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Found 6 papers, 3 papers with code
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations
We propose a novel mixture-of-experts class to optimize computer vision models in accordance with data transfer limitations at test time.
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.
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).
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).
Vectors of Locally Aggregated Centers for Compact Video Representation
We create vectors of locally aggregated centers (VLAC) by first clustering SIFT features to obtain local feature centers (LFCs) and then encoding the latter with respect to given centers of local feature centers (CLFCs), extracted from a training set.
