Papers Feature Compression
“Feature Compression” 태그가 달린 논문 96편 · 필터 해제
Feature Compression for Cloud-Edge Multimodal 3D Object Detection
Machine vision systems, which can efficiently manage extensive visual perception tasks, are becoming increasingly popular in industrial production and daily life. Due to the challenge of simultaneously obtaining accurate…
3D Object Detection3D Object ReconstructionFeature Compressionobject-detection+2Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving
With the increasing complexity of the traffic environment, the significance of safety perception in intelligent driving is intensifying. Traditional methods in the field of intelligent driving perception rely on deep lea…
3D Object DetectionFeature Compressionobject-detectionObject DetectionTowards unlocking the mystery of adversarial fragility of neural networks
In this paper, we study the adversarial robustness of deep neural networks for classification tasks. We look at the smallest magnitude of possible additive perturbations that can change the output of a classification alg…
Adversarial RobustnessClassificationFeature CompressionTexture-guided Coding for Deep Features
With the rapid development of machine vision technology in recent years, many researchers have begun to focus on feature compression that is better suited for machine vision tasks. The target of feature compression is de…
Feature Compressionfeature selectionImage ReconstructionJointRF: End-to-End Joint Optimization for Dynamic Neural Radiance Field Representation and Compression
Neural Radiance Field (NeRF) excels in photo-realistically static scenes, inspiring numerous efforts to facilitate volumetric videos. However, rendering dynamic and long-sequence radiance fields remains challenging due t…
Feature CompressionNeRFI$^2$VC: A Unified Framework for Intra- & Inter-frame Video Compression
Video compression aims to reconstruct seamless frames by encoding the motion and residual information from existing frames. Previous neural video compression methods necessitate distinct codecs for three types of frames …
DenoisingFeature CompressionMotion CompensationMotion Estimation+1Compressive Feature Selection for Remote Visual Multi-Task Inference
Deep models produce a number of features in each internal layer. A key problem in applications such as feature compression for remote inference is determining how important each feature is for the task(s) performed by th…
Feature Compressionfeature selectionDMOFC: Discrimination Metric-Optimized Feature Compression
Feature compression, as an important branch of video coding for machines (VCM), has attracted significant attention and exploration. However, the existing methods mainly focus on intra-feature similarity, such as the Mea…
Feature CompressionVector Quantization for Recommender Systems: A Review and Outlook
Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decades and remains widely utilized today. Wi…
Feature CompressionQuantizationRecommendation SystemsSurveyDistributed and Rate-Adaptive Feature Compression
We study the problem of distributed and rate-adaptive feature compression for linear regression. A set of distributed sensors collect disjoint features of regressor data. A fusion center is assumed to contain a pretraine…
Feature CompressionregressionNeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-Efficient Scene Representation
The emergence of Neural Radiance Fields (NeRF) has greatly impacted 3D scene modeling and novel-view synthesis. As a kind of visual media for 3D scene representation, compression with high rate-distortion performance is …
DecoderFeature CompressionNeRFNovel View Synthesis+1Flexible Variable-Rate Image Feature Compression for Edge-Cloud Systems
Feature compression is a promising direction for coding for machines. Existing methods have made substantial progress, but they require designing and training separate neural network models to meet different specificatio…
Feature CompressionFOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
Nanosatellite constellations equipped with sensors capturing large geographic regions provide unprecedented opportunities for Earth observation. As constellation sizes increase, network contention poses a downlink bottle…
Earth ObservationEdge-computingFeature CompressionImage Compression+2The Geometric Structure of Topic Models
Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research…
Feature CompressionTopic ModelsEMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection
In autonomous driving, cooperative perception makes use of multi-view cameras from both vehicles and infrastructure, providing a global vantage point with rich semantic context of road conditions beyond a single vehicle …
3D Object DetectionAutonomous DrivingFeature Compressionobject-detection+1Effective Communication with Dynamic Feature Compression
The remote wireless control of industrial systems is one of the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that need to be shared over the wireless medium may ov…
Deep Reinforcement LearningFeature CompressionQuantizationLearning to Manipulate Artistic Images
Recent advancement in computer vision has significantly lowered the barriers to artistic creation. Exemplar-based image translation methods have attracted much attention due to flexibility and controllability. However, t…
Computational EfficiencyFeature CompressionImage GenerationImage ManipulationDynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and computation resource availability, we propose…
Decision MakingDecoderEdge-computingFeature Compression+1Vertical Federated Image Segmentation
With the popularization of AI solutions for image based problems, there has been a growing concern for both data privacy and acquisition. In a large number of cases, information is located on separate data silos and it c…
Feature CompressionFederated LearningImage SegmentationSegmentation+2WidthFormer: Toward Efficient Transformer-based BEV View Transformation
We present WidthFormer, a novel transformer-based module to compute Bird's-Eye-View (BEV) representations from multi-view cameras for real-time autonomous-driving applications. WidthFormer is computationally efficient, r…
3D Object DetectionAutonomous DrivingDecoderEdge-computing+4