1 citations · 3 across the 7 of their papers we have counts for
7 papers
Large Language Models have Chain-of-Affect
Junjie Xu, Xingjiao Wu, Luwei Xiao +11
As large language models (LLMs) move into persistent, user-facing roles, their behavior must be understood not as isolated responses but as a trajectory unfolding over sustained in…
Minutes to Seconds: Speeded-up DDPM-based Image Inpainting with Coarse-to-Fine Sampling
Lintao Zhang, Xiangcheng Du, LeoWu TomyEnrique +3
For image inpainting, the existing Denoising Diffusion Probabilistic Model (DDPM) based method i.e. RePaint can produce high-quality images for any inpainting form. It utilizes a p…
Fine-Grained Scene Image Classification with Modality-Agnostic Adapter
Yiqun Wang, Zhao Zhou, Xiangcheng Du +3
When dealing with the task of fine-grained scene image classification, most previous works lay much emphasis on global visual features when doing multi-modal feature fusion. In oth…
UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images
Weijie Li, Yitian Wan, Xingjiao Wu +3
With the increasing prevalence of smartphones and websites, Image Aesthetic Assessment (IAA) has become increasingly crucial. While the significance of attributes in IAA is widely…
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
Anran Wu, Luwei Xiao, Xingjiao Wu +6
Visually-situated languages such as charts and plots are omnipresent in real-world documents. These graphical depictions are human-readable and are often analyzed in visually-rich…
Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering
Shuwen Yang, Anran Wu, Xingjiao Wu +4
Pre-trained multimodal models have achieved significant success in retrieval-based question answering. However, current multimodal retrieval question-answering models face two main…
Cross-Domain Document Layout Analysis Using Document Style Guide
Xingjiao Wu, Luwei Xiao, Xiangcheng Du +5
The document layout analysis (DLA) aims to decompose document images into high-level semantic areas (i.e., figures, tables, texts, and background). Creating a DLA framework with st…