Search Results for author:
Found 32 papers, 20 papers with code
Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning
In this work, we present an approach that leverages three highly synergistic components, which are identified as key ingredients: joint classifier training with inliers and outliers, semi-supervised learning through pseudo-labeling, and model ensembling.
The 2023 Video Similarity Dataset and Challenge
The problem comprises two distinct but related tasks: determining whether a query video shares content with a reference video ("detection"), and additionally temporally localizing the shared content within each video ("localization").
HSCNet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer
In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image.
Self-Supervised Video Similarity Learning
We introduce S$^2$VS, a video similarity learning approach with self-supervision.
Rethinking matching-based few-shot action recognition
Few-shot action recognition, i. e. recognizing new action classes given only a few examples, benefits from incorporating temporal information.
Large-to-small Image Resolution Asymmetry in Deep Metric Learning
The goal is to obtain a network for database examples that is trained to operate on large resolution images and benefits from fine-grained image details, and a second network for query examples that operates on small resolution images but preserves a representation space aligned with that of the database network.
Edge Augmentation for Large-Scale Sketch Recognition without Sketches
To bridge the domain gap we present a novel augmentation technique that is tailored to the task of learning sketch recognition from a training set of natural images.
Results and findings of the 2021 Image Similarity Challenge
The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods.
The Met Dataset: Instance-level Recognition for Artworks
Testing is primarily performed on photos taken by museum guests depicting exhibits, which introduces a distribution shift between training and testing.
Recall@k Surrogate Loss with Large Batches and Similarity Mixup
This work focuses on learning deep visual representation models for retrieval by exploring the interplay between a new loss function, the batch size, and a new regularization approach.
Ranked #1 on
Vehicle Re-Identification
on VehicleID Small
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 #5 on
Image Retrieval
on ROxford (Medium)
Graph convolutional networks for learning with few clean and many noisy labels
In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given.
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.
Explicit Spatial Encoding for Deep Local Descriptors
We evaluate the descriptor on standard benchmarks.
Label Propagation for Deep Semi-supervised Learning
In this work, we employ a transductive label propagation method that is based on the manifold assumption to make predictions on the entire dataset and use these predictions to generate pseudo-labels for the unlabeled data and train a deep neural network.
Understanding and Improving Kernel Local Descriptors
We propose a multiple-kernel local-patch descriptor based on efficient match kernels from pixel gradients.
Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking
State of the art image retrieval performance is achieved with CNN features and manifold ranking using a k-NN similarity graph that is pre-computed off-line.
Mining on Manifolds: Metric Learning without Labels
Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds.
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)
Unsupervised object discovery for instance recognition
Eliminating the impact of the clutter on the image descriptor increases the chance of retrieving relevant images and prevents topic drift due to actually retrieving the clutter in the case of query expansion.
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)
Multiple-Kernel Local-Patch Descriptor
We propose a multiple-kernel local-patch descriptor based on efficient match kernels of patch gradients.
Panorama to panorama matching for location recognition
Location recognition is commonly treated as visual instance retrieval on "street view" imagery.
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.
Fast Spectral Ranking for Similarity Search
This makes the Euclidean nearest neighbor search biased for this task.
Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations
The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches.
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
Particular object retrieval with integral max-pooling of CNN activations
Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations.
Ranked #4 on
Image Retrieval
on Par6k
A comparison of dense region detectors for image search and fine-grained classification
Our results show that the regular dense detector is outperformed by other methods in most situations, leading us to improve the state of the art in comparable setups on standard retrieval and fined-grain benchmarks.
Orientation covariant aggregation of local descriptors with embeddings
Our geometric-aware aggregation strategy is effective for image search, as shown by experiments performed on standard benchmarks for image and particular object retrieval, namely Holidays and Oxford buildings.
