128 citations · 206 across the 16 of their papers we have counts for
12 papers · 1 filter
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…
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…
Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
Feng Liang, Bichen Wu, Xiaoliang Dai +6
Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage…
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…
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…
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…
Inception Convolution with Efficient Dilation Search
Jie Liu, Chuming Li, Feng Liang +5
As a variant of standard convolution, a dilated convolution can control effective receptive fields and handle large scale variance of objects without introducing additional computa…
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition
Hsin-Pai Cheng, Feng Liang, Meng Li +5
Scale variance among different sizes of body parts and objects is a challenging problem for visual recognition tasks. Existing works usually design dedicated backbone or apply Neur…
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search
Mingzhu Shen, Feng Liang, Ruihao Gong +6
Quantization Neural Networks (QNN) have attracted a lot of attention due to their high efficiency. To enhance the quantization accuracy, prior works mainly focus on designing advan…
Computation Reallocation for Object Detection
Feng Liang, Chen Lin, Ronghao Guo +4
The allocation of computation resources in the backbone is a crucial issue in object detection. However, classification allocation pattern is usually adopted directly to object det…