153 citations · 525 across the 32 of their papers we have counts for
8 papers · 1 filter
FlowZero: Zero-Shot Text-to-Video Synthesis with LLM-Driven Dynamic Scene Syntax
Yu Lu, Linchao Zhu, Hehe Fan +1
Text-to-video (T2V) generation is a rapidly growing research area that aims to translate the scenes, objects, and actions within complex video text into a sequence of coherent visu…
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos
Xiaoxiao Sheng, Zhiqiang Shen, Gang Xiao +3
We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Previous methods commonly conduct representation learning at th…
Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos
Zhiqiang Shen, Xiaoxiao Sheng, Hehe Fan +5
Recently, the community has made tremendous progress in developing effective methods for point cloud video understanding that learn from massive amounts of labeled data. However, a…
DPMix: Mixture of Depth and Point Cloud Video Experts for 4D Action Segmentation
Yue Zhang, Hehe Fan, Yi Yang +1
In this technical report, we present our findings from the research conducted on the Human-Object Interaction 4D (HOI4D) dataset for egocentric action segmentation task. As a relat…
Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question Answering
Yi Cheng, Hehe Fan, Dongyun Lin +3
The main challenge in video question answering (VideoQA) is to capture and understand the complex spatial and temporal relations between objects based on given questions. Existing…
A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023
Yi Cheng, Ziwei Xu, Fen Fang +5
In this technical report, we present our findings from a study conducted on the EPIC-KITCHENS-100 Unsupervised Domain Adaptation task for Action Recognition. Our research focuses o…
Text to Point Cloud Localization with Relation-Enhanced Transformer
Guangzhi Wang, Hehe Fan, Mohan Kankanhalli
Automatically localizing a position based on a few natural language instructions is essential for future robots to communicate and collaborate with humans. To approach this goal, w…
STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition
Ming Li, Xiangyu Xu, Hehe Fan +7
Existing methods of privacy-preserving action recognition (PPAR) mainly focus on frame-level (spatial) privacy removal through 2D CNNs. Unfortunately, they have two major drawbacks…