49 citations · 160 across the 14 of their papers we have counts for
14 papers · 1 filter
Global Features are All You Need for Image Retrieval and Reranking
Shihao Shao, Kaifeng Chen, Arjun Karpur +3
Image retrieval systems conventionally use a two-stage paradigm, leveraging global features for initial retrieval and local features for reranking. However, the scalability of this…
Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories
Thomas Mensink, Jasper Uijlings, Lluis Castrejon +6
We propose Encyclopedic-VQA, a large scale visual question answering (VQA) dataset featuring visual questions about detailed properties of fine-grained categories and instances. It…
NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations
Varun Jampani, Kevis-Kokitsi Maninis, Andreas Engelhardt +13
Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on…
Yes, we CANN: Constrained Approximate Nearest Neighbors for local feature-based visual localization
Dror Aiger, André Araujo, Simon Lynen
Large-scale visual localization systems continue to rely on 3D point clouds built from image collections using structure-from-motion. While the 3D points in these models are repres…
Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints
Guilherme Potje, Felipe Cadar, Andre Araujo +2
Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. The core assumption of most methods is that im…
LFM-3D: Learnable Feature Matching Across Wide Baselines Using 3D Signals
Arjun Karpur, Guilherme Perrotta, Ricardo Martin-Brualla +2
Finding localized correspondences across different images of the same object is crucial to understand its geometry. In recent years, this problem has seen remarkable progress with…
Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information
Zu Kim, André Araujo, Bingyi Cao +5
There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic. Most fairness efforts in computer vision have been f…
Towards A Fairer Landmark Recognition Dataset
Zu Kim, André Araujo, Bingyi Cao +5
We introduce a new landmark recognition dataset, which is created with a focus on fair worldwide representation. While previous work proposes to collect as many images as possible…
Class-Balanced Distillation for Long-Tailed Visual Recognition
Ahmet Iscen, André Araujo, Boqing Gong +1
Real-world imagery is often characterized by a significant imbalance of the number of images per class, leading to long-tailed distributions. An effective and simple approach to lo…
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
Tobias Weyand, Andre Araujo, Bingyi Cao +1
While image retrieval and instance recognition techniques are progressing rapidly, there is a need for challenging datasets to accurately measure their performance -- while posing…
Unifying Deep Local and Global Features for Image Search
Bingyi Cao, Andre Araujo, Jack Sim
Image retrieval is the problem of searching an image database for items that are similar to a query image. To address this task, two main types of image representations have been s…
Detect-to-Retrieve: Efficient Regional Aggregation for Image Search
Marvin Teichmann, Andre Araujo, Menglong Zhu +1
Retrieving object instances among cluttered scenes efficiently requires compact yet comprehensive regional image representations. Intuitively, object semantics can help build the i…
Recurrent Neural Networks for Person Re-identification Revisited
Jean-Baptiste Boin, Andre Araujo, Bernd Girod
The task of person re-identification has recently received rising attention due to the high performance achieved by new methods based on deep learning. In particular, in the contex…
Large-Scale Image Retrieval with Attentive Deep Local Features
Hyeonwoo Noh, Andre Araujo, Jack Sim +2
We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature). The new feature is based on convolutional neura…