15 citations · 44 across the 17 of their papers we have counts for
17 papers
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…
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution
Ri Cheng, Yuqi Sun, Bo Yan +2
Recent multi-view multimedia applications struggle between high-resolution (HR) visual experience and storage or bandwidth constraints. Therefore, this paper proposes a Multi-View…
Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal
Xuhao Jiang, Weimin Tan, Ri Cheng +2
Under stereo settings, the performance of image JPEG artifacts removal can be further improved by exploiting the additional information provided by a second view. However, incorpor…
Perception-Oriented Stereo Image Super-Resolution
Chenxi Ma, Bo Yan, Weimin Tan +1
Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on im…
Rethinking Super-Resolution as Text-Guided Details Generation
Chenxi Ma, Bo Yan, Qing Lin +2
Deep neural networks have greatly promoted the performance of single image super-resolution (SISR). Conventional methods still resort to restoring the single high-resolution (HR) s…
Feature Pyramid Network for Multi-task Affective Analysis
Ruian He, Zhen Xing, Weimin Tan +1
Affective Analysis is not a single task, and the valence-arousal value, expression class, and action unit can be predicted at the same time. Previous researches did not pay enough…
Frame and Feature-Context Video Super-Resolution
Bo Yan, Chuming Lin, Weimin Tan
For video super-resolution, current state-of-the-art approaches either process multiple low-resolution (LR) frames to produce each output high-resolution (HR) frame separately in a…
Cycle-IR: Deep Cyclic Image Retargeting
Weimin Tan, Bo Yan, Chumin Lin +1
Supervised deep learning techniques have achieved great success in various fields due to getting rid of the limitation of handcrafted representations. However, most previous image…
Understanding the Feedforward Artificial Neural Network Model From the Perspective of Network Flow
Dawei Dai, Weimin Tan, Hong Zhan
In recent years, deep learning based on artificial neural network (ANN) has achieved great success in pattern recognition. However, there is no clear understanding of such neural c…