most citedTile Classification Based Viewport Prediction with Multi-modal Fusion Transformer

14 citations · 14 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2023★ 14 cited

Tile Classification Based Viewport Prediction with Multi-modal Fusion Transformer

Zhihao Zhang, Yiwei Chen, Weizhan Zhang +4

Viewport prediction is a crucial aspect of tile-based 360 video streaming system. However, existing trajectory based methods lack of robustness, also oversimplify the process of in…

cs.LG2023

SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization

Yingchun Wang, Jingcai Guo, Yi Liu +4

Model substructure learning aims to find an invariant network substructure that can have better out-of-distribution (OOD) generalization than the original full structure. Existing…

cs.AI2023

Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning

Yingchun Wang, Jingcai Guo, Song Guo +1

Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining sub-optimal…

cs.LG2023

Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models

Yingchun Wang, Jingcai Guo, Jie Zhang +3

Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy. However, FL has inherent challenges in term…

cs.LG2022

Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruning

Yingchun Wang, Jingcai Guo, Song Guo +2

Recent studies show that even highly biased dense networks contain an unbiased substructure that can achieve better out-of-distribution (OOD) generalization than the original model…

cs.CV2022

Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning

Yingchun Wang, Song Guo, Jingcai Guo +4

Network pruning is a promising way to generate light but accurate models and enable their deployment on resource-limited edge devices. However, the current state-of-the-art assumes…

eess.IV2022

Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report

Andrey Ignatov, Radu Timofte, Cheng-Ming Chiang +50

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While num…