Search Results for author:
Found 21 papers, 15 papers with code
ILIAS: Instance-Level Image retrieval At Scale
An extensive benchmarking is performed with the following observations: i) models fine-tuned on specific domains, such as landmarks or products, excel in that domain but fail on ILIAS ii) learning a linear adaptation layer using multi-domain class supervision results in performance improvements, especially for vision-language models iii) local descriptors in retrieval re-ranking are still a key ingredient, especially in the presence of severe background clutter iv) the text-to-image performance of the vision-language foundation models is surprisingly close to the corresponding image-to-image case.
Composed Image Retrieval for Training-Free Domain Conversion
This work addresses composed image retrieval in the context of domain conversion, where the content of a query image is retrieved in the domain specified by the query text.
Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch Classification
This work proposes a novel method for object co-segmentation, i. e. pixel-level localization of a common object in a set of images, that uses no pixel-level supervision for training.
Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization
The method that achieves the best performance on the augmented validation is selected from the proposed family.
Ranked #1 on
Single-Source Domain Generalization
on Digits-five
UDON: Universal Dynamic Online distillatioN for generic image representations
This boosts significantly the learning of complex domains which are characterised by a large number of classes and long-tail distributions.
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
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.
Object-Guided Day-Night Visual Localization in Urban Scenes
Direct matching of local features is sensitive to significant changes in illumination.
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
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 #6 on
Image Retrieval
on ROxford (Medium)
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.
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.
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
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.
Linking Art through Human Poses
We address the discovery of composition transfer in artworks based on their visual content.
Understanding and Improving Kernel Local Descriptors
We propose a multiple-kernel local-patch descriptor based on efficient match kernels from pixel gradients.
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 #10 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)
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.
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
