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Feature Compression

Compress data for machine interpretability to perform downstream tasks, rather than for human perception.

Papers

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Showing 51–75 of 96 papers

TitleStatusHype
DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality—0
In Defense of Clip-based Video Relation Detection—0
End-to-End Learnable Multi-Scale Feature Compression for VCM—0
Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization—0
SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network AcceleratorsCode0
Semantic and Effective Communication for Remote Control Tasks with Dynamic Feature Compression—0
Attention-based Feature Compression for CNN Inference Offloading in Edge Computing—0
Supervised Feature Compression based on Counterfactual AnalysisCode0
Efficient Feature Compression for Edge-Cloud SystemsCode0
Cross Modal Compression: Towards Human-comprehensible Semantic Compression—0
Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation—0
Feature Compression for Rate Constrained Object Detection on the Edge—0
SC2 Benchmark: Supervised Compression for Split ComputingCode0
Context-Aware Compilation of DNN Training Pipelines across Edge and CloudCode0
Source Feature Compression for Object Classification in Vision-Based Underwater Robotics—0
Reconstructing Pruned Filters using Cheap Spatial Transformations—0
Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics—0
Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression—0
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
Communication-Computation Efficient Device-Edge Co-Inference via AutoML—0
Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information—0
Fusion: Towards Automated ICD Coding via Feature Compression—0
Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search—0
Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic—0
Revisit Visual Representation in Analytics Taxonomy: A Compression Perspective—0
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