Search Results for author:
Found 15 papers, 4 papers with code
SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device
We have witnessed the unprecedented success of diffusion-based video generation over the past year.
SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training
For the first time, our model SnapGen, demonstrates the generation of 1024x1024 px images on a mobile device around 1. 4 seconds.
Ranked #11 on
Text-to-Image Generation
on GenEval
Slicing Vision Transformer for Flexible Inference
Thus, we propose a general framework, named Scala, to enable a single network to represent multiple smaller ViTs with flexible inference capability, which aligns with the inherent design of ViT to vary from widths.
AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation
Neural network architecture design requires making many crucial decisions.
Scalable Ranked Preference Optimization for Text-to-Image Generation
In this work, we investigate a scalable approach for collecting large-scale and fully synthetic datasets for DPO training.
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning
Specifically, we outperform the state of the art by 7% on UCF and 4% on HMDB for video retrieval, and 5% on UCF and 6% on HMDB for video classification
Transformers in Action: Weakly Supervised Action Segmentation
The video action segmentation task is regularly explored under weaker forms of supervision, such as transcript supervision, where a list of actions is easier to obtain than dense frame-wise labels.
Learning to Align Sequential Actions in the Wild
To this end, we propose an approach to enforce temporal priors on the optimal transport matrix, which leverages temporal consistency, while allowing for variations in the order of actions.
Learning by Aligning Videos in Time
To overcome this problem, we propose a temporal regularization term (i. e., Contrastive-IDM) which encourages different frames to be mapped to different points in the embedding space.
Direct and indirect transactions and requirements
The indirect transactions between sectors of an economic system has been a long-standing open problem.
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
We present a sampling-free approach for computing the epistemic uncertainty of a neural network.
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition
The lack of large-scale real datasets with annotations makes transfer learning a necessity for video activity understanding.
Human Motion Analysis with Deep Metric Learning
Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks.
Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization
One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization.
Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularization
One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization.
