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Found 18 papers, 1 papers with code
Variable-Rate Learned Image Compression with Multi-Objective Optimization and Quantization-Reconstruction Offsets
Third, variable rate quantization is used also for the hyper latent.
Learned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines
As an increasing amount of image and video content will be analyzed by machines, there is demand for a new codec paradigm that is capable of compressing visual input primarily for the purpose of computer vision inference, while secondarily supporting input reconstruction.
Frequency-aware Learned Image Compression for Quality Scalability
Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches.
Joint Image Compression and Denoising via Latent-Space Scalability
In this paper, we present a learning-based image compression framework where image denoising and compression are performed jointly.
SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences
We present a dataset that contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences.
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.
Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence
In collaborative intelligence applications, part of a deep neural network (DNN) is deployed on a lightweight device such as a mobile phone or edge device, and the remaining portion of the DNN is processed where more computing resources are available, such as in the cloud.
Lightweight compression of neural network feature tensors for collaborative intelligence
In collaborative intelligence applications, part of a deep neural network (DNN) is deployed on a relatively low-complexity device such as a mobile phone or edge device, and the remainder of the DNN is processed where more computing resources are available, such as in the cloud.
GAN-based Anomaly Detection in Imbalance Problems
Imbalance problems in object detection are one of the key issues that affect the performance greatly.
Ranked #1 on
Anomaly Detection
on MNIST
(using extra training data)
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.
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.
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.
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 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?
