62 citations · 80 across the 16 of their papers we have counts for
16 papers
A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding
Yue Zhang, Liqiang Jing, Jia Li +4
Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos remains limited. Existing approa…
FIHA: Autonomous Hallucination Evaluation in Vision-Language Models with Davidson Scene Graphs
Bowen Yan, Zhengsong Zhang, Liqiang Jing +2
The rapid development of Large Vision-Language Models (LVLMs) often comes with widespread hallucination issues, making cost-effective and comprehensive assessments increasingly vit…
DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?
Liqiang Jing, Zhehui Huang, Xiaoyang Wang +6
Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents…
FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback
Liqiang Jing, Xinya Du
Large Vision-Language Models (LVLMs) have demonstrated proficiency in tackling a variety of visual-language tasks. However, current LVLMs suffer from misalignment between text and…
Multimodal Interaction Modeling via Self-Supervised Multi-Task Learning for Review Helpfulness Prediction
HongLin Gong, Mengzhao Jia, Liqiang Jing
In line with the latest research, the task of identifying helpful reviews from a vast pool of user-generated textual and visual data has become a prominent area of study. Effective…
Fine-grained and Explainable Factuality Evaluation for Multimodal Summarization
Yue Zhang, Jingxuan Zuo, Ke Su +1
Multimodal summarization aims to generate a concise summary based on the input text and image. However, the existing methods potentially suffer from unfactual output. To evaluate t…
Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue
Kun Ouyang, Liqiang Jing, Xuemeng Song +3
Sarcasm Explanation in Dialogue (SED) is a new yet challenging task, which aims to generate a natural language explanation for the given sarcastic dialogue that involves multiple m…
Debiasing Multimodal Sarcasm Detection with Contrastive Learning
Mengzhao Jia, Can Xie, Liqiang Jing
Despite commendable achievements made by existing work, prevailing multimodal sarcasm detection studies rely more on textual content over visual information. It unavoidably induces…
VK-G2T: Vision and Context Knowledge enhanced Gloss2Text
Liqiang Jing, Xuemeng Song, Xinxing Zu +3
Existing sign language translation methods follow a two-stage pipeline: first converting the sign language video to a gloss sequence (i.e. Sign2Gloss) and then translating the gene…
FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models
Liqiang Jing, Ruosen Li, Yunmo Chen +1
We introduce FaithScore (Faithfulness to Atomic Image Facts Score), a reference-free and fine-grained evaluation metric that measures the faithfulness of the generated free-form an…
Knowledge-enhanced Memory Model for Emotional Support Conversation
Mengzhao Jia, Qianglong Chen, Liqiang Jing +2
The prevalence of mental disorders has become a significant issue, leading to the increased focus on Emotional Support Conversation as an effective supplement for mental health sup…
General Debiasing for Multimodal Sentiment Analysis
Teng Sun, Juntong Ni, Wenjie Wang +3
Existing work on Multimodal Sentiment Analysis (MSA) utilizes multimodal information for prediction yet unavoidably suffers from fitting the spurious correlations between multimoda…
Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation
Liqiang Jing, Xuemeng Song, Kun Ouyang +2
Multimodal Sarcasm Explanation (MuSE) is a new yet challenging task, which aims to generate a natural language sentence for a multimodal social post (an image as well as its captio…
Stylized Data-to-Text Generation: A Case Study in the E-Commerce Domain
Liqiang Jing, Xuemeng Song, Xuming Lin +3
Existing data-to-text generation efforts mainly focus on generating a coherent text from non-linguistic input data, such as tables and attribute-value pairs, but overlook that diff…
Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis
Teng Sun, Wenjie Wang, Liqiang Jing +3
Existing studies on multimodal sentiment analysis heavily rely on textual modality and unavoidably induce the spurious correlations between textual words and sentiment labels. This…
Multimodal Dialog Systems with Dual Knowledge-enhanced Generative Pretrained Language Model
Xiaolin Chen, Xuemeng Song, Liqiang Jing +3
Text response generation for multimodal task-oriented dialog systems, which aims to generate the proper text response given the multimodal context, is an essential yet challenging…