Search Results for author:
Found 17 papers, 8 papers with code
Repeatability Is Not Enough: Learning Affine Regions via Discriminability
A method for learning local affine-covariant regions is presented.
Ranked #4 on
Image Matching
on IMC PhotoTourism
(using extra training data)
Working hard to know your neighbor's margins: Local descriptor learning loss
We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT.
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
Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training
Vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems.
Neural Basis Models for Interpretability
However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfulness limitations.
Scalable Interpretability via Polynomials
We demonstrate by human subject evaluations that SPAMs are demonstrably more interpretable in practice, and are hence an effortless replacement for DNNs for creating interpretable and high-performance systems suitable for large-scale machine learning.
Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals
A visual counterfactual explanation replaces image regions in a query image with regions from a distractor image such that the system's decision on the transformed image changes to the distractor class.
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.
Camera Elevation Estimation from a Single Mountain Landscape Photograph
This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment.
Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors - Extended Version
This paper addresses the construction of a short-vector (128D) image representation for large-scale image and particular object retrieval.
From Single Image Query to Detailed 3D Reconstruction
Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade.
From Dusk Till Dawn: Modeling in the Dark
We present an algorithm that leverages the appearance variety to obtain more complete and accurate scene geometry along with consistent multi-illumination appearance information.
Attention-Based Query Expansion Learning
Query expansion is a technique widely used in image search consisting in combining highly ranked images from an original query into an expanded query that is then reissued, generally leading to increased recall and precision.
Large-Scale Attribute-Object Compositions
We study the problem of learning how to predict attribute-object compositions from images, and its generalization to unseen compositions missing from the training data.
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text.
Context Diffusion: In-Context Aware Image Generation
We propose Context Diffusion, a diffusion-based framework that enables image generation models to learn from visual examples presented in context.
