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2D Classification

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AutoCompSysLab/DCTNet

공식 코드

A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View

https://github.com/AutoLab-SAI-SJTU/MambaFusion

공식 코드

MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection

https://github.com/AutoML-4Paradigm/AutoSTR

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AutoSTR: Efficient Backbone Search for Scene Text Recognition

https://github.com/AutoVision-cloud/Deformable-PV-RCNN

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Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations

https://github.com/AutoVision-cloud/SA-Det3D

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SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection

https://github.com/AyanKumarBhunia/Deep-One-Shot-Logo-Retrieval

공식 코드

A Deep One-Shot Network for Query-based Logo Retrieval

https://github.com/Azure/mmlspark

공식 코드

MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales

https://github.com/BGUCompSci/WaveletCompressedConvolution

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Wavelet Feature Maps Compression for Image-to-Image CNNs

https://github.com/BICLab/EMS-YOLO

공식 코드

Deep Directly-Trained Spiking Neural Networks for Object Detection

https://github.com/BIT-DA/I2V-GAN

공식 코드

I2V-GAN: Unpaired Infrared-to-Visible Video Translation

https://github.com/BOBrown/JADF-caffe

공식 코드

Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection

https://github.com/BR-IDL/PaddleViT

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Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention

https://github.com/Babelscape/FENICE

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FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction

https://github.com/Banconxuan/RTS3D

공식 코드

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

https://github.com/BangLiu/ArticlePairMatching

공식 코드

Matching Article Pairs with Graphical Decomposition and Convolutions

https://github.com/BangguWu/ECANet

공식 코드

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

https://github.com/BarCodeReader/RCSB-PyTorch

공식 코드

Recursive Contour Saliency Blending Network for Accurate Salient Object Detection

https://github.com/BarCodeReader/SelfReformer

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SelfReformer: Self-Refined Network with Transformer for Salient Object Detection

https://github.com/Barabasi-Lab/quantifying_biobanks

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Quantifying the Impact of Biobanks and Cohort Studies

https://github.com/Bartzi/loans

공식 코드

LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks

https://github.com/BathVisArtData/PeopleArt

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Detecting People in Artwork with CNNs

https://github.com/BayesWatch/pytorch-prunes

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A Closer Look at Structured Pruning for Neural Network Compression

https://github.com/Benzlxs/tDBN

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Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes

https://github.com/BiSinger-SVS/BiSinger

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BiSinger: Bilingual Singing Voice Synthesis

https://github.com/BichenWuUCB/squeezeDet

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SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/BillChan226/AgentPoison

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AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases

https://github.com/Bissmella/Small-object-detection-transformers

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Multimodal Transformer Using Cross-Channel attention for Object Detection in Remote Sensing Images

https://github.com/BoPang1996/TubeTK

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TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model

https://github.com/Bob-cheng/MDE_Attack

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Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches

https://github.com/BobXWu/AKEW

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AKEW: Assessing Knowledge Editing in the Wild

https://github.com/Boese0601/2021-National-Underwater-Robotics-Vision-Optics

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CDNet is all you need: Cascade DCN based underwater object detection RCNN

https://github.com/BogiHsu/Universal-Adaptor

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Universal Adaptor: Converting Mel-Spectrograms Between Different Configurations for Speech Synthesis

https://github.com/Bohao-Lee/CME

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Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

https://github.com/BorealisAI/Autocast-plus-plus

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AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval

https://github.com/BorealisAI/CP-VAE

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On Variational Learning of Controllable Representations for Text without Supervision

https://github.com/BorealisAI/cross_domain_coherence

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A Cross-Domain Transferable Neural Coherence Model

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.