I'm thrilled to announce that we have two papers accepted for presentation at IEEE/CVF WACV 2024.
🔹INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
Our first paper, INCODE, introduces a new method that enhances Implicit Neural Representations (INRs) by leveraging deep prior knowledge. INCODE consists of a harmonizer network and a composer network, where the harmonizer network dynamically adjusts critical parameters of the composer's activation function. This leads to remarkable improvements in signal representation across various domains, including audio, image, and 3D shape. It also excels in addressing complex challenges such as neural radiance fields (NeRFs) and solving inverse problems like denoising, super-resolution, inpainting, and CT reconstruction.
📃 Project Page: https://lnkd.in/er9gXKfy
📚 Arxiv: https://lnkd.in/e6DBzEJs
💻 GitHub: https://lnkd.in/eCHefthw
🔹Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation
Our second paper introduces "Deformable Large Kernel Attention (D-LKA Attention)," a novel approach designed to elevate medical image segmentation. This method efficiently captures volumetric context using large convolution kernels, avoiding excessive computational demands. D-LKA Attention also benefits from deformable convolutions to adapt to diverse data patterns. We have created both 2D and 3D versions, with the 3D version excelling in cross-depth data understanding. This forms the basis of our new hierarchical Vision Transformer architecture, the D-LKA Net, which outperforms existing methods on popular medical segmentation datasets (Synapse, NIH Pancreas, and Skin lesion).
📚 Arxiv: https://lnkd.in/ezq66XCy
💻 GitHub: https://lnkd.in/eu42DJRW
I would like to express my sincere gratitude to Reza Azad, Alireza Hosseini, Ehsan Khodapanah Aghdam, Leon Niggemeier, Prof. Ulaş Bağcı, Prof. Merhof, and others for their invaluable contributions.
#WACV2024 #ComputerVision #MedicalImageSegmentation #AIResearch #INR
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