100 citations · 308 across the 28 of their papers we have counts for
10 papers · 1 filter
Conditional Image-to-Video Generation with Latent Flow Diffusion Models
Haomiao Ni, Changhao Shi, Kai Li +2
Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label li…
Attribute-Centric Compositional Text-to-Image Generation
Yuren Cong, Martin Renqiang Min, Li Erran Li +2
Despite the recent impressive breakthroughs in text-to-image generation, generative models have difficulty in capturing the data distribution of underrepresented attribute composit…
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis
Zhiheng Li, Martin Renqiang Min, Kai Li +1
Although progress has been made for text-to-image synthesis, previous methods fall short of generalizing to unseen or underrepresented attribute compositions in the input text. Lac…
AE-StyleGAN: Improved Training of Style-Based Auto-Encoders
Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min +3
StyleGANs have shown impressive results on data generation and manipulation in recent years, thanks to its disentangled style latent space. A lot of efforts have been made in inver…
Dual Projection Generative Adversarial Networks for Conditional Image Generation
Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos +4
Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been establish…
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
Yizhe Zhu, Martin Renqiang Min, Asim Kadav +1
We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision. Specifically, we exploit…
Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective
Kai Li, Martin Renqiang Min, Yun Fu
Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely based on the semantic descriptions of the classes. Existing algorithms usually formulate it as a seman…
Disentangled Deep Autoencoding Regularization for Robust Image Classification
Zhenyu Duan, Martin Renqiang Min, Li Erran Li +3
In spite of achieving revolutionary successes in machine learning, deep convolutional neural networks have been recently found to be vulnerable to adversarial attacks and difficult…
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Yao Li, Martin Renqiang Min, Wenchao Yu +3
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples. Inspired by the observation that the intrinsic dimension of i…
Adaptive Feature Abstraction for Translating Video to Text
Yunchen Pu, Martin Renqiang Min, Zhe Gan +1
Previous models for video captioning often use the output from a specific layer of a Convolutional Neural Network (CNN) as video features. However, the variable context-dependent s…