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Found 6 papers, 5 papers with code
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.
