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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 1–25 of 96 papers

TitleStatusHype
Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image SynthesisCode3
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode2
WidthFormer: Toward Efficient Transformer-based BEV View TransformationCode2
LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal UnderstandingCode2
Video in 10 Bits: Few-Bit VideoQA for Efficiency and PrivacyCode2
Compressing Features for Learning with Noisy LabelsCode1
Vector Quantization for Recommender Systems: A Review and OutlookCode1
Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge LearningCode1
Supervised Compression for Resource-Constrained Edge Computing SystemsCode1
VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object DetectionCode1
I^2VC: A Unified Framework for Intra- & Inter-frame Video CompressionCode1
FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature CompressionCode1
FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge ComputingCode1
Learning to Manipulate Artistic ImagesCode1
Lossy Compression for Lossless PredictionCode1
Compressive Feature Selection for Remote Visual Multi-Task Inference—0
Categorical Feature Compression via Submodular Optimization—0
Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation—0
Comparing of Term Clustering Frameworks for Modular Ontology Learning—0
An Efficient Large Recommendation Model: Towards a Resource-Optimal Scaling Law—0
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks—0
Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information—0
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning—0
Distilling Channels for Efficient Deep Tracking—0
Joint Device-Edge Inference over Wireless Links with Pruning—0
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