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평가표

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보존된 평가 기록

1

코드

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

https://github.com/selinakhan/CIGAr

공식 코드

Context-Infused Visual Grounding for Art

https://github.com/semanto-mondal/A-RAG-Augmented-YOLOv8-Framework

공식 코드

Vision Meets Language: A RAG-Augmented YOLOv8 Framework for Coffee Disease Diagnosis and Farmer Assistance

https://github.com/sense-x/agvm

공식 코드

Large-batch Optimization for Dense Visual Predictions

https://github.com/sense-x/geomim

공식 코드

GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding

https://github.com/sense-x/hop

공식 코드

Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction

https://github.com/sense-x/mixmim

공식 코드

MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers

https://github.com/sense-x/unihead

공식 코드

Unifying Visual Perception by Dispersible Points Learning

https://github.com/senwang98/monoskd

공식 코드

MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficient

https://github.com/seonwhee-genome/rvernet

공식 코드

Knowledge distillation to effectively attain both region-of-interest and global semantics from an image where multiple objects appear

https://github.com/seqam-lab/alwod

공식 코드

ALWOD: Active Learning for Weakly-Supervised Object Detection

https://github.com/sercharles/cn-rma

공식 코드

CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoors Object Detection from Multi-view Images

https://github.com/serenos/di-v2x

공식 코드

DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection

https://github.com/seungho-snu/SROOE

공식 코드

Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation

https://github.com/seungho-snu/fxsr

공식 코드

Flexible Style Image Super-Resolution using Conditional Objective

https://github.com/seungjun45/Water-Filling

공식 코드

Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal

https://github.com/seungwonoh5/Adversarial_Attacks_against_Object_Detection

공식 코드

Resilience of Autonomous Vehicle Object Category Detection to Universal Adversarial Perturbations

https://github.com/sfm-sr-denoising/sfm

공식 코드

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

https://github.com/sfzhang15/ATSS

공식 코드

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

https://github.com/sfzhang15/RefineDet

공식 코드

Single-Shot Refinement Neural Network for Object Detection

https://github.com/shachoi/RobustNet

공식 코드

RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening

https://github.com/shadyabh/Correction-Filter

공식 코드

Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers

https://github.com/shadyabh/IAGAN

공식 코드

Image-Adaptive GAN based Reconstruction

https://github.com/shahsohil/sunets

공식 코드

Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation

https://github.com/shallowdream204/SODA-SR

공식 코드

Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer

https://github.com/shamanDevel/IsosurfaceSuperresolution

공식 코드

Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution

https://github.com/shangqigao/gsq-image-SR

공식 코드

Multi-scale deep neural networks for real image super-resolution

https://github.com/shangtse/robust-physical-attack

공식 코드

ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

https://github.com/shangwei5/amsr

공식 코드

High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-Resolution

https://github.com/shank2358/mudet

공식 코드

Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events

https://github.com/shanshuo/diagnosevrd

공식 코드

Diagnosing Errors in Video Relation Detectors

https://github.com/shaohua0116/MultiDigitMNIST

공식 코드

GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture

https://github.com/shaohuadong2021/dplnet

공식 코드

Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning

https://github.com/shaoqb/multi_scale_booster

공식 코드

Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster

https://github.com/shaoqing26/fusionpainting

공식 코드

FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection

https://github.com/sharat29ag/CDAL

공식 코드

Contextual Diversity for Active Learning

https://github.com/sharif-apu/darkdeblur

공식 코드

DarkDeblur: Learning single-shot image deblurring in low-light condition

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