38 citations · 88 across the 64 of their papers we have counts for
11 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…
SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference
Feng Wang, Jieru Mei, Alan Yuille
Recent advances in contrastive language-image pretraining (CLIP) have demonstrated strong capabilities in zero-shot classification by aligning visual representations with target te…
Instruct2Attack: Language-Guided Semantic Adversarial Attacks
Jiang Liu, Chen Wei, Yuxiang Guo +5
We propose Instruct2Attack (I2A), a language-guided semantic attack that generates semantically meaningful perturbations according to free-form language instructions. We make use o…
IG Captioner: Information Gain Captioners are Strong Zero-shot Classifiers
Chenglin Yang, Siyuan Qiao, Yuan Cao +4
Generative training has been demonstrated to be powerful for building visual-language models. However, on zero-shot discriminative benchmarks, there is still a performance gap betw…
Structure-Aware Sparse-View X-ray 3D Reconstruction
Yuanhao Cai, Jiahao Wang, Alan Yuille +2
X-ray, known for its ability to reveal internal structures of objects, is expected to provide richer information for 3D reconstruction than visible light. Yet, existing neural radi…
Synthetic Data as Validation
Qixin Hu, Alan Yuille, Zongwei Zhou
This study leverages synthetic data as a validation set to reduce overfitting and ease the selection of the best model in AI development. While synthetic data have been used for au…
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
Jieneng Chen, Jieru Mei, Xianhang Li +12
Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. The u-shaped architecture, popularly known as U-Net, h…
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…
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts
Shiyi Du, Xiaosong Wang, Yongyi Lu +5
Image synthesis approaches, e.g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks. It is primarily beneficial to o…
Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data
Yu-Cheng Chou, Bowen Li, Deng-Ping Fan +2
Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with limited resources, it is challengin…