Search Results for author: Zhihao Shi

Found 15 papers, 6 papers with code

TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling

no code implementations2 Aug 2024 Dong Huo, Zixin Guo, Xinxin Zuo, Zhihao Shi, Juwei Lu, Peng Dai, Songcen Xu, Li Cheng, Yee-Hong Yang

For view consistent sampling, first of all we maintain a texture map in RGB space that is parameterized by the denoising step and updated after each sampling step of the diffusion model to progressively reduce the view discrepancy.

Denoising Texture Synthesis

Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming

no code implementations19 Apr 2024 Jie Wang, Zhihai Wang, Xijun Li, Yufei Kuang, Zhihao Shi, Fangzhou Zhu, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu

Moreover, we observe that (P3) what order of selected cuts to prefer significantly impacts the efficiency of MILP solvers as well.

ROPO: Robust Preference Optimization for Large Language Models

no code implementations5 Apr 2024 Xize Liang, Chao Chen, Shuang Qiu, Jie Wang, Yue Wu, Zhihang Fu, Zhihao Shi, Feng Wu, Jieping Ye

Preference alignment is pivotal for empowering large language models (LLMs) to generate helpful and harmless responses.

Text Generation

Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias

1 code implementation26 Sep 2023 Zhihao Shi, Jie Wang, Fanghua Lu, Hanzhu Chen, Defu Lian, Zheng Wang, Jieping Ye, Feng Wu

The inverse mapping leads to an objective function that is equivalent to that by the joint training, while it can effectively incorporate GNNs in the training phase of NEs against the learning bias.

Representation Learning

Provably Convergent Subgraph-wise Sampling for Fast GNN Training

no code implementations17 Mar 2023 Jie Wang, Zhihao Shi, Xize Liang, Defu Lian, Shuiwang Ji, Bin Li, Enhong Chen, Feng Wu

During the message passing (MP) in GNNs, subgraph-wise sampling methods discard messages outside the mini-batches in backward passes to avoid the well-known neighbor explosion problem, i. e., the exponentially increasing dependencies of nodes with the number of MP iterations.

Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution

1 code implementation19 Feb 2023 Jie Wang, Rui Yang, Zijie Geng, Zhihao Shi, Mingxuan Ye, Qi Zhou, Shuiwang Ji, Bin Li, Yongdong Zhang, Feng Wu

The appealing features of RSD-OA include that: (1) RSD-OA is invariant to visual distractions, as it is conditioned on the predefined subsequent action sequence without task-irrelevant information from transition dynamics, and (2) the reward sequence captures long-term task-relevant information in both rewards and transition dynamics.

reinforcement-learning Reinforcement Learning +2

LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence

1 code implementation2 Feb 2023 Zhihao Shi, Xize Liang, Jie Wang

The key idea of LMC is to retrieve the discarded messages in backward passes based on a message passing formulation of backward passes.

Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings

no code implementations24 Mar 2022 Jie Wang, Zhanqiu Zhang, Zhihao Shi, Jianyu Cai, Shuiwang Ji, Feng Wu

Semantic matching models -- which assume that entities with similar semantics have similar embeddings -- have shown great power in knowledge graph embeddings (KGE).

Entity Embeddings Knowledge Graph Embeddings +1

Video Frame Interpolation Transformer

1 code implementation CVPR 2022 Zhihao Shi, Xiangyu Xu, Xiaohong Liu, Jun Chen, Ming-Hsuan Yang

Existing methods for video interpolation heavily rely on deep convolution neural networks, and thus suffer from their intrinsic limitations, such as content-agnostic kernel weights and restricted receptive field.

Video Frame Interpolation

Towards a Unified Approach to Single Image Deraining and Dehazing

no code implementations26 Mar 2021 Xiaohong Liu, Yongrui Ma, Zhihao Shi, Linhui Dai, Jun Chen

We develop a new physical model for the rain effect and show that the well-known atmosphere scattering model (ASM) for the haze effect naturally emerges as its homogeneous continuous limit.

Single Image Deraining

GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing

no code implementations25 Mar 2021 Xiaohong Liu, Zhihao Shi, Zijun Wu, Jun Chen

We also propose a novel intra-task knowledge transfer mechanism that can memorize and take advantage of synthetic domain knowledge to assist the learning process on the translated data.

Dimensionality Reduction Image Dehazing +2

Learning for Unconstrained Space-Time Video Super-Resolution

no code implementations25 Feb 2021 Zhihao Shi, Xiaohong Liu, Chengqi Li, Linhui Dai, Jun Chen, Timothy N. Davidson, Jiying Zhao

Recent years have seen considerable research activities devoted to video enhancement that simultaneously increases temporal frame rate and spatial resolution.

Optical Flow Estimation Space-time Video Super-resolution +2

Video Frame Interpolation via Generalized Deformable Convolution

1 code implementation24 Aug 2020 Zhihao Shi, Xiaohong Liu, Kangdi Shi, Linhui Dai, Jun Chen

Video frame interpolation aims at synthesizing intermediate frames from nearby source frames while maintaining spatial and temporal consistencies.

Video Frame Interpolation

GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing

1 code implementation ICCV 2019 Xiaohong Liu, Yongrui Ma, Zhihao Shi, Jun Chen

The proposed hazing method does not rely on the atmosphere scattering model, and we provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images are concerned.

Dimensionality Reduction Diversity +3

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