activity
20142024
most citedDeep Learning Representation using Autoencoder for 3D Shape Retrieval

8 citations · 21 across the 15 of their papers we have counts for

collaborators

15 papers

cs.CV20241 cited

PVUW 2024 Challenge on Complex Video Understanding: Methods and Results

Henghui Ding, Chang Liu, Yunchao Wei +34

Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentat…

cs.CV20241 cited

DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data

Qihao Liu, Yi Zhang, Song Bai +2

We present DIRECT-3D, a diffusion-based 3D generative model for creating high-quality 3D assets (represented by Neural Radiance Fields) from text prompts. Unlike recent 3D generati…

cs.CV20241 cited

Debiasing Text-to-Image Diffusion Models

Ruifei He, Chuhui Xue, Haoru Tan +4

Learning-based Text-to-Image (TTI) models like Stable Diffusion have revolutionized the way visual content is generated in various domains. However, recent research has shown that…

cs.AI20243 cited

Progress and Prospects in 3D Generative AI: A Technical Overview including 3D human

Song Bai, Jie Li

While AI-generated text and 2D images continue to expand its territory, 3D generation has gradually emerged as a trend that cannot be ignored. Since the year 2023 an abundant amoun…

cs.CV20232 cited

Dataset Condensation via Generative Model

David Junhao Zhang, Heng Wang, Chuhui Xue +4

Dataset condensation aims to condense a large dataset with a lot of training samples into a small set. Previous methods usually condense the dataset into the pixels format. However…

cs.CV2023

Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks

David Junhao Zhang, Mutian Xu, Chuhui Xue +4

Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additiona…