Search Results for author:
Found 4 papers, 2 papers with code
Immersive Video Compression using Implicit Neural Representations
In this paper we, for the first time, extend their application to immersive (multi-view) videos, by proposing MV-HiNeRV, a new INR-based immersive video codec.
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning
In particular, the teacher model in FedSDD is an ensemble built by a small group of aggregated (global) models, instead of all client models, such that the computation cost will not scale with the number of clients.
HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
Learning-based video compression is currently a popular research topic, offering the potential to compete with conventional standard video codecs.
Ranked #1 on
Video Reconstruction
on UVG
SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling
Downsampling is widely adopted to achieve a good trade-off between accuracy and latency for visual recognition.
