Research

Computer Vision

Improved Conditional VRNNs for Video Prediction

October 27, 2019

Abstract

Predicting future frames for a video sequence is a challenging generative modeling task. Promising approaches include probabilistic latent variable models such as the Variational Auto-Encoder. While VAEs can handle uncertainty and model multiple possible future outcomes, they have a tendency to produce blurry predictions. In this work we argue that this is a sign of underfitting. To address this issue, we propose to increase the expressiveness of the latent distributions and to use higher capacity likelihood models. Our approach relies on a hierarchy of latent variables, which defines a family of flexible prior and posterior distributions in order to better model the probability of future sequences. We validate our proposal through a series of ablation experiments and compare our approach to current state-of-the-art latent variable models. Our method performs favorably under several metrics in three different datasets.

Download the Paper

Related Publications

November 10, 2022

Computer Vision

Learning State-Aware Visual Representations from Audible Interactions

Unnat Jain, Abhinav Gupta, Himangi Mittal, Pedro Morgado

November 10, 2022

November 06, 2022

Computer Vision

Neural Basis Models for Interpretability

Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan

November 06, 2022

October 25, 2022

Theseus: A Library for Differentiable Nonlinear Optimization

Mustafa Mukadam, Austin Wang, Brandon Amos, Daniel DeTone, Jing Dong, Joe Ortiz, Luis Pineda, Maurizio Monge, Ricky Chen, Shobha Venkataraman, Stuart Anderson, Taosha Fan, Paloma Sodhi

October 25, 2022

October 22, 2022

Computer Vision

Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection

Naila Murray, Lei Wang, Piotr Koniusz, Shan Zhang

October 22, 2022

April 30, 2018

Computer Vision

NAM – Unsupervised Cross-Domain Image Mapping without Cycles or GANs | Facebook AI Research

Yedid Hoshen, Lior Wolf

April 30, 2018

December 11, 2019

Speech & Audio

Computer Vision

Hyper-Graph-Network Decoders for Block Codes | Facebook AI Research

Eliya Nachmani, Lior Wolf

December 11, 2019

April 30, 2018

NLP

Speech & Audio

Identifying Analogies Across Domains | Facebook AI Research

Yedid Hoshen, Lior Wolf

April 30, 2018

November 01, 2018

NLP

Computer Vision

Non-Adversarial Unsupervised Word Translation | Facebook AI Research

Yedid Hoshen, Lior Wolf

November 01, 2018

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.