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Your Vision-Language Model Can't Even Count to 20: Exposing the Failures of VLMs in Compositional Counting
Xuyang Guo, Zekai Huang, Zhenmei Shi +2
Vision-Language Models (VLMs) have become a central focus of today's AI community, owing to their impressive abilities gained from training on large-scale vision-language data from…
T2VWorldBench: A Benchmark for Evaluating World Knowledge in Text-to-Video Generation
Yubin Chen, Xuyang Guo, Zhenmei Shi +2
Text-to-video (T2V) models have shown remarkable performance in generating visually reasonable scenes, while their capability to leverage world knowledge for ensuring semantic cons…
T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models
Xuyang Guo, Jiayan Huo, Zhenmei Shi +3
Thanks to recent advancements in scalable deep architectures and large-scale pretraining, text-to-video generation has achieved unprecedented capabilities in producing high-fidelit…
Can You Count to Nine? A Human Evaluation Benchmark for Counting Limits in Modern Text-to-Video Models
Xuyang Guo, Zekai Huang, Jiayan Huo +4
Generative models have driven significant progress in a variety of AI tasks, including text-to-video generation, where models like Video LDM and Stable Video Diffusion can produce…
Text-to-Image Diffusion Models Cannot Count, and Prompt Refinement Cannot Help
Xuyang Guo, Jiayan Huo, Yingyu Liang +4
Generative modeling is widely regarded as one of the most essential problems in today's AI community, with text-to-image generation having gained unprecedented real-world impacts.…