1k citations · 8k across the 112 of their papers we have counts for
7 papers · 2 filters
Multi-way Particle Swarm Fusion
Chen Liu, Hang Yan, Pushmeet Kohli +1
This paper proposes a novel MAP inference framework for Markov Random Field (MRF) in parallel computing environments. The inference framework, dubbed Swarm Fusion, is a natural gen…
Deep Multi-Modal Image Correspondence Learning
Chen Liu, Jiajun Wu, Pushmeet Kohli +1
Inference of correspondences between images from different modalities is an extremely important perceptual ability that enables humans to understand and recognize cross-modal conce…
Memory-augmented Attention Modelling for Videos
Rasool Fakoor, Abdel-rahman Mohamed, Margaret Mitchell +2
We present a method to improve video description generation by modeling higher-order interactions between video frames and described concepts. By storing past visual attention in t…
Deep disentangled representations for volumetric reconstruction
Edward Grant, Pushmeet Kohli, Marcel van Gerven
We introduce a convolutional neural network for inferring a compact disentangled graphical description of objects from 2D images that can be used for volumetric reconstruction. The…
Efficient Continuous Relaxations for Dense CRF
Alban Desmaison, Rudy Bunel, Pushmeet Kohli +2
Dense conditional random fields (CRF) with Gaussian pairwise potentials have emerged as a popular framework for several computer vision applications such as stereo correspondence a…
Learning to Navigate the Energy Landscape
Julien Valentin, Angela Dai, Matthias Nießner +4
In this paper, we present a novel and efficient architecture for addressing computer vision problems that use `Analysis by Synthesis'. Analysis by synthesis involves the minimizati…
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding
Yinda Zhang, Mingru Bai, Pushmeet Kohli +2
While deep neural networks have led to human-level performance on computer vision tasks, they have yet to demonstrate similar gains for holistic scene understanding. In particular,…