activity
20212023
most citedPromptMagician: Interactive Prompt Engineering for Text-to-Image Creation

124 citations · 140 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2023★ 124 cited

PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation

Yingchaojie Feng, Xingbo Wang, Kam Kwai Wong +5

Generative text-to-image models have gained great popularity among the public for their powerful capability to generate high-quality images based on natural language prompts. Howev…

cs.CL2023

Benchmarking Diverse-Modal Entity Linking with Generative Models

Sijia Wang, Alexander Hanbo Li, Henry Zhu +9

Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables. While existing entity linking (EL) models work well on per modality…

cs.CL2023★ 2 cited

AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes

Barry Menglong Yao, Sijia Wang, Yu Chen +5

We propose attribute-aware multimodal entity linking, where the input consists of a mention described with a text paragraph and images, and the goal is to predict the corresponding…

cs.CL2023

RE: Region-Aware Relation Extraction from Visually Rich Documents

Pritika Ramu, Sijia Wang, Lalla Mouatadid +2

Current research in form understanding predominantly relies on large pre-trained language models, necessitating extensive data for pre-training. However, the importance of layout s…

cs.CV2022★ 1 cited

Toward Sustainable Continual Learning: Detection and Knowledge Repurposing of Similar Tasks

Sijia Wang, Yoojin Choi, Junya Chen +2

Most existing works on continual learning (CL) focus on overcoming the catastrophic forgetting (CF) problem, with dynamic models and replay methods performing exceptionally well. H…

cs.CL2022★ 1 cited

The Art of Prompting: Event Detection based on Type Specific Prompts

Sijia Wang, Mo Yu, Lifu Huang

We compare various forms of prompts to represent event types and develop a unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot…

cs.CL2021★ 12 cited

Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding

Sijia Wang, Mo Yu, Shiyu Chang +2

Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols. These approaches are usually limit…