Search Results for author:
Found 19 papers, 11 papers with code
Text Prompting for Multi-Concept Video Customization by Autoregressive Generation
We present a method for multi-concept customization of pretrained text-to-video (T2V) models.
StoryBench: A Multifaceted Benchmark for Continuous Story Visualization
To fill this gap, we collect comprehensive human annotations on three existing datasets, and introduce StoryBench: a new, challenging multi-task benchmark to reliably evaluate forthcoming text-to-video models.
Phenaki: Variable Length Video Generation From Open Domain Textual Description
To the best of our knowledge, this is the first time a paper studies generating videos from time variable prompts.
Ranked #4 on
Video Prediction
on BAIR Robot Pushing
INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL
We propose a modified objective for model-based RL that, in combination with mutual information maximization, allows us to learn representations and dynamics for visual model-based RL without reconstruction in a way that explicitly prioritizes functionally relevant factors.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
FitVid: High-Capacity Pixel-Level Video Prediction
Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks.
FitVid: Overfitting in Pixel-Level Video Prediction
There is a growing body of evidence that underfitting on the training data is one of the primary causes for the low quality predictions.
Ranked #6 on
Video Generation
on BAIR Robot Pushing
On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning
In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning.
Model-based Reinforcement Learning
reinforcement-learning
+1
Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Model Based Reinforcement Learning for Atari
We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation
Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions.
Ranked #15 on
Video Generation
on BAIR Robot Pushing
Model-Based Reinforcement Learning for Atari
We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.
Ranked #12 on
Atari Games 100k
on Atari 100k
Adjustable Real-time Style Transfer
Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another.
Time Reversal as Self-Supervision
We test our method on the domain of assembly, specifically the mating of tetris-style block pairs.
Stochastic Variational Video Prediction
We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods.
Ranked #5 on
Video Prediction
on KTH
Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case
In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most influential algorithms that have been widely used in the bioinformatics community.
Fast Generation for Convolutional Autoregressive Models
In this work, we describe a method to speed up generation in convolutional autoregressive models.
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks.
NoiseOut: A Simple Way to Prune Neural Networks
In this paper, we propose NoiseOut, a fully automated pruning algorithm based on the correlation between activations of neurons in the hidden layers.
Seq-NMS for Video Object Detection
Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip.
