Search Results for author: Yuhe Jin

Found 8 papers, 4 papers with code

EEG Based Generative Depression Discriminator

no code implementations19 Jan 2024 Ziming Mao, Hao Wu, Yongxi Tan, Yuhe Jin

In the test, a segment of EEG signal was put into the two generators separately, if the relationship between the EEG signal and brain activity conforms to the characteristics of a certain category, then the signal generated by the generator of the corresponding category is more consistent with the original signal.

EEG

ViVid-1-to-3: Novel View Synthesis with Video Diffusion Models

no code implementations CVPR 2024 Jeong-gi Kwak, Erqun Dong, Yuhe Jin, Hanseok Ko, Shweta Mahajan, Kwang Moo Yi

Thus, to perform novel-view synthesis, we create a smooth camera trajectory to the target view that we wish to render, and denoise using both a view-conditioned diffusion model and a video diffusion model.

Novel View Synthesis Object

Neural Fourier Filter Bank

1 code implementation CVPR 2023 Zhijie Wu, Yuhe Jin, Kwang Moo Yi

We present a novel method to provide efficient and highly detailed reconstructions.

3D Shape Reconstruction

TUSK: Task-Agnostic Unsupervised Keypoints

no code implementations16 Jun 2022 Yuhe Jin, Weiwei Sun, Jan Hosang, Eduard Trulls, Kwang Moo Yi

Existing unsupervised methods for keypoint learning rely heavily on the assumption that a specific keypoint type (e. g. elbow, digit, abstract geometric shape) appears only once in an image.

Object Discovery Unsupervised Keypoints

Layered Controllable Video Generation

no code implementations24 Nov 2021 Jiahui Huang, Yuhe Jin, Kwang Moo Yi, Leonid Sigal

In the first stage, with the rich set of losses and dynamic foreground size prior, we learn how to separate the frame into foreground and background layers and, conditioned on these layers, how to generate the next frame using VQ-VAE generator.

Video Generation

MIST: Multiple Instance Spatial Transformer

1 code implementation CVPR 2021 Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.

Image Reconstruction

Image Matching across Wide Baselines: From Paper to Practice

5 code implementations3 Mar 2020 Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, Eduard Trulls

We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric.

Benchmarking

MIST: Multiple Instance Spatial Transformer Network

1 code implementation26 Nov 2018 Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.

Anomaly Detection In Surveillance Videos Image Reconstruction

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