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20172023
most citedRethinking Feature Discrimination and Polymerization for Large-scale Recognition

112 citations · 400 across the 20 of their papers we have counts for

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30 papers · 1 filter

cs.CV20237 cited

Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising

Fu-Yun Wang, Wenshuo Chen, Guanglu Song +3

Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and edi…

cs.CV2023

RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths

Zeyue Xue, Guanglu Song, Qiushan Guo +4

Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images,…

cs.CV20231 cited

Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction

Zhuofan Zong, Dongzhi Jiang, Guanglu Song +4

In this paper, we propose a new paradigm, named Historical Object Prediction (HoP) for multi-view 3D detection to leverage temporal information more effectively. The HoP approach i…

cs.CV2023

GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding

Jihao Liu, Tai Wang, Boxiao Liu +3

Multi-view camera-based 3D detection is a challenging problem in computer vision. Recent works leverage a pretrained LiDAR detection model to transfer knowledge to a camera-based s…

cs.CV2022

Teach-DETR: Better Training DETR with Teachers

Linjiang Huang, Kaixin Lu, Guanglu Song +4

In this paper, we present a novel training scheme, namely Teach-DETR, to learn better DETR-based detectors from versatile teacher detectors. We show that the predicted boxes from t…

cs.CV20222 cited

Large-batch Optimization for Dense Visual Predictions

Zeyue Xue, Jianming Liang, Guanglu Song +4

Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tac…