activity
20172025
most citedMeta-SGD: Learning to Learn Quickly for Few-Shot Learning

849 citations · 1.4k across the 49 of their papers we have counts for

collaborators

62 papers

cs.CV20222 cited

Generative Negative Text Replay for Continual Vision-Language Pretraining

Shipeng Yan, Lanqing Hong, Hang Xu +4

Vision-language pre-training (VLP) has attracted increasing attention recently. With a large amount of image-text pairs, VLP models trained with contrastive loss have achieved impr…

cs.LG20225 cited

ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization

Qishi Dong, Awais Muhammad, Fengwei Zhou +5

Recent advances on large-scale pre-training have shown great potentials of leveraging a large set of Pre-Trained Models (PTMs) for improving Out-of-Distribution (OoD) generalizatio…

cs.CV20221 cited

Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving

Longhui Yu, Yifan Zhang, Lanqing Hong +2

Object detection for autonomous vehicles has received increasing attention in recent years, where labeled data are often expensive while unlabeled data can be collected readily, ca…

cs.CV202264 cited

DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection

Lewei Yao, Jianhua Han, Youpeng Wen +6

Open-world object detection, as a more general and challenging goal, aims to recognize and localize objects described by arbitrary category names. The recent work GLIP formulates t…

cs.CV202237 cited

CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds

Haiyang Wang, Lihe Ding, Shaocong Dong +5

We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by levera…

cs.CV2022

DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction

Kaichen Zhou, Lanqing Hong, Changhao Chen +4

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames. However, they neither fully…