103 research outputs found
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
Diabetic retinopathy (DR) is a common retinal disease that leads to
blindness. For diagnosis purposes, DR image grading aims to provide automatic
DR grade classification, which is not addressed in conventional research
methods of binary DR image classification. Small objects in the eye images,
like lesions and microaneurysms, are essential to DR grading in medical
imaging, but they could easily be influenced by other objects. To address these
challenges, we propose a new deep learning architecture, called BiRA-Net, which
combines the attention model for feature extraction and bilinear model for
fine-grained classification. Furthermore, in considering the distance between
different grades of different DR categories, we propose a new loss function,
called grading loss, which leads to improved training convergence of the
proposed approach. Experimental results are provided to demonstrate the
superior performance of the proposed approach.Comment: Accepted at ICIP 201
Prompt-based Alignment of Headlines and Images Using OpenCLIP
In this paper, we describe how we leverage OpenCLIP to generate automated image recommendations for online news articles for the MediaEval 2023 NewsImages task. By exploring different text prompting techniques, a total of five retrieval approaches were devised. Results show, however, that the best performing approach is an unmodified CLIP version with the raw article headline as input. We reflect on this finding and its implication for future NewsImages tasks
Learning Unorthogonalized Matrices for Rotation Estimation
Estimating 3D rotations is a common procedure for 3D computer vision. The
accuracy depends heavily on the rotation representation. One form of
representation -- rotation matrices -- is popular due to its continuity,
especially for pose estimation tasks. The learning process usually incorporates
orthogonalization to ensure orthonormal matrices. Our work reveals, through
gradient analysis, that common orthogonalization procedures based on the
Gram-Schmidt process and singular value decomposition will slow down training
efficiency. To this end, we advocate removing orthogonalization from the
learning process and learning unorthogonalized `Pseudo' Rotation Matrices
(PRoM). An optimization analysis shows that PRoM converges faster and to a
better solution. By replacing the orthogonalization incorporated representation
with our proposed PRoM in various rotation-related tasks, we achieve
state-of-the-art results on large-scale benchmarks for human pose estimation
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