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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 26–50 of 96 papers

TitleStatusHype
An Efficient Large Recommendation Model: Towards a Resource-Optimal Scaling Law—0
MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models—0
Generative Map Priors for Collaborative BEV Semantic Segmentation—0
Privacy-Aware Multi-Device Cooperative Edge Inference with Distributed Resource Bidding—0
Structurally Consistent MRI Colorization using Cross-modal Fusion Learning—0
SpikeBottleNet: Spike-Driven Feature Compression Architecture for Edge-Cloud Co-Inference—0
Toward Scalable Image Feature Compression: A Content-Adaptive and Diffusion-Based Approach—0
Distilling Channels for Efficient Deep Tracking—0
Feature Compression for Cloud-Edge Multimodal 3D Object Detection—0
Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent DrivingCode0
Towards unlocking the mystery of adversarial fragility of neural networks—0
Texture-guided Coding for Deep Features—0
JointRF: End-to-End Joint Optimization for Dynamic Neural Radiance Field Representation and Compression—0
Compressive Feature Selection for Remote Visual Multi-Task Inference—0
DMOFC: Discrimination Metric-Optimized Feature Compression—0
Distributed and Rate-Adaptive Feature Compression—0
NeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-Efficient Scene Representation—0
Flexible Variable-Rate Image Feature Compression for Edge-Cloud SystemsCode0
The Geometric Structure of Topic Models—0
Effective Communication with Dynamic Feature CompressionCode0
Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder—0
Vertical Federated Image Segmentation—0
Understanding Deep Representation Learning via Layerwise Feature Compression and DiscriminationCode0
End-to-end Feature Selection Approach for Learning Skinny TreesCode0
The Road to On-board Change Detection: A Lightweight Patch-Level Change Detection Network via Exploring the Potential of Pruning and Pooling—0
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