Search Results for author:
Found 4 papers, 4 papers with code
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
In particular, annotation errors, the size of the dataset, and the level of challenge are addressed: new annotation for both datasets is created with an extra attention to the reliability of the ground truth.
Fine-tuning CNN Image Retrieval with No Human Annotation
We show that both hard-positive and hard-negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance of particular-object retrieval.
Ranked #9 on
Image Retrieval
on RParis (Medium)
Deep Shape Matching
We cast shape matching as metric learning with convolutional networks.
Ranked #1 on
Sketch-Based Image Retrieval
on Chairs
(using extra training data)
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in many computer vision tasks.
Ranked #5 on
Image Retrieval
on Par6k
