10 citations · 16 across the 15 of their papers we have counts for
6 papers · 1 filter
MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation
Haibo Tong, Zhaoyang Wang, Zhaorun Chen +11
Recent advancements in video generation have significantly improved the ability to synthesize videos from text instructions. However, existing models still struggle with key challe…
SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations
Zhaorun Chen, Francesco Pinto, Minzhou Pan +1
With the rise of generative AI and rapid growth of high-quality video generation, video guardrails have become more crucial than ever to ensure safety and security across platforms…
MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models
Peng Xia, Siwei Han, Shi Qiu +9
Interleaved multimodal comprehension and generation, enabling models to produce and interpret both images and text in arbitrary sequences, have become a pivotal area in multimodal…
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
Zhaorun Chen, Yichao Du, Zichen Wen +16
While text-to-image models like DALLE-3 and Stable Diffusion are rapidly proliferating, they often encounter challenges such as hallucination, bias, and the production of unsafe, l…
RankCLIP: Ranking-Consistent Language-Image Pretraining
Yiming Zhang, Zhuokai Zhao, Zhaorun Chen +3
Self-supervised contrastive learning models, such as CLIP, have set new benchmarks for vision-language models in many downstream tasks. However, their dependency on rigid one-to-on…
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
Zhaorun Chen, Zhuokai Zhao, Hongyin Luo +3
While large vision-language models (LVLMs) have demonstrated impressive capabilities in interpreting multi-modal contexts, they invariably suffer from object hallucinations (OH). W…