128 citations · 197 across the 19 of their papers we have counts for
19 papers
UniG3D: A Unified 3D Object Generation Dataset
Qinghong Sun, Yangguang Li, ZeXiang Liu +5
The field of generative AI has a transformative impact on various areas, including virtual reality, autonomous driving, the metaverse, gaming, and robotics. Among these application…
Mask Hierarchical Features For Self-Supervised Learning
Fenggang Liu, Yangguang Li, Feng Liang +3
This paper shows that Masking the Deep hierarchical features is an efficient self-supervised method, denoted as MaskDeep. MaskDeep treats each patch in the representation space as…
Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View Perception
Bin Huang, Yangguang Li, Enze Xie +7
Recently, the pure camera-based Bird's-Eye-View (BEV) perception removes expensive Lidar sensors, making it a feasible solution for economical autonomous driving. However, most exi…
Parallel Reasoning Network for Human-Object Interaction Detection
Huan Peng, Fenggang Liu, Yangguang Li +4
Human-Object Interaction (HOI) detection aims to learn how human interacts with surrounding objects. Previous HOI detection frameworks simultaneously detect human, objects and thei…
Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline
Yangguang Li, Bin Huang, Zeren Chen +8
Recently, perception task based on Bird's-Eye View (BEV) representation has drawn more and more attention, and BEV representation is promising as the foundation for next-generation…
BEVBert: Multimodal Map Pre-training for Language-guided Navigation
Dong An, Yuankai Qi, Yangguang Li +4
Large-scale pre-training has shown promising results on the vision-and-language navigation (VLN) task. However, most existing pre-training methods employ discrete panoramas to lear…
RF: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference
Hao Wang, Yixin Cao, Yangguang Li +3
Document-level natural language inference (DOCNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothe…
A Mixture of Surprises for Unsupervised Reinforcement Learning
Andrew Zhao, Matthieu Gaetan Lin, Yangguang Li +2
Unsupervised reinforcement learning aims at learning a generalist policy in a reward-free manner for fast adaptation to downstream tasks. Most of the existing methods propose to pr…
Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization
Lei Cui, Yangguang Li, Xin Lu +2
Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model. Existing BO algorithms could converge sl…
Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies
Xingrun Xing, Yangguang Li, Wei Li +6
Existing Binary Neural Networks (BNNs) mainly operate on local convolutions with binarization function. However, such simple bit operations lack the ability of modeling contextual…
Task-Balanced Distillation for Object Detection
Ruining Tang, Zhenyu Liu, Yangguang Li +6
Mainstream object detectors are commonly constituted of two sub-tasks, including classification and regression tasks, implemented by two parallel heads. This classic design paradig…
1st Place Solutions for RxR-Habitat Vision-and-Language Navigation Competition (CVPR 2022)
Dong An, Zun Wang, Yangguang Li +5
This report presents the methods of the winning entry of the RxR-Habitat Competition in CVPR 2022. The competition addresses the problem of Vision-and-Language Navigation in Contin…
SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners
Feng Liang, Yangguang Li, Diana Marculescu
Recently, self-supervised Masked Autoencoders (MAE) have attracted unprecedented attention for their impressive representation learning ability. However, the pretext task, Masked I…
Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision
Yufeng Cui, Lichen Zhao, Feng Liang +2
Contrastive Language-Image Pretraining (CLIP) has emerged as a novel paradigm to learn visual models from language supervision. While researchers continue to push the frontier of C…
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples
Hao Wang, Yangguang Li, Zhen Huang +3
Unsupervised sentence embedding aims to obtain the most appropriate embedding for a sentence to reflect its semantic. Contrastive learning has been attracting developing attention.…
Rethinking the Zigzag Flattening for Image Reading
Qingsong Zhao, Yi Wang, Zhipeng Zhou +4
Sequence ordering of word vector matters a lot to text reading, which has been proven in natural language processing (NLP). However, the rule of different sequence ordering in comp…
RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training
Luya Wang, Feng Liang, Yangguang Li +3
Recently, self-supervised vision transformers have attracted unprecedented attention for their impressive representation learning ability. However, the dominant method, contrastive…
INTERN: A New Learning Paradigm Towards General Vision
Jing Shao, Siyu Chen, Yangguang Li +24
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society. However,…
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm
Yangguang Li, Feng Liang, Lichen Zhao +5
Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferab…