15 citations · 44 across the 17 of their papers we have counts for
9 papers · 1 filter
A Generative Framework for Self-Supervised Facial Representation Learning
Ruian He, Zhen Xing, Weimin Tan +1
Self-supervised representation learning has gained increasing attention for strong generalization ability without relying on paired datasets. However, it has not been explored suff…
MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior
Shili Zhou, Xuhao Jiang, Weimin Tan +2
In recent years, many deep learning-based methods have been proposed to tackle the problem of optical flow estimation and achieved promising results. However, they hardly consider…
SAMFlow: Eliminating Any Fragmentation in Optical Flow with Segment Anything Model
Shili Zhou, Ruian He, Weimin Tan +1
Optical Flow Estimation aims to find the 2D dense motion field between two frames. Due to the limitation of model structures and training datasets, existing methods often rely too…
Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation
Ri Cheng, Xuhao Jiang, Ruian He +3
The video frame interpolation (VFI) model applies the convolution operation to all locations, leading to redundant computations in regions with easy motion. We can use dynamic spat…
DifFSS: Diffusion Model for Few-Shot Semantic Segmentation
Weimin Tan, Siyuan Chen, Bo Yan
Diffusion models have demonstrated excellent performance in image generation. Although various few-shot semantic segmentation (FSS) models with different network structures have be…
Instruct-NeuralTalker: Editing Audio-Driven Talking Radiance Fields with Instructions
Yuqi Sun, Ruian He, Weimin Tan +1
Recent neural talking radiance field methods have shown great success in photorealistic audio-driven talking face synthesis. In this paper, we propose a novel interactive framework…
Learning Survival Distribution with Implicit Survival Function
Yu Ling, Weimin Tan, Bo Yan
Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples. In implementation, existing methods model the su…
Multi-Modality Deep Network for JPEG Artifacts Reduction
Xuhao Jiang, Weimin Tan, Qing Lin +3
In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress. However, few methods are suitable for…
Multi-Modality Deep Network for Extreme Learned Image Compression
Xuhao Jiang, Weimin Tan, Tian Tan +2
Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur…