Search Results for author:
Found 16 papers, 12 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.
Understanding and Improving Kernel Local Descriptors
We propose a multiple-kernel local-patch descriptor based on efficient match kernels from pixel gradients.
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)
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
The 2021 Image Similarity Dataset and Challenge
This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021).
Ranked #1 on
Image Similarity Detection
on DISC21 dev
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)
Learning and aggregating deep local descriptors for instance-level recognition
At inference, the local descriptors are provided by the activations of internal components of the network.
Ranked #5 on
Image Retrieval
on ROxford (Medium)
No Fear of the Dark: Image Retrieval under Varying Illumination Conditions
Image retrieval under varying illumination conditions, such as day and night images, is addressed by image preprocessing, both hand-crafted and learned.
Ranked #3 on
Image Retrieval
on 24/7 Tokyo
Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower
We show successful attacks to partially unknown systems, by designing various loss functions for the adversarial image construction.
Minimal Solvers for Rectifying from Radially-Distorted Scales and Change of Scales
The proposed solvers use the affine invariant that coplanar repeats have the same scale in rectified space.
Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations
This paper introduces minimal solvers that jointly solve for radial lens undistortion and affine-rectification using local features extracted from the image of coplanar translated and reflected scene texture, which is common in man-made environments.
Dark Side Augmentation: Generating Diverse Night Examples for Metric Learning
We propose to train a GAN-based synthetic-image generator, translating available day-time image examples into night images.
Ranked #1 on
Image Retrieval
on 24/7 Tokyo
Asymmetric Feature Maps with Application to Sketch Based Retrieval
To demonstrate the advantages of the AFM method, we derive a short vector image representation that, due to asymmetric feature maps, supports efficient scale and translation invariant sketch-based image retrieval.
Linking Art through Human Poses
We address the discovery of composition transfer in artworks based on their visual content.
Object-Guided Day-Night Visual Localization in Urban Scenes
Direct matching of local features is sensitive to significant changes in illumination.
Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations
In this work, we address the problem of universal image embedding, where a single universal model is trained and used in multiple domains.
