76 citations · 179 across the 18 of their papers we have counts for
23 papers
BUS:Efficient and Effective Vision-language Pre-training with Bottom-Up Patch Summarization
Chaoya Jiang, Haiyang Xu, Wei Ye +7
Vision Transformer (ViT) based Vision-Language Pre-training (VLP) models have demonstrated impressive performance in various tasks. However, the lengthy visual token sequences fed…
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition
Jianing Wang, Chengcheng Han, Chengyu Wang +5
Few-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data. Previous methods solve this problem based on token-wise classification, whi…
Parameter-Efficient Sparsity for Large Language Models Fine-Tuning
Yuchao Li, Fuli Luo, Chuanqi Tan +4
With the dramatically increased number of parameters in language models, sparsity methods have received ever-increasing research focus to compress and accelerate the models. While…
Towards Unified Prompt Tuning for Few-shot Text Classification
Jianing Wang, Chengyu Wang, Fuli Luo +6
Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts. Yet, PLMs are unfamil…
Image Captioning In the Transformer Age
Yang Xu, Li Li, Haiyang Xu +3
Image Captioning (IC) has achieved astonishing developments by incorporating various techniques into the CNN-RNN encoder-decoder architecture. However, since CNN and RNN do not sha…
Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency
Yanyang Li, Fuli Luo, Runxin Xu +3
Structured pruning has been extensively studied on monolingual pre-trained language models and is yet to be fully evaluated on their multilingual counterparts. This work investigat…