29 citations · 57 across the 15 of their papers we have counts for
15 papers
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering
Weixi Weng, Jieming Zhu, Xiaojun Meng +3
Multimodal large language models (MLLMs) have demonstrated great performance on visual question answering (VQA). When it comes to knowledge-based Visual Question Answering (KB-VQA)…
End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling
Jianxin Liang, Xiaojun Meng, Yueqian Wang +3
Video Question Answering (VideoQA) has emerged as a challenging frontier in the field of multimedia processing, requiring intricate interactions between visual and textual modaliti…
Prompt-Based Length Controlled Generation with Multiple Control Types
Renlong Jie, Xiaojun Meng, Lifeng Shang +2
Large language models (LLMs) have attracted great attention given their strong performance on a wide range of NLP tasks. In practice, users often expect generated texts to fall wit…
HawkEye: Training Video-Text LLMs for Grounding Text in Videos
Yueqian Wang, Xiaojun Meng, Jianxin Liang +3
Video-text Large Language Models (video-text LLMs) have shown remarkable performance in answering questions and holding conversations on simple videos. However, they perform almost…
Unsupervised Extractive Summarization with Learnable Length Control Strategies
Renlong Jie, Xiaojun Meng, Xin Jiang +1
Unsupervised extractive summarization is an important technique in information extraction and retrieval. Compared with supervised method, it does not require high-quality human-lab…
Prompt-Based Length Controlled Generation with Reinforcement Learning
Renlong Jie, Xiaojun Meng, Lifeng Shang +2
Large language models (LLMs) like ChatGPT and GPT-4 have attracted great attention given their surprising performance on a wide range of NLP tasks. Length controlled generation of…
Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video
Zenan Xu, Xiaojun Meng, Yasheng Wang +4
Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our…
Enhancing Coherence of Extractive Summarization with Multitask Learning
Renlong Jie, Xiaojun Meng, Lifeng Shang +2
This study proposes a multitask learning architecture for extractive summarization with coherence boosting. The architecture contains an extractive summarizer and coherent discrimi…
Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding
Haoli Bai, Zhiguang Liu, Xiaojun Meng +9
Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-tr…
Lexicon-injected Semantic Parsing for Task-Oriented Dialog
Xiaojun Meng, Wenlin Dai, Yasheng Wang +4
Recently, semantic parsing using hierarchical representations for dialog systems has captured substantial attention. Task-Oriented Parse (TOP), a tree representation with intents a…
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal Information
Fanchao Qi, Chuancheng Lv, Zhiyuan Liu +3
In linguistics, a sememe is defined as the minimum semantic unit of languages. Sememe knowledge bases (KBs), which are built by manually annotating words with sememes, have been su…
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks
Zhengkun Zhang, Wenya Guo, Xiaojun Meng +5
The workflow of pretraining and fine-tuning has emerged as a popular paradigm for solving various NLP and V&L (Vision-and-Language) downstream tasks. With the capacity of pretraine…
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
Jiaxi Gu, Xiaojun Meng, Guansong Lu +9
Vision-Language Pre-training (VLP) models have shown remarkable performance on various downstream tasks. Their success heavily relies on the scale of pre-trained cross-modal datase…
UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation
Zhengkun Zhang, Xiaojun Meng, Yasheng Wang +3
With the rapid increase of multimedia data, a large body of literature has emerged to work on multimodal summarization, the majority of which target at refining salient information…
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations
Yuan Zang, Bairu Hou, Fanchao Qi +3
Adversarial attacking aims to fool deep neural networks with adversarial examples. In the field of natural language processing, various textual adversarial attack models have been…