27 citations · 94 across the 16 of their papers we have counts for
16 papers
Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks
Chaojie Wang, Xinyang Liu, Dongsheng Wang +3
Although existing variational graph autoencoders (VGAEs) have been widely used for modeling and generating graph-structured data, most of them are still not flexible enough to appr…
Instruction Tuning-free Visual Token Complement for Multimodal LLMs
Dongsheng Wang, Jiequan Cui, Miaoge Li +3
As the open community of large language models (LLMs) matures, multimodal LLMs (MLLMs) have promised an elegant bridge between vision and language. However, current research is inh…
DocGraphLM: Documental Graph Language Model for Information Extraction
Dongsheng Wang, Zhiqiang Ma, Armineh Nourbakhsh +2
Advances in Visually Rich Document Understanding (VrDU) have enabled information extraction and question answering over documents with complex layouts. Two tropes of architectures…
Tuning Multi-mode Token-level Prompt Alignment across Modalities
Dongsheng Wang, Miaoge Li, Xinyang Liu +3
Advancements in prompt tuning of vision-language models have underscored their potential in enhancing open-world visual concept comprehension. However, prior works only primarily f…
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
Ruiying Lu, YuJie Wu, Long Tian +4
Unsupervised image Anomaly Detection (UAD) aims to learn robust and discriminative representations of normal samples. While separate solutions per class endow expensive computation…
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
Miaoge Li, Dongsheng Wang, Xinyang Liu +4
Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent spac…
REFinD: Relation Extraction Financial Dataset
Simerjot Kaur, Charese Smiley, Akshat Gupta +5
A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailme…
Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation
Dongsheng Wang, Xu Jia, Yang Zhang +5
Event-based cameras are bio-inspired sensors that capture brightness change of every pixel in an asynchronous manner. Compared with frame-based sensors, event cameras have microsec…
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing
Zequn Zeng, Hao Zhang, Zhengjue Wang +3
Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing o…
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models
Xinyang Liu, Dongsheng Wang, Bowei Fang +5
For downstream applications of vision-language pre-trained models, there has been significant interest in constructing effective prompts. Existing works on prompt engineering, whic…
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
Yishi Xu, Dongsheng Wang, Bo Chen +3
Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, l…
Knowledge-Aware Bayesian Deep Topic Model
Dongsheng Wang, Yishi Xu, Miaoge Li +4
We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling. Although embedded topic models (ETMs) and its variants have gained…
Ordinal Graph Gamma Belief Network for Social Recommender Systems
Dongsheng Wang, Chaojie Wang, Bo Chen +1
To build recommender systems that not only consider user-item interactions represented as ordinal variables, but also exploit the social network describing the relationships betwee…
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
Dongsheng Wang, Dandan Guo, He Zhao +4
A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is…
TopicNet: Semantic Graph-Guided Topic Discovery
Zhibin Duan, Yishi Xu, Bo Chen +3
Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic…
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
Zhibin Duan, Dongsheng Wang, Bo Chen +5
Hierarchical topic models such as the gamma belief network (GBN) have delivered promising results in mining multi-layer document representations and discovering interpretable topic…