258 citations · 406 across the 8 of their papers we have counts for
8 papers
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
An Yang, Junshu Pan, Junyang Lin +4
The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining. In this work, we construct…
Prompt Tuning for Generative Multimodal Pretrained Models
Hao Yang, Junyang Lin, An Yang +3
Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural language pretraining and even vision pretraining. In this work, we explore the t…
Instance-wise Prompt Tuning for Pretrained Language Models
Yuezihan Jiang, Hao Yang, Junyang Lin +6
Prompt Learning has recently gained great popularity in bridging the gap between pretraining tasks and various downstream tasks. It freezes Pretrained Language Models (PLMs) and on…
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Peng Wang, An Yang, Rui Men +7
In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization. We propose OFA, a Task-Agnostic and Mo…
Learning Relation Alignment for Calibrated Cross-modal Retrieval
Shuhuai Ren, Junyang Lin, Guangxiang Zhao +5
Despite the achievements of large-scale multimodal pre-training approaches, cross-modal retrieval, e.g., image-text retrieval, remains a challenging task. To bridge the semantic ga…
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation
Peng Wang, Junyang Lin, An Yang +4
Table-to-text generation refers to generating a descriptive text from a key-value table. Traditional autoregressive methods, though can generate text with high fluency, suffer from…
M6-T: Exploring Sparse Expert Models and Beyond
An Yang, Junyang Lin, Rui Men +12
Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scal…
InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining
Junyang Lin, An Yang, Yichang Zhang +3
Multi-modal pretraining for learning high-level multi-modal representation is a further step towards deep learning and artificial intelligence. In this work, we propose a novel mod…