Search Results for author: Filip Radenovic

Found 17 papers, 8 papers with code

Context Diffusion: In-Context Aware Image Generation

no code implementations6 Dec 2023 Ivona Najdenkoska, Animesh Sinha, Abhimanyu Dubey, Dhruv Mahajan, Vignesh Ramanathan, Filip Radenovic

We propose Context Diffusion, a diffusion-based framework that enables image generation models to learn from visual examples presented in context.

Image Generation In-Context Learning

Scalable Interpretability via Polynomials

1 code implementation27 May 2022 Abhimanyu Dubey, Filip Radenovic, Dhruv Mahajan

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.

Additive models BIG-bench Machine Learning +1

Neural Basis Models for Interpretability

1 code implementation27 May 2022 Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan

However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfulness limitations.

Additive models Interpretable Machine Learning

Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals

1 code implementation24 Mar 2022 Simon Vandenhende, Dhruv Mahajan, Filip Radenovic, Deepti Ghadiyaram

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.

counterfactual Counterfactual Explanation +1

Large-Scale Attribute-Object Compositions

no code implementations24 May 2021 Filip Radenovic, Animesh Sinha, Albert Gordo, Tamara Berg, Dhruv Mahajan

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.

Attribute Object

Attention-Based Query Expansion Learning

no code implementations ECCV 2020 Albert Gordo, Filip Radenovic, Tamara Berg

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.

Image Retrieval

Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower

1 code implementation ICCV 2019 Giorgos Tolias, Filip Radenovic, Ondřej Chum

We show successful attacks to partially unknown systems, by designing various loss functions for the adversarial image construction.

Adversarial Attack Retrieval

Camera Elevation Estimation from a Single Mountain Landscape Photograph

no code implementations12 Jul 2016 Martin Cadik, Jan Vasicek, Michal Hradis, Filip Radenovic, Ondrej Chum

This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment.

From Dusk Till Dawn: Modeling in the Dark

no code implementations CVPR 2016 Filip Radenovic, Johannes L. Schonberger, Dinghuang Ji, Jan-Michael Frahm, Ondrej Chum, Jiri Matas

We present an algorithm that leverages the appearance variety to obtain more complete and accurate scene geometry along with consistent multi-illumination appearance information.

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