150 citations · 314 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…