51 citations · 82 across the 4 of their papers we have counts for
8 papers
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks
Zhecan Wang, Noel Codella, Yen-Chun Chen +8
Cross-modal encoders for vision-language (VL) tasks are often pretrained with carefully curated vision-language datasets. While these datasets reach an order of 10 million samples,…
Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models
Jize Cao, Zhe Gan, Yu Cheng +3
Recent Transformer-based large-scale pre-trained models have revolutionized vision-and-language (V+L) research. Models such as ViLBERT, LXMERT and UNITER have significantly lifted…
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
Linjie Li, Yen-Chun Chen, Yu Cheng +3
We present HERO, a novel framework for large-scale video+language omni-representation learning. HERO encodes multimodal inputs in a hierarchical structure, where local context of a…
Distilling Knowledge Learned in BERT for Text Generation
Yen-Chun Chen, Zhe Gan, Yu Cheng +2
Large-scale pre-trained language model such as BERT has achieved great success in language understanding tasks. However, it remains an open question how to utilize BERT for languag…
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Yizhe Zhang, Siqi Sun, Michel Galley +6
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges ext…
UNITER: UNiversal Image-TExt Representation Learning
Yen-Chun Chen, Linjie Li, Licheng Yu +5
Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understandi…