Research Area
Year Published

122 Results

May 6, 2019

Efficient Lifelong Learning with A-GEM

International Conference on Learning Representations (ICLR)

In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task. In this work, we investigate the efficiency of current lifelong approaches, in terms of sample complexity, computational and memory cost.

By: Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach, Mohamed Elhoseiny

May 6, 2019

Selfless Sequential Learning

International Conference on Learning Representations (ICLR)

Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for future tasks to be added and thus leave enough capacity for them.

By: Rahaf Aljundi, Marcus Rohrbach, Tinne Tuytelaars

May 4, 2019

Quasi-Hyperbolic Momentum and Adam for Deep Learning

International Conference on Learning Representations (ICLR)

Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover.

By: Jerry Ma, Denis Yarats

May 1, 2019

Large-scale weakly-supervised pre-training for video action recognition

Conference Computer Vision and Pattern Recognition (CVPR)

Current fully-supervised video datasets consist of only a few hundred thousand videos and fewer than a thousand domain-specific labels. This hinders the progress towards advanced video architectures. This paper presents an in-depth study of using large volumes of web videos for pre-training video models for the task of action recognition.

By: Deepti Ghadiyaram, Matt Feiszli, Du Tran, Xueting Yan, Hen Wang, Dhruv Mahajan

April 28, 2019

Inverse Path Tracing for Joint Material and Lighting Estimation

Computer Vision and Pattern Recognition (CVPR)

Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials and illumination. We introduce Inverse Path Tracing, a novel approach to jointly estimate the material properties of objects and light sources in indoor scenes by using an invertible light transport simulation.

By: Dejan Azinović, Tzu-Mao Li, Anton S. Kaplanyan, Matthias Nießner

March 12, 2019

Convolutional neural networks for mesh-based parcellation of the cerebral cortex

Medical Imaging with Deep Learning (MIDL)

We show experimentally on the Human Connectome Project dataset that the proposed graph convolutional models outperform current state-of-the-art and baselines, highlighting the potential and applicability of these methods to tackle neuroimaging challenges, paving the road towards a better characterization of brain diseases.

By: Guillem Cucurull, Konrad Wagstyl, Arantxa Casanova, Petar Velickovic, Estrid Jakobsen, Michal Drozdzal, Adriana Romero, Alan Evans, Yoshua Bengio

January 30, 2019

Large-Scale Visual Relationship Understanding

AAAI Conference on Artificial Intelligence (AAAI)

Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved.

By: Ji Zhang, Yannis Kalantidis, Marcus Rohrbach, Manohar Paluri, Ahmed Elgammal, Mohamed Elhoseiny

December 8, 2018

From Satellite Imagery to Disaster Insights

AI for Social Good Workshop at NeurIPS 2018

The use of satellite imagery has become increasingly popular for disaster monitoring and response. After a disaster, it is important to prioritize rescue operations, disaster response and coordinate relief efforts. These have to be carried out in a fast and efficient manner since resources are often limited in disaster affected areas and it’s extremely important to identify the areas of maximum damage. However, most of the existing disaster mapping efforts are manual which is time-consuming and often leads to erroneous results.

By: Jigar Doshi, Saikat Basu, Guan Pang

December 4, 2018

A2-Nets: Double Attention Networks

Neural Information Processing Systems (NeurIPS)

Learning to capture long-range relations is fundamental to image/video recognition. Existing CNN models generally rely on increasing depth to model such relations which is highly inefficient. In this work, we propose the “double attention block”, a novel component that aggregates and propagates informative global features from the entire spatio-temporal space of input images/videos, enabling subsequent convolution layers to access features from the entire space efficiently.

By: Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng

November 27, 2018

Deep Incremental Learning for Efficient High-Fidelity Face Tracking


In this paper, we present an incremental learning framework for efficient and accurate facial performance tracking. Our approach is to alternate the modeling step, which takes tracked meshes and texture maps to train our deep learning-based statistical model, and the tracking step, which takes predictions of geometry and texture our model infers from measured images and optimize the predicted geometry by minimizing image, geometry and facial landmark errors.

By: Chenglei Wu, Takaaki Shiratori, Yaser Sheikh