190 citations · 622 across the 19 of their papers we have counts for
20 papers
CLIP-Mesh: Generating textured meshes from text using pretrained image-text models
Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky +1
We present a technique for zero-shot generation of a 3D model using only a target text prompt. Without any 3D supervision our method deforms the control shape of a limit subdivided…
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…
Generative Compositional Augmentations for Scene Graph Prediction
Boris Knyazev, Harm de Vries, Cătălina Cangea +3
Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection of vision and language. We consider a challe…
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…