activity
20172024
most citedAIM: Adapting Image Models for Efficient Video Action Recognition

62 citations · 240 across the 28 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

cs.CV2024

Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer

Wenhan Wu, Ce Zheng, Zihao Yang +3

Recently, transformers have demonstrated great potential for modeling long-term dependencies from skeleton sequences and thereby gained ever-increasing attention in skeleton action…

cs.CV20244 cited

ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models

Peiming Li, Ziyi Wang, Mengyuan Liu +2

Grasp generation aims to create complex hand-object interactions with a specified object. While traditional approaches for hand generation have primarily focused on visibility and…

eess.IV2024

IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules

Min Li, Chen Chen, Zhuang Xiong +6

Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potentia…

cs.CV2024

MLP: Motion Label Prior for Temporal Sentence Localization in Untrimmed 3D Human Motions

Sheng Yan, Mengyuan Liu, Yong Wang +3

In this paper, we address the unexplored question of temporal sentence localization in human motions (TSLM), aiming to locate a target moment from a 3D human motion that semantical…

cs.CV2024

Sparse Points to Dense Clouds: Enhancing 3D Detection with Limited LiDAR Data

Aakash Kumar, Chen Chen, Ajmal Mian +2

3D detection is a critical task that enables machines to identify and locate objects in three-dimensional space. It has a broad range of applications in several fields, including a…

cs.CV2024

Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization

Yuhang Li, Xin Dong, Chen Chen +4

Synthetic image data generation represents a promising avenue for training deep learning models, particularly in the realm of transfer learning, where obtaining real images within…