Results 61 to 70 of about 1,683,190 (291)
Self-Supervised Vision Transformers for Malware Detection
IEEE Access, 2022 Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Previously unseen malware which is not determined by security vendors are often used in these attacks and it is becoming ...
Sachith Seneviratne +3 more
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Reversible Vision Transformers
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory requirement from the depth of the model, Reversible Vision Transformers enable scaling up architectures with efficient memory usage.
Karttikeya Mangalam +6 more
openaire +2 more sources
IEEE Open Journal of Intelligent Transportation Systems, 2023 In this paper, we investigate the use of Vision Transformers for processing and understanding visual data in an autonomous driving setting. Specifically, we explore the use of Vision Transformers for semantic segmentation and monocular depth estimation ...
Durga Prasad Bavirisetti +3 more
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Multimodal Token Fusion for Vision Transformers [PDF]
Computer Vision and Pattern Recognition, 2022 Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images.
Yikai Wang +5 more
semanticscholar +1 more source
Transformer architectures for computer vision: A comprehensive review and future research directions [PDF]
EPJ Web of ConferencesLong-range dependencies and contextual relationships in videos were captured by using Convolutional Neural Networks (CNNs) in past. Recently the use of Transformers is started for capturing the long-range dependencies and contextual relationships in ...
Ugile Tukaram, Uke Nilesh
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Identifying the role of vision transformer for skin cancer—A scoping review
Frontiers in Artificial Intelligence, 2023 IntroductionDetecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between ...
Sulaiman Khan, Hazrat Ali, Zubair Shah
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EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers [PDF]
European Conference on Computer Vision, 2022 Self-attention based models such as vision transformers (ViTs) have emerged as a very competitive architecture alternative to convolutional neural networks (CNNs) in computer vision.
Junting Pan +7 more
semanticscholar +1 more source
Vision Transformers Are Robust Learners
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Transformers, composed of multiple self-attention layers, hold strong promises toward a generic learning primitive applicable to different data modalities, including the recent breakthroughs in computer vision achieving state-of-the-art (SOTA) standard accuracy. What remains largely unexplored is their robustness evaluation and attribution.
Sayak Paul, Pin-Yu Chen
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Re-Introducing BN Into Transformers for Vision Tasks
IEEE Access, 2023 In recent years, Transformer-based models have exhibited significant advancements over previous models in natural language processing and vision tasks. This powerful methodology has also been extended to the 3D point cloud domain, where it can mitigate ...
Xue-Song Tang, Xian-Lin Xie
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BUViTNet: Breast Ultrasound Detection via Vision Transformers
Diagnostics, 2022 Convolutional neural networks (CNNs) have enhanced ultrasound image-based early breast cancer detection. Vision transformers (ViTs) have recently surpassed CNNs as the most effective method for natural image analysis. ViTs have proven their capability of
Gelan Ayana, Se-woon Choe
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