150 citations · 314 across the 13 of their papers we have counts for
13 papers
Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models
Munan Ning, Bin Zhu, Yujia Xie +5
Video-based large language models (Video-LLMs) have been recently introduced, targeting both fundamental improvements in perception and comprehension, and a diverse range of user i…
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Yung-Sung Chuang, Yujia Xie, Hongyin Luo +3
Despite their impressive capabilities, large language models (LLMs) are prone to hallucinations, i.e., generating content that deviates from facts seen during pretraining. We propo…
Interactive Editing for Text Summarization
Yujia Xie, Xun Wang, Si-Qing Chen +2
Summarizing lengthy documents is a common and essential task in our daily lives. Although recent advancements in neural summarization models can assist in crafting general-purpose…
Album Storytelling with Iterative Story-aware Captioning and Large Language Models
Munan Ning, Yujia Xie, Dongdong Chen +5
This work studies how to transform an album to vivid and coherent stories, a task we refer to as "album storytelling". While this task can help preserve memories and facilitate exp…
Summarization with Precise Length Control
Lesly Miculicich, Yujia Xie, Song Wang +1
Many applications of text generation such as summarization benefit from accurately controlling the text length. Existing approaches on length-controlled summarization either result…
Personalized Abstractive Summarization by Tri-agent Generation Pipeline
Wen Xiao, Yujia Xie, Giuseppe Carenini +1
Tailoring outputs from large language models, like ChatGPT, to implicit user preferences remains a challenge despite their impressive generative capabilities. In this paper, we pro…
Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
Baolin Peng, Michel Galley, Pengcheng He +8
Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. Howev…
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation
Munan Ning, Donghuan Lu, Yujia Xie +6
Unsupervised domain adaption has been widely adopted in tasks with scarce annotated data. Unfortunately, mapping the target-domain distribution to the source-domain unconditionally…
Look Before You Match: Instance Understanding Matters in Video Object Segmentation
Junke Wang, Dongdong Chen, Zuxuan Wu +7
Exploring dense matching between the current frame and past frames for long-range context modeling, memory-based methods have demonstrated impressive results in video object segmen…
Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles
Shuquan Ye, Yujia Xie, Dongdong Chen +4
This paper focuses on analyzing and improving the commonsense ability of recent popular vision-language (VL) models. Despite the great success, we observe that existing VL-models s…
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks
Junke Wang, Dongdong Chen, Zuxuan Wu +7
This paper presents OmniVL, a new foundation model to support both image-language and video-language tasks using one universal architecture. It adopts a unified transformer-based v…
Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning
Yujia Xie, Luowei Zhou, Xiyang Dai +4
People say, "A picture is worth a thousand words". Then how can we get the rich information out of the image? We argue that by using visual clues to bridge large pretrained vision…
REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering
Yuanze Lin, Yujia Xie, Dongdong Chen +3
This paper revisits visual representation in knowledge-based visual question answering (VQA) and demonstrates that using regional information in a better way can significantly impr…