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20222026
most citedMMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

34 citations · 61 across the 13 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.CV2024

VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation

Weiming Ren, Huan Yang, Jie Min +2

Current large multimodal models (LMMs) face significant challenges in processing and comprehending long-duration or high-resolution videos, which is mainly due to the lack of high-…

cs.CV2024

OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision

Cong Wei, Zheyang Xiong, Weiming Ren +3

Instruction-guided image editing methods have demonstrated significant potential by training diffusion models on automatically synthesized or manually annotated image editing pairs…

cs.CL2024★ 34 cited

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Yubo Wang, Xueguang Ma, Ge Zhang +14

In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in…

cs.CV2024★ 1 cited

Video Diffusion Models: A Survey

Andrew Melnik, Michal Ljubljanac, Cong Lu +3

Diffusion generative models have recently become a powerful technique for creating and modifying high-quality, coherent video content. This survey provides a comprehensive overview…

cs.CV2024★ 2 cited

AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks

Max Ku, Cong Wei, Weiming Ren +2

In the dynamic field of digital content creation using generative models, state-of-the-art video editing models still do not offer the level of quality and control that users desir…

cs.CV2024

Towards Artwork Explanation in Large-scale Vision Language Models

Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2

Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…

cs.CL2024★ 1 cited

StructLM: Towards Building Generalist Models for Structured Knowledge Grounding

Alex Zhuang, Ge Zhang, Tianyu Zheng +7

Structured data sources, such as tables, graphs, and databases, are ubiquitous knowledge sources. Despite the demonstrated capabilities of large language models (LLMs) on plain tex…

cs.CV2024★ 4 cited

ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation

Weiming Ren, Huan Yang, Ge Zhang +4

Image-to-video (I2V) generation aims to use the initial frame (alongside a text prompt) to create a video sequence. A grand challenge in I2V generation is to maintain visual consis…