29 citations · 36 across the 3 of their papers we have counts for
7 papers · 1 filter
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
Louis Thiry, Michael Arbel, Eugene Belilovsky +1
A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, o…
A Simple and Scalable Shape Representation for 3D Reconstruction
Mateusz Michalkiewicz, Eugene Belilovsky, Mahsa Baktashmotlagh +1
Deep learning applied to the reconstruction of 3D shapes has seen growing interest. A popular approach to 3D reconstruction and generation in recent years has been the CNN encoder-…
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas +3
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of…
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
Boris Knyazev, Harm de Vries, Cătălina Cangea +3
Scene graph generation (SGG) aims to predict graph-structured descriptions of input images, in the form of objects and relationships between them. This task is becoming increasingl…
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
Cătălina Cangea, Eugene Belilovsky, Pietro Liò +1
Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given…
Blindfold Baselines for Embodied QA
Ankesh Anand, Eugene Belilovsky, Kyle Kastner +2
We explore blindfold (question-only) baselines for Embodied Question Answering. The EmbodiedQA task requires an agent to answer a question by intelligently navigating in a simulate…