11 citations · 32 across the 8 of their papers we have counts for
8 papers
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning
Mingyu Zheng, Zhifan Feng, Jia Wang +4
Despite the commendable progress of recent LLM-based data synthesis methods, they face two limitations in generating table instruction tuning data. First, they can not thoroughly e…
Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation
Chen Liang, Zhifan Feng, Zihe Liu +4
Chain-of-thought prompting significantly boosts the reasoning ability of large language models but still faces three issues: hallucination problem, restricted interpretability, and…
Improving Video Retrieval by Adaptive Margin
Feng He, Qi Wang, Zhifan Feng +4
Video retrieval is becoming increasingly important owing to the rapid emergence of videos on the Internet. The dominant paradigm for video retrieval learns video-text representatio…
CLOP: Video-and-Language Pre-Training with Knowledge Regularizations
Guohao Li, Hu Yang, Feng He +4
Video-and-language pre-training has shown promising results for learning generalizable representations. Most existing approaches usually model video and text in an implicit manner,…
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance
Wei Li, Xue Xu, Xinyan Xiao +8
Diffusion generative models have recently greatly improved the power of text-conditioned image generation. Existing image generation models mainly include text conditional diffusio…
A CLIP-Enhanced Method for Video-Language Understanding
Guohao Li, Feng He, Zhifan Feng
This technical report summarizes our method for the Video-And-Language Understanding Evaluation (VALUE) challenge (https://value-benchmark.github.io/challenge\_2021.html). We propo…
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Xianpei Han, Zhichun Wang, Jiangtao Zhang +22
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation…