activity
20172023
most citedThinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers

133 citations · 343 across the 25 of their papers we have counts for

collaborators

41 papers

cs.CV2023

Language-Guided Music Recommendation for Video via Prompt Analogies

Daniel McKee, Justin Salamon, Josef Sivic +1

We propose a method to recommend music for an input video while allowing a user to guide music selection with free-form natural language. A key challenge of this problem setting is…

cs.CV20224 cited

Multi-Task Learning of Object State Changes from Uncurated Videos

Tomáš Souček, Jean-Baptiste Alayrac, Antoine Miech +2

We aim to learn to temporally localize object state changes and the corresponding state-modifying actions by observing people interacting with objects in long uncurated web videos.…

cs.LG20221 cited

Benchmarking Learning Efficiency in Deep Reservoir Computing

Hugo Cisneros, Josef Sivic, Tomas Mikolov

It is common to evaluate the performance of a machine learning model by measuring its predictive power on a test dataset. This approach favors complicated models that can smoothly…

cs.RO20221 cited

Differentiable Collision Detection: a Randomized Smoothing Approach

Louis Montaut, Quentin Le Lidec, Antoine Bambade +3

Collision detection appears as a canonical operation in a large range of robotics applications from robot control to simulation, including motion planning and estimation. While the…

cs.RO2022

Collision Detection Accelerated: An Optimization Perspective

Louis Montaut, Quentin Le Lidec, Vladimir Petrik +2

Collision detection between two convex shapes is an essential feature of any physics engine or robot motion planner. It has often been tackled as a computational geometry problem,…

cs.CV20221 cited

Learning to Answer Visual Questions from Web Videos

Antoine Yang, Antoine Miech, Josef Sivic +2

Recent methods for visual question answering rely on large-scale annotated datasets. Manual annotation of questions and answers for videos, however, is tedious, expensive and preve…