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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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GammaGAN: Gamma-Scaled Class Embeddings for Conditional Video Generation.

Minjae Kang¹, Yong Seok Heo^1,2

¹Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Republic of Korea.

Sensors (Basel, Switzerland)

|October 14, 2023

Summary

GammaGAN improves conditional video generation by effectively using class labels via projection and normalization. This novel approach enhances video quality from single images, outperforming existing methods.

Keywords:

GammaGAN class embeddings conditional generative adversarial networks conditional video generation generative adversarial networks projection discriminator video generation

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Conditional video generation from single images with class labels is challenging.
Traditional conditional generative adversarial networks (cGANs) struggle with effective class label utilization.

Purpose of the Study:

To propose GammaGAN, a novel model for improved conditional video generation.
To enhance the utilization of class labels in generative adversarial networks for video synthesis.

Main Methods:

Developed GammaGAN with two streams: a class embedding stream and a data stream.
Employed a projection method for effective class label utilization.
Introduced scaling of class embeddings and normalization of data stream outputs to balance feature vectors and class information.

Main Results:

GammaGAN demonstrated enhanced video quality compared to previous models.
Achieved relative improvements of 1.61% in PSNR, 1.66% in SSIM, and 0.36% in LPIPS on the MUG facial expression dataset.
The normalization technique effectively balanced the influence of feature vectors and class embeddings.

Conclusions:

The proposed GammaGAN model significantly advances conditional video generation.
The method shows promise for future research in generating high-quality videos from class-conditioned inputs.
Effective class label integration is crucial for plausible video synthesis.