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Found 23 papers, 1 papers with code
Privacy-Preserving Autoencoder for Collaborative Object Detection
Privacy is a crucial concern in collaborative machine vision where a part of a Deep Neural network (DNN) model runs on the edge, and the rest is executed on the cloud.
Rate-Distortion Theory in Coding for Machines and its Application
To meet this growing demand, several methods have been developed for image and video coding for machines.
VVC+M: Plug and Play Scalable Image Coding for Humans and Machines
We focus on the case of images, proposing to utilize the pre-existing residual coding capabilities of video codecs such as VVC to create a scalable codec from any image compression for machines (ICM) scheme.
No-Box Attacks on 3D Point Cloud Classification
To this end, we define 14 point cloud features and use multiple linear regression to examine whether these features can be used for adversarial point prediction, and which combination of features is best suited for this purpose.
Rate-Distortion in Image Coding for Machines
In our experiments we show the trade-off between the human and machine sides of such a scalable model, and discuss the benefit of using deeper layers for training in that regard.
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment
Experimental results show that our signed graph sampling method outperformed existing fast sampling schemes noticeably on various datasets.
Collaborative Inference for AI-Empowered IoT Devices
Artificial intelligence (AI) technologies, and particularly deep learning systems, are traditionally the domain of large-scale cloud servers, which have access to high computational and energy resources.
Scalable Image Coding for Humans and Machines
The simplest task is assigned to a subset of the latent space (the base layer), while more complicated tasks make use of additional subsets of the latent space, i. e., both the base and enhancement layer(s).
Latent-space scalability for multi-task collaborative intelligence
We investigate latent-space scalability for multi-task collaborative intelligence, where one of the tasks is object detection and the other is input reconstruction.
Exploring Bayesian Surprise to Prevent Overfitting and to Predict Model Performance in Non-Intrusive Load Monitoring
We compare the performance of several benchmark NILM algorithms supported by NILMTK, in order to establish a useful threshold on the two combined measures of surprise.
PowerGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks
Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter.
Generative Adversarial Network
Non-Intrusive Load Monitoring
Back-and-Forth prediction for deep tensor compression
Recent AI applications such as Collaborative Intelligence with neural networks involve transferring deep feature tensors between various computing devices.
Towards Automated Swimming Analytics Using Deep Neural Networks
Methods for creating a system to automate the collection of swimming analytics on a pool-wide scale are considered in this paper.
Datasets for Face and Object Detection in Fisheye Images
We present two new fisheye image datasets for training face and object detection models: VOC-360 and Wider-360.
FDDB-360: Face Detection in 360-degree Fisheye Images
360-degree cameras offer the possibility to cover a large area, for example an entire room, without using multiple distributed vision sensors.
Deep Frame Prediction for Video Coding
We propose a novel frame prediction method using a deep neural network (DNN), with the goal of improving video coding efficiency.
Near-Lossless Deep Feature Compression for Collaborative Intelligence
However, this necessitates sending deep feature data from the mobile to the cloud in order to perform inference.
Deep feature compression for collaborative object detection
Recent studies have shown that the efficiency of deep neural networks in mobile applications can be significantly improved by distributing the computational workload between the mobile device and the cloud.
High efficiency compression for object detection
In this paper we present a bit allocation and rate control strategy that is tailored to object detection.
Can you find a face in a HEVC bitstream?
Finding faces in images is one of the most important tasks in computer vision, with applications in biometrics, surveillance, human-computer interaction, and other areas.
Can you tell a face from a HEVC bitstream?
We focus on one of the poster problems of visual analytics -- face detection -- and approach the issue of reducing the computation by asking: Is it possible to detect a face without full image reconstruction from the High Efficiency Video Coding (HEVC) bitstream?
Load Disaggregation Based on Aided Linear Integer Programming
Load disaggregation based on aided linear integer programming (ALIP) is proposed.
How Many Bits Does it Take for a Stimulus to Be Salient?
Visual saliency has been shown to depend on the unpredictability of the visual stimulus given its surround.
