126 citations · 199 across the 11 of their papers we have counts for
12 papers
LIVEditor-14B: Lightning Unified Video Editing via In-Context Sparse Attention
Shitong Shao, Zikai Zhou, Haopeng Li +4
Video editing has evolved toward In-Context Learning (ICL) paradigms, yet the resulting quadratic attention costs create a critical computational bottleneck. In this work, we propo…
Admitting Ignorance Helps the Video Question Answering Models to Answer
Haopeng Li, Tom Drummond, Mingming Gong +2
Significant progress has been made in the field of video question answering (VideoQA) thanks to deep learning and large-scale pretraining. Despite the presence of sophisticated mod…
Multimodal Semantic Communication for Generative Audio-Driven Video Conferencing
Haonan Tong, Haopeng Li, Hongyang Du +3
This paper studies an efficient multimodal data communication scheme for video conferencing. In our considered system, a speaker gives a talk to the audiences, with talking head vi…
Video Semantic Communication with Major Object Extraction and Contextual Video Encoding
Haopeng Li, Haonan Tong, Sihua Wang +3
This paper studies an end-to-end video semantic communication system for massive communication. In the considered system, the transmitter must continuously send the video to the re…
Answering from Sure to Uncertain: Uncertainty-Aware Curriculum Learning for Video Question Answering
Haopeng Li, Mohammed Bennamoun, Jun Liu +2
While significant advancements have been made in video question answering (VideoQA), the potential benefits of enhancing model generalization through tailored difficulty scheduling…
Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports
Haopeng Li, Andong Deng, Jun Liu +5
Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not b…
Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation
Zhitong Xiong, Haopeng Li, Xiao Xiang Zhu
Training semantic segmentation models with few annotated samples has great potential in various real-world applications. For the few-shot segmentation task, the main challenge is h…
Progressive Video Summarization via Multimodal Self-supervised Learning
Li Haopeng, Ke Qiuhong, Gong Mingming +1
Modern video summarization methods are based on deep neural networks that require a large amount of annotated data for training. However, existing datasets for video summarization…
Video Joint Modelling Based on Hierarchical Transformer for Co-summarization
Li Haopeng, Ke Qiuhong, Gong Mingming +1
Video summarization aims to automatically generate a summary (storyboard or video skim) of a video, which can facilitate large-scale video retrieval and browsing. Most of the exist…
Precondition and Effect Reasoning for Action Recognition
Yoo Hongsang, Li Haopeng, Ke Qiuhong +2
Human action recognition has drawn a lot of attention in the recent years due to the research and application significance. Most existing works on action recognition focus on learn…
Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
Haopeng Li, Lingbo Liu, Kunlin Yang +5
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos. To model spatial-temporal dependencie…
Reconstructive Sequence-Graph Network for Video Summarization
Bin Zhao, Haopeng Li, Xiaoqiang Lu +1
Exploiting the inner-shot and inter-shot dependencies is essential for key-shot based video summarization. Current approaches mainly devote to modeling the video as a frame sequenc…