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Found 7 papers, 5 papers with code
XFeat: Accelerated Features for Lightweight Image Matching
Besides, our model is designed to offer the choice of matching at the sparse or semi-dense levels, each of which may be more suitable for different downstream applications, such as visual navigation and augmented reality.
Improving the matching of deformable objects by learning to detect keypoints
We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence.
Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories
Empirically, we show that our dataset poses a hard challenge for large vision+language models as they perform poorly on our dataset: PaLI [14] is state-of-the-art on OK-VQA [37], yet it only achieves 13. 0% accuracy on our dataset.
Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints
Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval.
Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images
We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence.
Learning Geodesic-Aware Local Features from RGB-D Images
Most of the existing handcrafted and learning-based local descriptors are still at best approximately invariant to affine image transformations, often disregarding deformable surfaces.
Extracting Deformation-Aware Local Features by Learning to Deform
Despite the advances in extracting local features achieved by handcrafted and learning-based descriptors, they are still limited by the lack of invariance to non-rigid transformations.
