Search Results for author: Liang Hou

Found 15 papers, 11 papers with code

Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motion

no code implementations5 Feb 2024 Shiyuan Yang, Liang Hou, Haibin Huang, Chongyang Ma, Pengfei Wan, Di Zhang, Xiaodong Chen, Jing Liao

In practice, users often desire the ability to control object motion and camera movement independently for customized video creation.

Object Video Generation

I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models

no code implementations27 Dec 2023 Xun Guo, Mingwu Zheng, Liang Hou, Yuan Gao, Yufan Deng, Pengfei Wan, Di Zhang, Yufan Liu, Weiming Hu, ZhengJun Zha, Haibin Huang, Chongyang Ma

I2V-Adapter adeptly propagates the unnoised input image to subsequent noised frames through a cross-frame attention mechanism, maintaining the identity of the input image without any changes to the pretrained T2V model.

Video Generation

TEA: Test-time Energy Adaptation

1 code implementation24 Nov 2023 Yige Yuan, Bingbing Xu, Liang Hou, Fei Sun, HuaWei Shen, Xueqi Cheng

To address this, we propose a novel energy-based perspective, enhancing the model's perception of target data distributions without requiring access to training data or processes.


PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion

1 code implementation25 May 2023 Yige Yuan, Bingbing Xu, Bo Lin, Liang Hou, Fei Sun, HuaWei Shen, Xueqi Cheng

The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones.

Data Augmentation

Layer-adaptive Structured Pruning Guided by Latency

no code implementations23 May 2023 Siyuan Pan, Linna Zhang, Jie Zhang, Xiaoshuang Li, Liang Hou, Xiaobing Tu

Structured pruning can simplify network architecture and improve inference speed.

Network Pruning

Graph Adversarial Immunization for Certifiable Robustness

1 code implementation16 Feb 2023 Shuchang Tao, HuaWei Shen, Qi Cao, Yunfan Wu, Liang Hou, Xueqi Cheng

In this paper, we propose and formulate graph adversarial immunization, i. e., vaccinating part of graph structure to improve certifiable robustness of graph against any admissible adversarial attack.

Adversarial Attack Combinatorial Optimization

Adversarial Camouflage for Node Injection Attack on Graphs

1 code implementation3 Aug 2022 Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Liang Hou, Fei Sun, Xueqi Cheng

In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes.

Conditional GANs with Auxiliary Discriminative Classifier

2 code implementations21 Jul 2021 Liang Hou, Qi Cao, HuaWei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng

Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively.

Conditional Image Generation Generative Adversarial Network

Self-Supervised GANs with Label Augmentation

2 code implementations NeurIPS 2021 Liang Hou, HuaWei Shen, Qi Cao, Xueqi Cheng

Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environment.

Data Augmentation Image Generation +2

SDGNN: Learning Node Representation for Signed Directed Networks

1 code implementation7 Jan 2021 JunJie Huang, HuaWei Shen, Liang Hou, Xueqi Cheng

Guided by related sociological theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks.

Network Embedding

Slimmable Generative Adversarial Networks

1 code implementation10 Dec 2020 Liang Hou, Zehuan Yuan, Lei Huang, HuaWei Shen, Xueqi Cheng, Changhu Wang

In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power.

Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification

no code implementations27 Jul 2020 Bingbing Xu, Jun-Jie Huang, Liang Hou, Hua-Wei Shen, Jinhua Gao, Xue-Qi Cheng

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node.

Classification General Classification +2

Adversarial Immunization for Certifiable Robustness on Graphs

2 code implementations19 Jul 2020 Shuchang Tao, Hua-Wei Shen, Qi Cao, Liang Hou, Xue-Qi Cheng

Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.

Adversarial Attack Bilevel Optimization +2

Signed Graph Attention Networks

1 code implementation26 Jun 2019 Junjie Huang, Hua-Wei Shen, Liang Hou, Xue-Qi Cheng

We evaluate the proposed SiGAT method by applying it to the signed link prediction task.

Graph Attention Link Prediction +2

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