Search Results for author:
Found 8 papers, 3 papers with code
Deep Perceptual Preprocessing for Video Coding
We introduce the concept of rate-aware deep perceptual preprocessing (DPP) for video encoding.
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing
The core of our framework comprises a spatial feature learning module, which utilizes residual-graph convolutional neural networks (RG-CNN), for end-to-end learning of appearance-based features directly from graphs.
Graph-Based Object Classification for Neuromorphic Vision Sensing
Neuromorphic vision sensing (NVS)\ devices represent visual information as sequences of asynchronous discrete events (a. k. a., ``spikes'') in response to changes in scene reflectance.
Deep Video Precoding
We propose to use deep neural networks as precoders for current and future video codecs and adaptive video streaming systems.
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs).
Improved Techniques for Adversarial Discriminative Domain Adaptation
Adversarial discriminative domain adaptation (ADDA) is an efficient framework for unsupervised domain adaptation in image classification, where the source and target domains are assumed to have the same classes, but no labels are available for the target domain.
Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor
We demonstrate that selective access to MB motion vector (MV) information within compressed video bitstreams can also provide for selective, motion-adaptive, MB pixel decoding (a. k. a., MB texture decoding).
Voronoi-based compact image descriptors: Efficient Region-of-Interest retrieval with VLAD and deep-learning-based descriptors
Our proposal is a compact image descriptor that combines the state-of-the-art in content-based descriptor extraction with a multi-level, Voronoi-based spatial partitioning of each dataset image.
