401 citations · 1.6k across the 64 of their papers we have counts for
12 papers · 1 filter
A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive Properties
Junfei Xiao, Ziqi Zhou, Wenxuan Li +6
This paper introduces ProLab, a novel approach using property-level label space for creating strong interpretable segmentation models. Instead of relying solely on category-specifi…
Rejuvenating image-GPT as Strong Visual Representation Learners
Sucheng Ren, Zeyu Wang, Hongru Zhu +3
This paper enhances image-GPT (iGPT), one of the pioneering works that introduce autoregressive pretraining to predict the next pixels for visual representation learning. Two simpl…
Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training
Yipeng Gao, Zeyu Wang, Wei-Shi Zheng +2
Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, i.e., aligning point cloud representation to image and text embedding space individually.…
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning
Peiran Xu, Zeyu Wang, Jieru Mei +4
Federated learning (FL) is an emerging paradigm in machine learning, where a shared model is collaboratively learned using data from multiple devices to mitigate the risk of data l…
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation
Zeyu Wang, Dingwen Li, Chenxu Luo +2
3D perception based on the representations learned from multi-camera bird's-eye-view (BEV) is trending as cameras are cost-effective for mass production in autonomous driving indus…
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
Yiqing Wang, Zihan Li, Jieru Mei +7
Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a n…
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
Qingyue Wei, Lequan Yu, Xianhang Li +4
Medical imaging has witnessed remarkable progress but usually requires a large amount of high-quality annotated data which is time-consuming and costly to obtain. To alleviate this…
CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a $10,000 Budget; An Extra $4,000 Unlocks 81.8% Accuracy
Xianhang Li, Zeyu Wang, Cihang Xie
The recent work CLIPA presents an inverse scaling law for CLIP training -- whereby the larger the image/text encoders used, the shorter the sequence length of image/text tokens tha…
An Inverse Scaling Law for CLIP Training
Xianhang Li, Zeyu Wang, Cihang Xie
CLIP, one of the pioneering foundation models that connect images and text, has enabled many recent breakthroughs in computer vision. However, its associated training cost is prohi…
Diffusion Models as Masked Autoencoders
Chen Wei, Karttikeya Mangalam, Po-Yao Huang +7
There has been a longstanding belief that generation can facilitate a true understanding of visual data. In line with this, we revisit generatively pre-training visual representati…
Benchmarking Robustness in Neural Radiance Fields
Chen Wang, Angtian Wang, Junbo Li +2
Neural Radiance Field (NeRF) has demonstrated excellent quality in novel view synthesis, thanks to its ability to model 3D object geometries in a concise formulation. However, curr…
On the Adversarial Robustness of Camera-based 3D Object Detection
Shaoyuan Xie, Zichao Li, Zeyu Wang +1
In recent years, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost. However, the robustness o…