42 citations · 144 across the 13 of their papers we have counts for
25 papers
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
Bing Su, Dazhao Du, Zhao Yang +6
Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive abi…
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval
Haoyu Lu, Nanyi Fei, Yuqi Huo +3
Large-scale single-stream pre-training has shown dramatic performance in image-text retrieval. Regrettably, it faces low inference efficiency due to heavy attention layers. Recentl…
HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers
Mingyu Ding, Xiaochen Lian, Linjie Yang +4
High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored…
Pre-Trained Models: Past, Present and Future
Xu Han, Zhengyan Zhang, Ning Ding +21
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophis…
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
Yuqi Huo, Manli Zhang, Guangzhen Liu +32
Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction bet…
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning
Yizhao Gao, Nanyi Fei, Guangzhen Liu +3
Most recent few-shot learning (FSL) methods are based on meta-learning with episodic training. In each meta-training episode, a discriminative feature embedding and/or classifier a…