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Found 8 papers, 5 papers with code
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition
To tackle this issue, we propose content-motion latent diffusion model (CMD), a novel efficient extension of pretrained image diffusion models for video generation.
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck
In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition (i. e., less generalizable), so that one cannot prevent a model from co-adapting on such (so-called) "shortcut" signals: this makes the model fragile in various distribution shifts.
Video Probabilistic Diffusion Models in Projected Latent Space
Specifically, PVDM is composed of two components: (a) an autoencoder that projects a given video as 2D-shaped latent vectors that factorize the complex cubic structure of video pixels and (b) a diffusion model architecture specialized for our new factorized latent space and the training/sampling procedure to synthesize videos of arbitrary length with a single model.
Scalable Neural Video Representations with Learnable Positional Features
Succinct representation of complex signals using coordinate-based neural representations (CNRs) has seen great progress, and several recent efforts focus on extending them for handling videos.
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
In this paper, we found that the recent emerging paradigm of implicit neural representations (INRs) that encodes a continuous signal into a parameterized neural network effectively mitigates the issue.
Ranked #25 on
Video Generation
on UCF-101
RoMA: Robust Model Adaptation for Offline Model-based Optimization
We consider the problem of searching an input maximizing a black-box objective function given a static dataset of input-output queries.
Abstract Reasoning via Logic-guided Generation
Abstract reasoning, i. e., inferring complicated patterns from given observations, is a central building block of artificial general intelligence.
Consistency Regularization for Adversarial Robustness
Adversarial training (AT) is currently one of the most successful methods to obtain the adversarial robustness of deep neural networks.
