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cs.CV2024

Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability,Reproducibility, and Practicality

Tianle Zhang, Langtian Ma, Yuchen Yan +9

Recent text-to-video (T2V) technology advancements, as demonstrated by models such as Gen2, Pika, and Sora, have significantly broadened its applicability and popularity. Despite t…

cs.CV2024

PhyBench: A Physical Commonsense Benchmark for Evaluating Text-to-Image Models

Fanqing Meng, Wenqi Shao, Lixin Luo +8

Text-to-image (T2I) models have made substantial progress in generating images from textual prompts. However, they frequently fail to produce images consistent with physical common…

cs.CY2024

Position: Towards Implicit Prompt For Text-To-Image Models

Yue Yang, Yuqi Lin, Hong Liu +7

Recent text-to-image (T2I) models have had great success, and many benchmarks have been proposed to evaluate their performance and safety. However, they only consider explicit prom…

cs.MM2024

ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models

Shuo Liu, Kaining Ying, Hao Zhang +8

This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess indivi…

cs.CV2024

MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

Kaining Ying, Fanqing Meng, Jin Wang +19

Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation. However, existing multimod…