vision Transformers (ViTs) are the current state-of-the-art in deep learning for computervision tasks. They are trained on vast datasets and are capable of useful downstream tasks through clever use of the attention ...
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ISBN:
(纸本)9783031776090;9783031776106
vision Transformers (ViTs) are the current state-of-the-art in deep learning for computervision tasks. They are trained on vast datasets and are capable of useful downstream tasks through clever use of the attention mechanism. The biggest limiting factor for ViTs is the number of pixels and tokens that can be processed in a given pass. Memory constraints on both patch size and the number of patches mean that ViTs are most effective at processing relatively low-resolution images. Whilst ViTs can attend very flexibly across an image, attending across images in a naive fashion requires memory proportional to the square of the number of images. This is a further limiting factor. Given the task of automated assessment of psoriasis severity, a chronic skin condition that can affect large portions of a person's skin, it is necessary to look across multiple images and at fine detail in large images. We present a method that adapts ViTs to a two-stage design that allows for the regression of a patient's psoriasis score across multiple images and resolutions and shows its effectiveness relative to a baseline ViT. The implementation of our method is available at https://***/KCL-BMEIS/***.
Due to factors such as the large body volume, high position of the driver's cabin, and limited range of rearview mirror reflection, trucks have blind spots when making right turns and reversing. This prevents driv...
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Multimodal data such as text and image play an important role in various fields. Traditional machine learning methods often only deal with the data of a single modality, while ignoring the relevance between different ...
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Since its introduction, Denoising Diffusion Probabilistic Models (DDPM) have received widespread attention for their exceptional performance in image generation. They generate new samples by simulating the denoising p...
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The proceedings contain 122 papers. The topics discussed include: DFrFT-ES model for emotion recognition based on fractional Fourier transform of EEG signals;research on traffic sign recognition under complex meteorol...
ISBN:
(纸本)9781510687615
The proceedings contain 122 papers. The topics discussed include: DFrFT-ES model for emotion recognition based on fractional Fourier transform of EEG signals;research on traffic sign recognition under complex meteorological conditions;diffusion-augmented learning for long-tail recognition;apple leaf scab recognition using CNN and transfer learning;container image management in cloud-edge environments: an image deletion method based on layer affinity;computer graphics and imageprocessing techniques based on visual communication design;dynamic fusion and non-negative matrix factorization-based multi-view clustering method;convolutional recurrent neural network-based EEG signal classification in motor imagery;and sentiment classification of MOOC courses by merging local context focus and bi-directional gated recurrent unit.
Depression has the potential to impact death rates, particularly when it comes to death by suicide. Inadequate diagnosis may result in a delay or unsuitable therapy, which can worsen symptoms of depression. Unaddresse...
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Depth information is useful in many imageprocessing and computervision applications, but in photography, depth information is lost in the process of projecting a real-world scene onto a 2D plane. Extracting depth in...
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Creating natural language descriptions or captions for images is a formidable task that requires a combination of computervision techniques to understand image content and natural language processing models to expres...
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Fluidized bed granulation is a unit operation widely used in the pharmaceutical, chemical and food processing industries. It is a manufacturing technology that by suspending lose powders using hot air and transforms t...
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vision-language tracking models aim to improve target tracking performance by fusing visual features and language description of the target, making it more useful and robust for a wider range of applications. Transfor...
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