206 citations · 707 across the 16 of their papers we have counts for
16 papers
Visually Guided Generative Text-Layout Pre-training for Document Intelligence
Zhiming Mao, Haoli Bai, Lu Hou +4
Prior study shows that pre-training techniques can boost the performance of visual document understanding (VDU), which typically requires models to gain abilities to perceive and r…
Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding
Haoli Bai, Zhiguang Liu, Xiaojun Meng +9
Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-tr…
LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling
Dongsheng Chen, Chaofan Tao, Lu Hou +3
Recent large-scale video-language pre-trained models have shown appealing performance on various downstream tasks. However, the pre-training process is computationally expensive du…
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation
Wenliang Dai, Lu Hou, Lifeng Shang +3
The recent large-scale vision-language pre-training (VLP) of dual-stream architectures (e.g., CLIP) with a tremendous amount of image-text pair data, has shown its superiority on v…
Compression of Generative Pre-trained Language Models via Quantization
Chaofan Tao, Lu Hou, Wei Zhang +5
The increasing size of generative Pre-trained Language Models (PLMs) has greatly increased the demand for model compression. Despite various methods to compress BERT or its variant…
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
Jiaxi Gu, Xiaojun Meng, Guansong Lu +9
Vision-Language Pre-training (VLP) models have shown remarkable performance on various downstream tasks. Their success heavily relies on the scale of pre-trained cross-modal datase…