Alan Yuille
Biographic Data
| ID | 4982703 |
|---|---|
| NAME | Alan Yuille |
| GIVEN NAMES | Alan |
| FAMILY NAME | Yuille |
| SIGNATURE | YUILLE A |
| AFFILIATIONS | Johns Hopkins University |
| ORCID | 0000-0001-5207-9249 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1995 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Mamba-Reg: Vision Mamba Also Needs Registers
Similar to Vision Transformers, this paper identifies artifacts also present within the feature maps of Vision Mamba. These artifacts, corresponding to high-norm tokens emerging in low-information background areas of images, appear much more severe in Vision Mamba—they exist prevalently even with the tiny-sized model and activate extensively across background regions. To mitigate this issue, we follow the prior solution of introducing register to…
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural …
The complexities of eliciting and assessing children's statements
The complexities of eliciting and assessing children's statements
The complexities of eliciting and assessing children's statements
The complexities of eliciting and assessing children's statements
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural …
Mamba-Reg: Vision Mamba Also Needs Registers
Similar to Vision Transformers, this paper identifies artifacts also present within the feature maps of Vision Mamba. These artifacts, corresponding to high-norm tokens emerging in low-information background areas of images, appear much more severe in Vision Mamba—they exist prevalently even with the tiny-sized model and activate extensively across background regions. To mitigate this issue, we follow the prior solution of introducing register to…
Computer Science (2 works) · Deception detection and forensic psychology (2 works) · Psychology (2 works) · Advanced Image and Video Retrieval Techniques (1 works) · Advanced Neural Network Applications (1 works) · African history and culture studies (1 works) · Artificial Intelligence (1 works) · Artificial neural network (1 works) · Child custody (1 works) · Conditional random field (1 works)