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평가표

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보존된 평가 기록

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코드

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

https://github.com/csuhan/s2anet

공식 코드

Align Deep Features for Oriented Object Detection

https://github.com/csuhan/vfa

공식 코드

Few-Shot Object Detection via Variational Feature Aggregation

https://github.com/csuvis/BarchartReverseEngineering

공식 코드

Reverse-engineering Bar Charts Using Neural Networks

https://github.com/cswin/RC-Nets

공식 코드

Image Restoration Using Deep Regulated Convolutional Networks

https://github.com/cswry/osediff

공식 코드

One-Step Effective Diffusion Network for Real-World Image Super-Resolution

https://github.com/cswry/seesr

공식 코드

SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution

https://github.com/csxmli2016/GFRNet

공식 코드

Learning Warped Guidance for Blind Face Restoration

https://github.com/csxmli2016/marconet

공식 코드

Learning Generative Structure Prior for Blind Text Image Super-resolution

https://github.com/csxmli2016/redegnet

공식 코드

From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution

https://github.com/csysi/fsel

공식 코드

Frequency-Spatial Entanglement Learning for Camouflaged Object Detection

https://github.com/csysi/glconet

공식 코드

GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detection

https://github.com/csysi/udcnet

공식 코드

United Domain Cognition Network for Salient Object Detection in Optical Remote Sensing Images

https://github.com/cszn/BSRGAN

공식 코드

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

https://github.com/cszn/DPSR

공식 코드

Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels

https://github.com/cszn/DnCNN

공식 코드

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

https://github.com/cszn/KAIR

공식 코드

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

https://github.com/cszn/SRMD

공식 코드

Learning a Single Convolutional Super-Resolution Network for Multiple Degradations

https://github.com/cszn/USRNet

공식 코드

Deep Unfolding Network for Image Super-Resolution

https://github.com/cszn/ircnn

공식 코드

Learning Deep CNN Denoiser Prior for Image Restoration

https://github.com/cszzshi/SimD

공식 코드

Similarity Distance-Based Label Assignment for Tiny Object Detection

https://github.com/ctu-vras/pcl-augmentation

공식 코드

Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and Segmentation

https://github.com/ctu-vras/t-concord3d

공식 코드

Teachers in concordance for pseudo-labeling of 3D sequential data

https://github.com/ctuning/cbench

공식 코드

The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps

https://github.com/cuhk-aim-group/star-rl

공식 코드

Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution

https://github.com/cuibaby/dpk

공식 코드

Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation

https://github.com/cuiziteng/eccv_aeris

공식 코드

Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection

https://github.com/cuiziteng/iccv_maet

공식 코드

Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection

https://github.com/cuiziteng/illumination-adaptive-transformer

공식 코드

You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction

https://github.com/cuiziteng/maet

공식 코드

Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection

https://github.com/cuogeihong/ceasc

공식 코드

Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images

https://github.com/cuong-pv/FAM-KD

공식 코드

Frequency Attention for Knowledge Distillation

https://github.com/cuplv/text-to-sql-wizardcoder

공식 코드

Analyzing the Effectiveness of Large Language Models on Text-to-SQL Synthesis

https://github.com/cuppp1998/360_object_tracking

공식 코드

Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis

https://github.com/custommetrics-sst/sst_customevaluationmetrics

공식 코드

SST-EM: Advanced Metrics for Evaluating Semantic, Spatial and Temporal Aspects in Video Editing

https://github.com/cv-core/MIT-Driverless-CV-TrainingInfra

공식 코드

Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical Solutions

https://github.com/cv-junchengli/sisr-survey

공식 코드

A Systematic Survey of Deep Learning-based Single-Image Super-Resolution

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