Results 31 to 40 of about 157,275 (228)

Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review

BMC Medical Imaging, 2023
Background Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications.
Hazrat Ali, Farida Mohsen, Zubair Shah
doaj   +1 more source

Privacy-Preserving Semantic Segmentation Using Vision Transformer

Journal of Imaging, 2022
In this paper, we propose a privacy-preserving semantic segmentation method that uses encrypted images and models with the vision transformer (ViT), called the segmentation transformer (SETR).
Hitoshi Kiya, Teru Nagamori, Shoko Imaizumi, Sayaka Shiota   +3 more
doaj   +1 more source

Recurrent Attentional Networks for Saliency Detection

, 2016
Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales.
Kuen, Jason, Wang, Gang, Wang, Zhenhua
core   +1 more source

Modeling Image Virality with Pairwise Spatial Transformer Networks

, 2017
The study of virality and information diffusion online is a topic gaining traction rapidly in the computational social sciences. Computer vision and social network analysis research have also focused on understanding the impact of content and information
Agarwal, Sumeet, Dubey, Abhimanyu
core   +1 more source

V-LTCS: Backbone exploration for Multimodal Misogynous Meme detection

Natural Language Processing Journal
Memes have become a fundamental part of online communication and humour, reflecting and shaping the culture of today’s digital age. The amplified Meme culture is inadvertently endorsing and propagating casual Misogyny. This study proposes V-LTCS (Vision-
Sneha Chinivar, Roopa M.S., Arunalatha J.S., Venugopal K.R.   +3 more
doaj   +1 more source

GaitTriViT and GaitVViT: Transformer-based methods emphasizing spatial or temporal aspects in gait recognition [PDF]

PeerJ Computer Science
In image recognition tasks, subjects with long distances and low resolution remain a challenge, whereas gait recognition, identifying subjects by walking patterns, is considered one of the most promising biometric technologies due to its stability and ...
Hongyun Sheng
doaj   +2 more sources

QuadTree Attention for Vision Transformers

CoRR, 2022
Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency. However, their quadratic computational complexity poses a major obstacle for applying them to vision tasks requiring dense predictions, such as object detection, feature matching, stereo, etc.
Tang, Shitao, Zhang, Jiahui, Zhu, Siyu, Tan, Ping   +3 more
openaire   +4 more sources

Transforming glaucoma diagnosis: transformers at the forefront

Frontiers in Artificial Intelligence
Although the Vision Transformer architecture has become widely accepted as the standard for image classification tasks, using it for object detection in computer vision poses significant challenges.
Farheen Chincholi, Harald Koestler
doaj   +1 more source

Gait-ViT: Gait Recognition with Vision Transformer

Sensors, 2022
Identifying an individual based on their physical/behavioral characteristics is known as biometric recognition. Gait is one of the most reliable biometrics due to its advantages, such as being perceivable at a long distance and difficult to replicate ...
Jashila Nair Mogan, Chin Poo Lee, Kian Ming Lim, Kalaiarasi Sonai Muthu   +3 more
doaj   +1 more source

Local Descriptors Optimized for Average Precision

, 2018
Extraction of local feature descriptors is a vital stage in the solution pipelines for numerous computer vision tasks. Learning-based approaches improve performance in certain tasks, but still cannot replace handcrafted features in general. In this paper,
He, Kun, Lu, Yan, Sclaroff, Stan
core   +1 more source