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Found 21 papers, 2 papers with code
Bridging the gap between image coding for machines and humans
Image coding for machines (ICM) aims at reducing the bitrate required to represent an image while minimizing the drop in machine vision analysis accuracy.
NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines
To the best of our knowledge, this is the first research paper showing a hybrid video codec that outperforms VVC on multiple datasets and multiple machine vision tasks.
Leveraging progressive model and overfitting for efficient learned image compression
Nevertheless, the proposed LIC systems are still inferior to the state-of-the-art traditional techniques, for example, the Versatile Video Coding (VVC/H. 266) standard, due to either their compression performance or decoding complexity.
Adaptation and Attention for Neural Video Coding
In this work, we propose an end-to-end learned video codec that introduces several architectural novelties as well as training novelties, revolving around the concepts of adaptation and attention.
Lossless Image Compression Using a Multi-Scale Progressive Statistical Model
Recently, multi-scale autoregressive models have been proposed to address this limitation.
Image coding for machines: an end-to-end learned approach
Over recent years, deep learning-based computer vision systems have been applied to images at an ever-increasing pace, oftentimes representing the only type of consumption for those images.
Learned Image Coding for Machines: A Content-Adaptive Approach
One possible solution approach consists of adapting current human-targeted image and video coding standards to the use case of machine consumption.
Learning to Learn to Compress
In a second phase, the Model-Agnostic Meta-learning approach is adapted to the specific case of image compression, where the inner-loop performs latent tensor overfitting, and the outer loop updates both encoder and decoder neural networks based on the overfitting performance.
Efficient Adaptation of Neural Network Filter for Video Compression
We present an efficient finetuning methodology for neural-network filters which are applied as a postprocessing artifact-removal step in video coding pipelines.
End-to-End Learning for Video Frame Compression with Self-Attention
One of the core components of conventional (i. e., non-learned) video codecs consists of predicting a frame from a previously-decoded frame, by leveraging temporal correlations.
A Compression Objective and a Cycle Loss for Neural Image Compression
In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss.
Compressing Weight-updates for Image Artifacts Removal Neural Networks
In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible.
Compressibility Loss for Neural Network Weights
In this paper we apply a compressibility loss that enables learning highly compressible neural network weights.
Block-optimized Variable Bit Rate Neural Image Compression
In this work, we propose an end-to-end block-based auto-encoder system for image compression.
Object Detection in Equirectangular Panorama
We introduced a high-resolution equirectangular panorama (360-degree, virtual reality) dataset for object detection and propose a multi-projection variant of YOLO detector.
Depth Masked Discriminative Correlation Filter
Depth information provides a strong cue for occlusion detection and handling, but has been largely omitted in generic object tracking until recently due to lack of suitable benchmark datasets and applications.
Saliency-Enhanced Robust Visual Tracking
In this work, we propose an improvement over DCF based trackers by combining saliency based and other features based filter responses.
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations
We show the effect of l2 normalization on anomaly detection accuracy.
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure
In order to have an in-depth theoretical understanding, in this manuscript, we investigate the graph degree in spectral graph clustering based and kernel based point of views and draw connections to a recent kernel method for the two sample problem.
Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels
By using these encoded images, we train a memory-efficient network using only 0. 048\% of the number of parameters that other deep salient object detection networks have.
Video Ladder Networks
Thanks to the recurrent connections, the decoder can exploit temporal summaries generated from all layers of the encoder.
