: Lee, Young-Jun, et al.
Publicado: (2025)
por: Lee, Young-Jun, et al.
Publicado: (2025)
Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis
por: Zhang, Shengxuming, et al.
Publicado: (2024)
por: Zhang, Shengxuming, et al.
Publicado: (2024)
UWBench: A Comprehensive Vision-Language Benchmark for Underwater Understanding
por: Zhang, Da, et al.
Publicado: (2025)
por: Zhang, Da, et al.
Publicado: (2025)
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?
por: Laskar, Md Tahmid Rahman, et al.
Publicado: (2025)
por: Laskar, Md Tahmid Rahman, et al.
Publicado: (2025)
FPBench: A Comprehensive Benchmark of Multimodal Large Language Models for Fingerprint Analysis
por: Gavas, Ekta, et al.
Publicado: (2025)
por: Gavas, Ekta, et al.
Publicado: (2025)
CELLO: Causal Evaluation of Large Vision-Language Models
por: Chen, Meiqi, et al.
Publicado: (2024)
por: Chen, Meiqi, et al.
Publicado: (2024)
Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models
por: Yan, Bei, et al.
Publicado: (2024)
por: Yan, Bei, et al.
Publicado: (2024)
How Far Have Medical Vision-Language Models Come? A Comprehensive Benchmarking Study
por: Liu, Che, et al.
Publicado: (2025)
por: Liu, Che, et al.
Publicado: (2025)
Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models
por: Atabuzzaman, Md., et al.
Publicado: (2025)
por: Atabuzzaman, Md., et al.
Publicado: (2025)
CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models
por: An, Xiao, et al.
Publicado: (2024)
por: An, Xiao, et al.
Publicado: (2024)
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models
por: Qin, Zhenyue, et al.
Publicado: (2024)
por: Qin, Zhenyue, et al.
Publicado: (2024)
Lost in Space: Probing Fine-grained Spatial Understanding in Vision and Language Resamplers
por: Pantazopoulos, Georgios, et al.
Publicado: (2024)
por: Pantazopoulos, Georgios, et al.
Publicado: (2024)
SPARK: Multi-Vision Sensor Perception and Reasoning Benchmark for Large-scale Vision-Language Models
por: Yu, Youngjoon, et al.
Publicado: (2024)
por: Yu, Youngjoon, et al.
Publicado: (2024)
PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding
por: Kuzucu, Selim, et al.
Publicado: (2026)
por: Kuzucu, Selim, et al.
Publicado: (2026)
Enhancing Large Vision Language Models with Self-Training on Image Comprehension
por: Deng, Yihe, et al.
Publicado: (2024)
por: Deng, Yihe, et al.
Publicado: (2024)
Ejemplares similares
-
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields
por: Suzuki, Teppei
Publicado: (2023) -
Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learning
por: Suzuki, Teppei
Publicado: (2024) -
Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation
por: Yu, Hong-Tao, et al.
Publicado: (2025) -
Evaluating Attribute Comprehension in Large Vision-Language Models
por: Zhang, Haiwen, et al.
Publicado: (2024) -
Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling
por: Wu, Tsung-Han, et al.
Publicado: (2025)